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REVIEW article

Front. Mol. Biosci., 10 January 2018
Sec. Molecular Recognition
This article is part of the Research Topic Roles of Water and Polarization in Molecular Recognition View all 6 articles

Recent Developments and Applications of the MMPBSA Method

\r\nChanghao Wang,,Changhao Wang1,2,3D&#x;Artagnan GreeneD'Artagnan Greene2Li Xiao,Li Xiao2,4Ruxi QiRuxi Qi2Ray Luo,,,*Ray Luo1,2,4,5*
  • 1Chemical and Materials Physics Graduate Program, University of California, Irvine, Irvine, CA, United States
  • 2Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
  • 3Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, United States
  • 4Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
  • 5Department of Chemical Engineering and Materials Science, University of California, Irvine, Irvine, CA, United States

The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

Introduction

It is widely accepted that high-level quantum mechanical (QM) methods provide the most detailed and accurate description of molecular structures, dynamics, and functions. However, for many biochemical systems that are often too complex, and/or biochemical processes that are too long, classical approaches are more commonly employed due to their efficiency and reasonable accuracy. To model biochemical systems classically, both long-range polar and short-range non-polar interactions are important for accurate and transferrable models (Perutz, 1978; Davis and McCammon, 1990; Honig and Nicholls, 1995). Among the classical simulation methods, the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach (Srinivasan et al., 1998; Kollman et al., 2000; Gohlke and Case, 2004; Yang et al., 2011; Miller et al., 2012; Wang C. H. et al., 2016) has emerged as an efficient and reliable method to model molecular recognition, such as for protein-ligand binding interactions.

The development of binding free energy calculation methods has been a central focus in molecular simulations. Indeed, theoretically rigorous, but computationally expensive, free energy perturbation, and thermodynamic integration methods were both proposed much earlier than the MMPBSA method (Zwanzig, 1954; Bennett, 1976; Straatsma and McCammon, 1991). For simple and small systems where they can be reliably applied, these “exact” methods have been shown to be more accurate than the MMPBSA method. However, their applications to typically large and complex biomolecular recognition problems are quite limited. This is due to low efficiency and slow convergence. There are several key approximations utilized in the MMPBSA method that allow it to be used as an efficient and reasonable approximation for free energy simulations. The PBSA model is used so that the solvation contribution to the free energy is approximated by using a continuum solvent model. In addition, the method separately approximates enthalpy and entropy contributions to the free energy, with varying degrees of success as discussed below. There are other approximate methods that are similar to MMPBSA, such as the Mining Minima (M2) method and the Linear Interaction Energy (LIE) method. These are all categorized as end-point methods that focus on the end states of processes to compute the free energy changes (Head et al., 1997; Luo et al., 1999, 2001; Luo and Gilson, 2000; Aqvist and Marelius, 2001; Chen et al., 2004; Chang et al., 2007; Moghaddam et al., 2011; Mikulskis et al., 2012; Muddana and Gilson, 2012; Muddana et al., 2014).

In this review, we focus on recent developments and applications of the MMPBSA method that have been reported since 2014, which was roughly the time when the last major MMPBSA review was published (Genheden and Ryde, 2015). In the following, we first review the improvements made to the MMPBSA method over the last few years. This is followed by recent applications of the method in various fields, with a focus on biomedical applications.

Improvements of MMPBSA

Overview of MMPBSA

The MMPBSA method is most often applied to the calculation of binding free energies (ΔGbind) of small molecule ligands bound to large biomolecule receptors, although large inter-biomolecular recognitions are also often reported as reviewed below. The binding free energy of the bound ligand-receptor complex in an aqueous solvent (ΔGbind, aq) can be approximated as (Srinivasan et al., 1998; Kollman et al., 2000):

    ΔGbind,aq=ΔHTΔSΔEMM+ΔGbind,solvTΔS,    (1)
            ΔEMM=ΔEcovalent+ΔEelectrostatic+ΔEvdW,    (2)
  ΔEcovalent=ΔEbond+ΔEangle+ΔEtorsion,    (3)
ΔGbind,solv=ΔGpolar+ΔGnon-polar,    (4)

where ΔEMM, ΔGbind, solv, and − TΔS represent the gas-phase molecular mechanical energy change, the solvation free energy change, and the conformational entropy change upon binding, respectively. All of these changes are computed via ensemble averaging over a large set of sampled conformations. ΔEMM includes three terms calculated using molecular mechanics (MM): the covalent energy change (ΔEcovalent), the electrostatic energy change (ΔEelectrostatic), and the van der Waals energy change (ΔEvdW). ΔEcovalent consists of changes in the bond terms (ΔEbond), the angle terms (ΔEangle), and the torsion terms (ΔEtorsion). The solvation free energy change (ΔGbind, solv) is usually separated into polar and non-polar contributions (ΔGpolar and ΔGnon-polar). The entropy term is the most difficult to compute, and it is often approximated with a normal mode method using a few selected snapshots.

To compute all the ensemble averages in Equations (1–4) for a binding affinity calculation, the MMPBSA method usually begins with a molecular dynamics (MD) simulation of the complex using the single-trajectory approach, or three separate MD simulations of the complex, receptor, and ligand, respectively, in the multi-trajectory approach. A snapshot of a structure is taken at various time points during the production portion of the MD simulations. These snapshots are then used to calculate average values and uncertainties of various quantities of interest. The MD simulations are almost always conducted in an explicit solvent model to obtain the most accurate snapshots possible before carrying out any calculations that make use of them. It is important to obtain many different conformations, or snapshots, over a suitable timeframe for use in later statistical analysis as it is often not trivial to observe converged averaging, even in the relatively easier single-trajectory approach (Wang C. H. et al., 2016).

The Polar Solvation Term

For the calculation of the solvation free energy change (ΔGbind, solv), the explicit solvent is removed and replaced with an implicit continuum solvent to greatly speed up the calculation time. The polar solvation term (ΔGpolar) in Equation (4) is calculated using a finite-difference solution or a Generalized Born (GB) pairwise approximation of the Poisson-Boltzmann equation (PBE) (Warwicker and Watson, 1982; Bashford and Karplus, 1990; Jeancharles et al., 1991; Gilson, 1995; Edinger et al., 1997; Luo et al., 1997, 2002; Lu and Luo, 2003; Tan et al., 2006; Cai et al., 2009, 2010, 2011; Wang et al., 2009, 2010, 2012, 2013; Ye et al., 2009, 2010; Wang and Luo, 2010; Hsieh and Luo, 2011; Botello-Smith et al., 2012; Liu et al., 2013; Wang C. H. et al., 2017). In addition to modeling binding interactions, PBE methods have also been applied to the prediction of pKa values for ionizable groups in biomolecules (Luo et al., 1998; Georgescu et al., 2002; Nielsen and McCammon, 2003; Warwicker, 2004; Tang et al., 2007), solvation free energies (Nicholls et al., 2008; Shivakumar et al., 2009), and protein folding and design (Hsieh and Luo, 2004; Wen and Luo, 2004; Wen et al., 2004; Lwin and Luo, 2005, 2006; Marshall et al., 2005; Lwin et al., 2006; Korman et al., 2008; Tan and Luo, 2008, 2009).

The PBE is based on the more fundamental Poisson equation:

·ε(r)ϕ(r)=4πρf(r),    (5)

where ε(r) is a predefined dielectric distribution function for the solvated molecular system, ϕ(r) is the potential distribution function, and ρf(r) is the fixed atomic charge density. To model the electrostatic interaction due to the additional presence of salt in the aqueous solution, the electrostatic potential ϕ(r) can be described by the PBE as:

·ε(r)ϕ(r)+λ(r)f(ϕ(r))=4πρf(r) ,    (6)

with

f(ϕ(r))=4πiNzieciexp (zieϕ(r)kBT).    (7)

Here, λ(r) is a predefined ion-exclusion function with a value of 0 within the Stern layer and the molecular interior and a value of 1 outside the Stern layer. The salt-related term f(ϕ(r)) is a function of the potential, ϕ(r), the valence, zi, of ion type i, and the bulk concentration, ci, at a given temperature T. When both the ionic strength and electric field are weak, the PBE can be linearized for easier numerical solutions:

·ε(r)ϕ(r)=4πρf(r)+ενκ2ϕ(r),    (8)

where κ2=8πe2IεvkBT. Here εν denotes the solvent dielectric constant, and I represents the ionic strength of the solution.

Over the past few years, a few new algorithm developments were reported for the numerical solution of the PBE (Xie, 2014; Fisicaro et al., 2016; Xie and Jiang, 2016). To deal with the singularity and nonlinearity of the PBE, Xie proposed a new decomposition and minimization scheme, together with a new proof on the existence and uniqueness of the PBE solution. A new PBE finite element solver was developed based on these solution decomposition and minimization techniques (Xie, 2014). Fisicaro et al. presented a preconditioned conjugate gradient technique to solve the generalized Poisson problem, and the linear regime of the PBE, in some 10 iterations. In combination with a self-consistent procedure, this technique was able to solve the non-linear Poisson–Boltzmann problem in a formulation including ionic steric effects (Fisicaro et al., 2016). Later Xie et al. incorporated nonlocal dielectric effects into the classic PBE for a protein in ionic solvent to derive a nonlocal modified Poisson–Boltzmann equation (NMPBE) and developed a finite element algorithm with a related package for solving the NMPBE (Xie and Jiang, 2016). Their results demonstrate the potential for the NMPBE to be a better predictor of electrostatic solvation and binding free energies compared to the standard PBE. It is worth noting that there has been a community wide push to explore alternative hardware for biomolecular simulations, such as the graphics processing units (GPU), which have a parallel architecture and are suited for high-performance computation with dense data parallelism (Colmenares et al., 2014a,b; Qi R. et al., 2017). A finite difference scheme with the successive over-relaxation method was implemented on the CUDA-based GPUs in the DelPhi package, which achieved a speedup of ~10 times in the linear and non-linear cases (Colmenares et al., 2014b). More recently, Qi et al. implemented and analyzed commonly used linear PBE solvers on CUDA GPUs for biomolecular simulations, including both standard and preconditioned conjugate gradient (CG) solvers with several alternative preconditioners (Qi R. et al., 2017). After extensive testing, the optimal GPU performance was observed using the Jacobi-preconditioned CG solver with a significant speedup that was up to 50 times faster than the standard CG solver on CPU. These progressive efforts on efficient numerical PBE solvers show great potential for accelerating MMPBSA computation.

Since the prior review (Genheden and Ryde, 2015), the numerical procedure and related factors for the widely used finite-difference method were also investigated for their impact on the MMPBSA method (Wang C. H. et al., 2016). This study showed that the impact of grid spacing on the quality of MMPBSA calculations is small in protein-ligand binding calculations; the agreement with experiment changed by a negligible amount when the grid spacing was changed from 0.50 to 0.25 Å. This indicated that the widely adopted default value of 0.50 Å used by the community was sufficient. The impact of different atomic radius sets and different molecular surface definitions was also analyzed, and weak influences were found on the agreement with experiment (Wang C. H. et al., 2016). This is probably due to the use of high protein dielectrics for the often-charged ligands and/or active sites as discussed below.

The effect of the solute dielectric constant was also investigated. A higher solute dielectric constant (using 2 or 4 instead of 1) was found to perform better in the virtual screening of ligands for tyrosine kinases (Sun et al., 2014a). Our own analysis of six groups of receptors reached a similar conclusion; the binding affinities using high dielectric constants (4 and 20) agreed better with experiment. The difference between calculations using dielectric constants of 4 and 20 was not very apparent except for the case of a highly charged binding pocket in one receptor (Wang C. H. et al., 2016). Aside from the study of higher solute dielectric constants, a residue-dependent dielectric model was also developed for use in an alanine scanning protocol with the MMPBSA method (Simoes et al., 2017). An attempt to modify the solute dielectric environment by incorporating structurally important, explicit water molecules in protein-ligand pockets for MMPBSA calculations was also reported, and it was found to improve the modeling of binding affinities for a series of JNK3 kinase inhibitors (Zhu Y. L. et al., 2014).

A hybrid QM/MM solute was also used in MMPBSA applications for predicting the binding affinities of FabI inhibitors (Su et al., 2015). The study suggested that the prediction results are sensitive to radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length. The finding here appears to contradict our prior discussion. This is because the solute dielectric constant is set to 1 (vaccum) due to the explicit consideration of polarization in QM/MM studies. In the study of (Wang C. H. et al., 2016), a high solute dielectric constant was used to mimic polarization implicitly. In general, the use of high dielectric constants washes out the sensitivity to radii and surface definitions.

The Non-polar Solvation Term

The non-polar (non-electrostatic) solvation free energy contribution (ΔGnon-polar) to the solvation free energy change (ΔGbind, solv) arises from the solute cavity formation within the solvent and van der Waals interactions between the solute and the solvent around the cavity (Weeks et al., 1971; Smith and Tanford, 1973; Pratt and Chandler, 1977, 1980; Widom, 1982; Kang et al., 1987; Floris and Tomasi, 1989; Floris et al., 1991; Ashbaugh et al., 1999; Hummer, 1999; Lum et al., 1999; Gallicchio et al., 2000, 2002; Levy et al., 2003; Zacharias, 2003; Gallicchio and Levy, 2004; Su and Gallicchio, 2004; Dzubiella et al., 2006; Wagoner and Baker, 2006; Tan et al., 2007). Until recent times, the non-polar solvation free energy has been simply estimated to be proportional to the solvent accessible surface area (SASA) of the solute:

ΔGnon - polar=γSASA + b.    (9)

This is referred to as the classical approach below. The surface tension γ and the correction term b are usually set to be constant for all solute molecules; for example, these are 0.00542 kcal/mol-Å2 and 0.92 kcal/mol, respectively, in the AMBER package (Case et al., 2016).

In more modern approaches (Tan et al., 2007), the cavity formation free energy and van der Waals (dispersion) free energy are modeled as separate terms because they scale differently vs. solute size. One way to correlate the cavity formation free energy is to use the volume (SAV) enclosed by the solvent accessible surface. A solvent accessible volume integration, or a solvent accessible surface integration, can be used to compute the dispersion term (ΔGdispersion), so the total non-polar solvation free energy can be estimated as:

ΔGnon - polar=ΔGdispersion+ΔGcavity                                     = ΔGdispersion+γSAV + b.    (10)

These scaling factors apparently depend on the choices of atomic and solvent probe radii, for example, they are set to 0.0378 kcal/mol-Å2 and −0.569 kcal/mol in the AMBER package (Case et al., 2016). It is interesting to note that the coefficient is much higher compared to the classical model since the dispersion term in solvation is always a negative term. The overall non-polar solvation free energies are similar between the classical and modern models, at least for small molecules where all atoms are exposed. The performance of both the classical and modern non-polar solvation models was analyzed, and it was found that the modern approach reduced the root-mean-square deviations of computed binding affinities (both relative and absolute) from experimental values while the correlations changed little from those computed using the classical approach (Wang C. H. et al., 2016).

The Entropy Term

The configurational entropy (S) in Equation (1) is often approximated by normal mode or quasi-harmonic analysis. Many proposed methods exist to calculate entropy (Kassem et al., 2015), but it is notoriously difficult to obtain a converged quantity. Thus, further approximations are often utilized; for example, usually only residues within a small sphere (radius of 8–12 Å) centered at the ligand and a limited number of snapshots (<100) are used for normal mode analysis. Furthermore, the single trajectory approach (i.e., that for the complex) is most often used in MMPBSA studies that do not consider any binding-induced structural changes. In such an approach, configurational entropy computed by the normal mode analysis is often omitted completely in the ranking of relative binding affinities as its inclusion often does not improve the agreement with experiment (Yang et al., 2011).

A few new ideas to approximate entropy were reported recently. For example, a new method termed BEERT (Binding Entropy Estimation of Rotation and Translation) was proposed to approximate configurational entropy changes in terms of the reduction in translational and rotational freedom of the ligand upon protein-ligand binding, starting from the flexible molecule approach (Ben-Shalornit et al., 2017). An interaction entropy (IE) method was also proposed to investigate the entropy change upon binding (Duan L. et al., 2016; Duan et al., 2017). The interaction entropy (IE) contribution to binding free energy was defined as

TΔS=kBTlneβEplint,    (11)

where ΔEplint is the fluctuation of the protein-ligand interaction energy for both electrostatic and van der Waals interactions. The ensemble average of eβΔEplint can be extracted from MD simulations, avoiding normal mode calculations. Both developers of BEERT and IE claim that these methods are highly efficient.

Extension to Membrane Proteins

The development of implicit membrane models based on existing continuum solvent approaches has advanced, making it possible to extend the MMPBSA method to biological membrane systems. The presence of an implicit membrane adds a complication to the numerical solution of the underlying PBE that is brought about by dielectric inhomogeneities that appear on the boundary surfaces of the computation grid. This issue can be alleviated by employing the periodic boundary condition, which is a common practice for electrostatic computations in MD simulations. The conjugate gradient and successive over-relaxation methods can be adjusted to take into account periodic calculations, but the convergence rate using either method is quite low. This limits their application to MMPBSA calculations which require that a large number of conformations be processed. To improve the convergence rate for use in biomolecular applications, the Incomplete Cholesky preconditioning method and the geometric multigrid method were both extended to incorporate periodicity (Botello-Smith and Luo, 2015). Applications to protein-ligand binding utilizing the newly developed membrane MMPBSA method were also reported (Greene et al., 2016; Xiao et al., 2017).

Extension to the High-Speed Screening of Ligands

The MMPBSA method has also been utilized as a rescoring method for docking applications. In protein-protein docking, it was found that MMPBSA rescoring was more capable at distinguishing correct complex structures from decoys than ZDOCK scoring in a test set of 46 protein-protein complexes for certain combinations of force fields, solvation models, and dielectric constants (Chen et al., 2016). A new binding affinity estimator, PBSA_E, that is based on MMPBSA inputs, was also optimized for protein-ligand binding. The optimization made use of a training set consisting of high-quality experimental data that was gathered from 145 complex structures. When the predicted binding affinities using PBSA_E were compared with the predicted binding affinities using other popular scoring functions such as GlideXP, GlideSP, and SYBYL_F, the PBSA_E method exhibited improved accuracy in terms of both achieving higher correlations with measured binding affinities and lower root-mean-square deviations (Liu X. et al., 2016). A study on MMPBSA in hierarchical virtual screening (HVS) was reported. This study demonstrated the predictability and validity of using the MMPBSA method for lead discovery as it identified novel inhibitors of the p38 MAP kinase by employing a physics-based scoring function combined with a knowledge-based structural filter (Cao et al., 2014).

A study on the performance of MMPBSA in docking applications found that it depends on the choice of the solvent models among many factors that were analyzed. Specifically, the authors found that the choice of solvent models plays a minor role for one-protein-family/one-ligand cases which represent the unbiased protein–ligand complex sampling. However, for the total dataset with biased sampling, where some proteins and their homologs have an overabundant presence in the dataset, they found that numerical PB solvent methods do not perform as well as GB solvent methods. In addition, they showed that numerical PB methods were more sensitive to whether MD simulations were used for averaging. Such methods may be currently more suitable for individual protein binding free energy rankings where MD simulations can be easily conducted. This study also demonstrated that the numerical noise from screening applications that utilize only one or a few structures should be addressed in future developments of the dielectric model for numerical PB methods (Sun et al., 2014b).

Extension to Other High-Performance Analyses

MMPBSA was also adapted for high-performance mutational analysis. Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict changes in binding free energy that are brought about by point mutations. SAAMBE utilized 3D structures of protein-protein complexes in a sequence- and structure-based approach. The method was centered around two components: a MMPBSA-based component, and a set of statistical terms obtained from the physico-chemical properties of protein complexes. A better agreement with experimentally determined binding free energy changes over a set of 1,300 mutations in 43 proteins indicated a significant improvement for predictions made using SAAMBE (Li M. H. et al., 2014; Petukh et al., 2015).

Additionally, MMPBSA was adapted into a novel structure-based multiscale approach to identify the key specificity determining residues (SDRs) of PDZ domains that appeared in explicit solvent MD simulations on PDZ-peptide complexes. SDRs were then used together with knowledge-based scoring functions in a proteome-wide search to locate their interaction partners (Tiwari and Mohanty, 2014).

New Toolkits

Over the past few years, several new toolkits were released to facilitate the use of MMPBSA calculations. A free energy workflow tool, FEW, was developed for AMBER to assist in the setup of molecular dynamics simulations in explicit membrane environments. FEW also assists in the setup and execution of effective binding free energy calculations for a single-trajectory implicit solvent/implicit membrane MMPBSA approach that involves multiple ligands binding to the same membrane protein (Homeyer and Gohlke, 2015). g_mmpbsa was developed for GROMACS, and it implemented the MMPBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages (Kumari et al., 2014). GMXPBSA is another user-friendly suite of Bash/Perl scripts for streamlining MMPBSA calculations for GROMACS users (Paissoni et al., 2014). An easy-to-use pipeline tool named Calculation of Free Energy (CaFE) was published to facilitate both MMPBSA and LIE calculations. CaFe is capable of handling numerous static structure and molecular dynamics trajectory file formats generated by different molecular simulation packages, and it also supports various force field parameters (Liu and Hou, 2016).

Applications of MMPBSA

Protein-Ligand Binding Interactions

Since the last comprehensive review of MMPBSA applications in 2015 (Genheden and Ryde, 2015), MMPBSA has continued to see its role in pharmaceutical research and development increase. MMPBSA was widely applied in the development of anticancer compounds where kinases were identified as the most promising targets. Recent inhibitor design includes the epidermal growth factor receptor kinase domain (Li et al., 2015; Moonrin et al., 2015; Zhao et al., 2017), anaplastic lymphoma kinase (Kong et al., 2015), cyclin-dependent kinases (Li X. L. et al., 2014; Czelen, 2016; Dong et al., 2016a,b, 2017; Arba et al., 2017), extracellular signal-regulated kinase 2 (Chen, 2017), casein kinase 2 (Wang X. W. et al., 2014), sphingosine kinases (Fang et al., 2016), Src/Abl tyrosine kinases (Fong, 2015; Ma et al., 2015), RET tyrosine kinase (Gao et al., 2015), PRK1 (Slynko et al., 2014), Akt kinase (Lu et al., 2015), phosphatidylinositol 3 kinase (Bian et al., 2014), and Myt1 kinase (Wichapong et al., 2014). As an illustration of the method's performance, Slynko et al. observed a correlation coefficient of 0.78 between the experimental pIC50 of 26 PRK1 inhibitors and computational MMPBSA binding affinities. They also utilized a quantitative structure activity relationship (QSAR) model to improve the correlation to 0.88, combining affinities from MMPBSA, QM/MMGBSA, and Glide scoring (Slynko et al., 2014). There were other attractive targets including indoleamine 2,3-dioxygenase 1 (Zou et al., 2017), translationally controlled tumor protein (Kumar R. et al., 2017), estrogen receptor (Anbarasu and Jayanthi, 2017), MutT homolog 1 (Zhou et al., 2016), survivin (Sarvagalla et al., 2016), CD44 (Nguyen et al., 2016), calmodulin (Gonzalez-Andrade et al., 2016), androgen receptor (Liu H. L. et al., 2016), human topoisomerase I (Guruge et al., 2016), Mcl-1 (Zhao et al., 2015), vascular endothelial growth factor receptor-2 (Wu et al., 2015), tubulin (Liao et al., 2014a,b; Santoshi and Naik, 2014; Suri and Naik, 2015; Suri et al., 2015), the Hsp70 protein family (Bhattacharjee et al., 2015; Schneider et al., 2016), the Hsp90 protein family (Arba et al., 2015), glucose 6-phosphate dehydrogenase (Obiol-Pardo et al., 2014; Zhao et al., 2014), lysozyme (Zhan et al., 2015), p53 (Verma S. et al., 2016), wheat germ agglutinin (Parasuraman et al., 2014), bromodomains (Muvva et al., 2014), matrix metalloproteinases (Zhou et al., 2014), protein arginine methyltransferases (Hong et al., 2014; Yan et al., 2014), human arsenic methyltransferase (Abro and Azam, 2016), Atox1 proteins (Wang X. L. et al., 2014), tyrosyl-DNA phosphodiesterase 2 (Kossmann et al., 2016), and urokinase-type plasminogen activator (Sa et al., 2014).

Applications in the development of antibacterial, antiviral, and antiparasitic drugs are also common, including inhibitor designs to targets such as enoyl reductase (Kamsri et al., 2014; Yang et al., 2017), succinate-ubiquinone oxidoreductase (Zhu X. L. et al., 2014; Xiong et al., 2015), thymidylate kinase (Biswas et al., 2017), peptidoglycan recognition proteins (Sahoo et al., 2014a), Mycobacterium tuberculosis pantothenate synthetase (Ntie-Kang et al., 2014), glutamine synthetase (Moreira et al., 2016), FtsZ protein (Zhang H. et al., 2015), and the cytochrome bc1 complex (Zhu et al., 2015). Other efforts focused on HIV viruses, including HIV-1 protease (Li D. et al., 2014; Tzoupis et al., 2014; Chen J. Z. et al., 2015; Meher and Wang, 2015; Chen, 2016; Hu et al., 2016; Sroczynski et al., 2016; Xanthopoulos et al., 2016), gp120 (Wang J. H. et al., 2015), gp41 (Song et al., 2014), reverse transcriptase (Bernardo and Silva, 2014), HIV integrase (Quevedo et al., 2014; Han et al., 2016), and a comparative analysis of inhibitors for HIV-1 and HIV-2 proteases (Chen et al., 2014a). Wright et al. studied 9 inhibitor-bound HIV-1 proteases and compared the absolute binding free energies computed via MMPBSA and MMGBSA with and without normal mode-derived configurational entropy. They found only the values of MMPBSA using the normal mode method were close to experimental values (Wright et al., 2014). Influenza was also an important research topic in antiviral compounds, with targets such as neuraminidase protein (Wang and Chen, 2014; Tran et al., 2015; Chintakrindi et al., 2016; Yang et al., 2016), non-structural proteins (Ai et al., 2014), H7N9 neuraminidase (Phanich et al., 2016), and the M2 proton channel (Homeyer et al., 2016). Other antiviral targets were also examined, such as RNA-dependent RNA polymerase (Yu et al., 2014; Wang J. H. et al., 2016), NS3/4A hepatitis C virus protease (Xue et al., 2014; Fu and Wei, 2015; Meeprasert et al., 2016), human furin (Omotuyi, 2015), and the capsomere of virus-like particles (Li Y. Y. et al., 2014). The method was additionally applied to the identification of novel protease targets for antiviral compounds (Pethe et al., 2017). Studies of antimalarials were also reported. For example, the reduced effectiveness of pyrimethamine, and its relationship to mutation in Plasmodium falciparum dihydrofolate reductase, was studied (Mokmak et al., 2014; Abbat et al., 2015). Falcipain-2, a papain family cysteine protease, was also examined as an antimalarial drug target (Omotuyi, 2014).

A third common group of applications are for studies of targets in neural disorders. Targets such as dopamine D2 receptor (Salmas et al., 2017), monoamine oxidase enzymes (Marsavelski and Vianello, 2017), and opioid G protein-coupled receptors (Leonis et al., 2014) were analyzed as antipsychotic compounds. Here, Leonis et al. employed the recently reported crystal structure of the human κ-opioid receptor (κ-OR) to explain the binding mechanism with its antagonist JDTic and agonist SalA. Both JDTic and SalA show that they are capable of forming a favorable complex in the MMPBSA analysis, which was later confirmed by experiment (Leonis et al., 2014). For Parkinson's disease, ligand-binding to the SUR1 receptor (Santos et al., 2016) and the adenosine receptor (Zhang L. H. et al., 2014) was analyzed. Many targets were studied for Alzheimer's disease including β-secretase (Koukoulitsa et al., 2016), angiotensin-converting enzyme (Bhavaraju et al., 2016), acetylcholinesterase and butyrylcholinesterase (Kurt et al., 2017), and inhibitors directly targeting amyloid aggregation (Berhanu and Masunov, 2015). Ataxin-2 protein was studied for the treatment of spinocerebellar ataxia (Sinha et al., 2017), superoxide dismutase for amyotrophic lateral sclerosis (Zhuang et al., 2016), and Niemann-Pick type Cl and C2 proteins (Poongavanam et al., 2016) that occur in rare neurodegenerative diseases.

MMPBSA was also widely applied in studies of many other major diseases. For blood disorders, targets included human serum albumin (Roy et al., 2015; Kragh-Hansen et al., 2016; Yu et al., 2016a,b), collagen-binding alpha 2 beta 1 integrin (Zhang and Sun, 2014), and human alpha-thrombin (Duan L. L. et al., 2016). In the latter study, Duan et al. investigated the binding affinity between human alpha-thrombin and ligand L86 by employing a nonpolarizable AMBER force field and the polarized protein-specific charge (PPC) force field. They found that the PPC binding affinity was closer to the experimental value reported by Nantermet et al. (2003). For immune disorders, studies on Janus kinases (Zhang W. et al., 2016; Wang J. L. et al., 2017), interleukin 10 cytokine (Ni et al., 2017), and receptor-related orphan receptor-gamma-t (Wang F. F. et al., 2015), were reported. For inflammatory disorders, targets such as COX-2 (Chaudhary and Aparoy, 2017), interleukin 6 (Verma R. et al., 2016), toll-like receptors (Shen et al., 2016), human leukocyte antigen (Kongkaew et al., 2015), chymase enzyme (Verma et al., 2017), tumor necrosis factor (Ivanisenko et al., 2014), and Nalp3 (Sahoo et al., 2014b) were studied. For diabetes, analyses of targets included phosphorylase kinase (Begum et al., 2015), glycogen synthase kinase (Arfeen et al., 2015), protein kinase C beta II (Grewal and Sobhia, 2014), and dipeptidyl peptidase-4 enzyme (Gu et al., 2014; Sneha and Doss, 2016). In a search for compounds for the treatment of chronic kidney disease, Vitamin D receptor and cytochrome P450 were analyzed (Nagamani et al., 2016). Malonyl-CoA decarboxylase (Ling et al., 2016), sirtuins (Karaman and Sippl, 2015), adipocyte fatty-acid binding protein (Chen et al., 2014b), 11β-hydroxysteroid dehydrogenase type 1 (Qian H. Y. et al., 2016), and protein tyrosine phosphatase 1B (Kocakaya, 2014) were reported for treatments of other metabolic diseases. For other diseases and disorders, targets such as pyrroline-5-carboxylate reductase for cutis laxa (Sang et al., 2017), renin complexes for hypertension (Tzoupis et al., 2015), and the type 1 receptor of TGF beta for wound healing (Gesteira et al., 2017) were examined.

There were a wide range of applications to targets outside the scope of pharmaceutical research. Although these studies are not related to drug discovery, accurate modeling of protein-ligand binding is also important in a wide variety of other contexts. For example, Starovoytov et al. calculated MMPBSA binding affinities of BPA-A, BPA-C, and BPA-D bound to human estrogen-related receptor γ in their toxicology research. Their analysis showed that BPA-A was the strongest binder to the receptor (Starovoytov et al., 2014). Other similar studies include globin, for its catalytic mechanism in the hydrolysis of substituted phenyl hexanoates (Ercan et al., 2014), the role of conserved residues in substrate binding to Brassica rapa auxin amidohydrolase (Smolko et al., 2016), the effect of hydrophobic interactions in substrate binding to recombinant enzyme carboxylesterase (Shao et al., 2014), the substrate-enzyme interactions of endo-1,4-β-xylanase (Zhan et al., 2014), azoreductase protein for the biodegradation of azo dyes (Dehghanian et al., 2016; Haghshenas et al., 2016), cytochrome P450 2A6 for nicotine addiction (Lu et al., 2014), Cel48F for producing bioethanol via fiber degradation (Qian M. D. et al., 2016), rubisco for biofuel production (Siqueira et al., 2016), streptavidin-biotin complex in biochemical sensing (Liu F. J. et al., 2016), sorotidine 5-monophosphate decarboxylase for its impressive rate enhancement (Jamshidi et al., 2014), tyrosyl-tRNA synthetases for genetic encoding of unnatural amino acids (Ren et al., 2015), T7 RNA polymerase for generating RNA labels (Borkotoky et al., 2016), AF9 in the YEATS family for the recognition of H3K9ac (Wang Q. et al., 2016), cysteine protease 1 precursor from Zea for the hydrolysate of corn gluten meal (Liu et al., 2014), folate receptor alpha for producing milk with high folate concentration (Sahoo et al., 2014c), and both acid amido synthetase (Wang X. et al., 2015) and brassinosteroid (Lei et al., 2015) for plant growth.

Protein-Protein Binding Interactions

There are many applications of MMPBSA that involve the calculation of protein-protein or protein-peptide binding affinities. For anti-cancer applications, we saw studies on complexes of an ankyrin repeat with integrin-linked kinase binding to PINCH1 (Gautam et al., 2014, 2015), cyclin-dependent kinase 8 with cyclin C (Xu et al., 2014), Hsp90 with Cdc37-derived peptides (Wang L. et al., 2015), and p53 with MDMX (Shi et al., 2015). As an illustration, Wang et al. designed an eleven-residue peptide that was able to bind to Hsp90 with a predicted affinity of 6.9 mM, which was comparable to the experimental value of 3.0 mM (Wang L. et al., 2015).

For anti-viral applications, research efforts were focused on analyzing interactions for ankyrin and domain III of the envelope protein of dengue virus II (Chong et al., 2015; Dubey et al., 2017), a modular capsomere of a murine polyomavirus (MPV) VLP designed to protect against influenza (Zhang L. et al., 2014), antibody recognition of immunoglobulin 2D1 for influenza virus H1N1 (Leong et al., 2015), differential structural dynamics and antigenicity of two HA-specific CTL epitopes binding to HLA-A*0201 for influenza virus H5N1 (Sun and Liu, 2015), a complex of histone deacetylase 6 and ubiquitin-specific protease 5 for influenza virus A (Passos et al., 2016), and for a complex of an antibody and epitope variant of HIV-1 p24 capsid protein (Karim et al., 2015). For antibacterial applications, there were studies of salamander PGRP1 with its splice variant adPGRP1a for the innate immune system (Qi Z. T. et al., 2017), and an adsorption mechanism of human beta-defensin-3 on bacterial membranes (Lee et al., 2016). Based on their MMPBSA analysis, Lee et al. found that the binding affinity of human beta-defensin-3 bound to a gram-positive membrane is over 3 times higher than when it is bound to a gram-negative membrane. Interestingly, this difference is mostly derived from electrostatic interactions, consistent with a net charge that is three times larger for gram-positive membranes compared to gram-negative membranes (Lee et al., 2016).

Studies of protein-protein interactions were also found for other diseases and disorders including serum paraoxonase 1 with high-density lipoprotein for antiatherosclerotic activity (Patra et al., 2014), vascular endothelial growth factor A with binding domains of anti-angiogenic agents for retinal neovascular degenerative diseases (Platania et al., 2015), angiotensin-converting enzyme with Angiotensin-II for hypertension (Guan et al., 2016), titin with T-cap/telethonin for dilated cardiomyopathy (Kumar D. T. et al., 2017), CC chemokine ligand 5 (CCL5) with human neutrophil peptide-1 (HNP1) for chronic inflammatory diseases (Wichapong et al., 2016), and ANKS6 with ANKS3 for polycystic kidney disease (Kan et al., 2016). In the study of CCL5 binding with human HNP1, Wichapong et al. reported a correlation coefficient of r = 0.66 between experiment and MMPBSA results for different species of CCL5. In addition, they confirmed that the entropy change upon binding was negligible in this study, so the entropy term could be ignored when a relative binding free energy was considered (Wichapong et al., 2016).

In addition, there were research efforts that focused on basic mechanisms such as protein stability and conformational dynamics (Bhavaraju and Hansmann, 2015; Getov et al., 2016). Many other studies were also reported, including an analysis of full length amylin oligomer aggregation (Berhanu and Masunov, 2014), effects of single point mutations on amyloid formation (Bhavaraju and Hansmann, 2015; Getov et al., 2016; Petukh et al., 2016), interactions between methylated histone H3 and effector domains of the PHD family in pursuit of a molecular mechanism of epigenetics (Grauffel et al., 2015), the binding mechanism of actin-depolymerizing factor 1 and G-actin (Du et al., 2016), the interaction between phosphotyrosine binding domains and peptides for neuronal development, immune responses, tissue homeostasis, and cell growth (Sain et al., 2016), the cognate transducer complex srII-htrII for the downstream signaling mechanism of sensory rhodopsin (Sahoo and Fujiwara, 2017), the binding of CBP to c-Myb for understanding the exact function of CBP and its interaction with c-Myb (Odoux et al., 2016), the catalytic stability of the tetrameric complex of cystathionine gamma-lyase (El-Sayed et al., 2015), the study of the mechanism of a molecular chaperone using acid-stress chaperone HdeA and its substrate protein (Zhou et al., 2017), and the binding of thiopeptide to a ribosomal subunit in order to understand the structure–activity relationship of thiostrepton derivatives (Wolf et al., 2014).

There are also several non-biomedical applications such as a study on the interactions between two adjacent gamma tubulins within the gamma-tubulin ring complex for growing yeast (Suri et al., 2014), between beta-sheet regions in corneous beta proteins of sauropsids to explain its stability and polymerization into filaments (Calvaresi et al., 2016), and the recognition of Avr protein by eukaryotic transcription factor xa5 of rice to understand the gene-for-gene mechanism that governs the direct interaction of R-Avr protein (Dehury et al., 2015). In the study by Suri et al. computational alanine scanning was employed to determine hotspot contributions in the interaction between gamma tubulins. Their analysis showed that most hotspot mutations reduce affinity by 1.16 kcal/mol, while for very crucial amino acids, such as Asp252 and Arg341, the affinity was decreased by more than 10 kcal/mol, which correlated well with experiment (Suri et al., 2014).

Complexes Involving Nucleic Acids

For DNA-protein interactions, there were mechanistic studies involving proteins such as HU, one of the major nucleoid-associated proteins for stabilizing DNA bending (Kim et al., 2014), highly conserved chromatin protein Cren7 for cellular processes such as transcription, replication, and repair (Chen L. et al., 2015), MutS, which recognizes mismatched DNA in DNA repair using ATP (Ishida and Matsumoto, 2016), copper nucleases for predicting electrostatic interactions with B-DNA (Liu C. M. et al., 2016), and metallopeptides binding to the Drew-Dickerson dodecamer (Galindo-Murillo and Cheatham, 2014). For RNA-protein interactions, the binding of toll-like receptor 3 and 22 was studied in relation to fish viral diseases (Sahoo et al., 2015), and the RNA-recognition motif of RNA-binding protein was analyzed for anti-cancer actitivies (Chang et al., 2016). Two studies on DNA-DNA interactions were also reported, including the calculation of binding affinities of short double stranded oligonucleotides (Yesudas et al., 2015), of three-quartet intramolecular human telomeric DNA G-quadruplexes (Islam et al., 2016), and of strand-strand interactions in a human telomeric tetrameric quadruplex (Chaubey et al., 2015). Yesudas et al. studied oligonucleotides (9–20 mers DNA) with the “3-trajectory” approach, without counter ions, by using GBSA, PBSA, and 3D-RISM-KH methods to calculate binding free energies. They showed that the 3D-RISM-KH method was in better agreement with the experimental data for larger oligonucleotides while the GBSA method performed better for smaller oligonucleotides (Yesudas et al., 2015).

There were also several studies involving small ligands binding to nucleic acids. Interactions of anticancer compounds with DNA, such as cisplatin and oxaliplatin (Jalili et al., 2016), distamycin with the DNA minor groove (Jalili and Maddah, 2017), and plant alkaloid chelerythrine with the human telomere sequence (Ghosh et al., 2015) were reported. Aminobenzimidazole binding to an internal ribosome entry site was studied for its anti-hepatitis C virus effect (Henriksen et al., 2014). The interactions of histone-derived antimicrobial peptides buforin II and DesHDAP1 with DNA were also investigated (Sim et al., 2017). Basic mechanistic studies were carried out, including cationic porphyrin-anthraquinone hybrids binding to DNA duplexes (Arba and Tjahjono, 2015) and G-quadruplexes (Arba et al., 2016), as well as ligands binding to riboswitches upon mutation (Hu et al., 2017). The interactions of reactive metabolites of anticancer compounds with DNA were also analyzed (Tumbi et al., 2014). In the binding of ligands to riboswitches, Hu et al. analyzed the relative binding free energies for a guanine riboswitch (GR) and a GUA complex relative to three complexes: 6GU (3.4 kcal/mol), 2BP (5.48 kcal/mol), and XAN (6.19 kcal/mol). These values were in good agreement with experimental observations of 3.21, 4.12, and 5.47 kcal/mol, respectively (Hu et al., 2017).

Guest-Host and Nano Systems

For guest-host systems, several studies were reported. Two octa acid hosts complexed with six guest molecules were analyzed by Bhakat and Soderhjelm for resolving the problem of trapped water in binding cavities. They performed a well-tempered funnel metadynamics (WT-FM) and MMPBSA analyses for the two octa-acid hosts, OAH (without methyl groups) and OAMe (with methyl groups), using both GAFF and OPLS force fields. Their analyses showed that the binding affinities of WT-FM are basically similar to experimental values while MMPBSA results have errors in the range of 5–10 kcal/mol due to its approximate nature (Bhakat and Soderhjelm, 2017). A binding interaction was analyzed to show that electrostatic interactions have the largest contribution to the stability of the cucurbituril-pseudorotaxane complex (Malhis et al., 2015). Complex stability was analyzed for multiple systems, such as the 1:1 and 1:2 inclusion complexes formed by nor-Seco-cucurbit[10]uril and 1-adamantanmethylammonium in water (El-Barghouthi et al., 2015), cyclodextrin-Ibuprofen complexes (Wang R. M. et al., 2015), E-selectin-oligosaccharide complexes (Barra et al., 2017), and the naringenin-2,6-dimethyl β-cyclodextrin inclusion complex (Sangpheak et al., 2014). Finally, enantiomeric discrimination of chiral organic salts by chiral aza-15-crown-5 ether with C1 symmetry was reported (Kocakaya et al., 2015).

For nano systems, a mechanism explaining how C-60 can block potassium ion channels was proposed (Calvaresi et al., 2015). The authors showed that a new binding site for C-60 exists in the channel cavity at the intracellular entrance of the selectivity filter. The escape barrier from the binding site is ~21 kcal/mol as calculated via the umbrella sampling method, in good agreement with the MMPBSA result. MMPBSA was also used in the theoretical design of the cyclic lipopeptide nanotube as a molecular channel in the lipid bilayer (Izadyar et al., 2015; Khavani et al., 2015, 2017), in the study of the enzyme immobilization mechanism of alpha-chymotrypsin onto carbon nanotubes in organic media (Zhang L. Y. et al., 2015), and the mechanism of carbon nanotube activation of subtilisin Carlsberg in polar and non-polar organic media (Zhang L. Y. et al., 2016).

Monomer Stability

For single biomolecule stability research, we saw reports on the structures and energies of the alternate frame folding calbindin-D9k protein (Tong et al., 2015), the effect of copper ions in the stability and structural change of human growth protein (Tazikeh-Lemeski, 2016), and the role of potassium in stabilizing the human telomeric intra-molecular G-quadruplex structure (Wang Z. et al., 2015; Wang and Liu, 2017). In their study of calbindin-D9k, Tong et al. found two transition states and an intermediate state with a first rate-controlling barrier of 4.7 kcal/mol and a second barrier of 1.7 kcal/mol using MMPBSA, both of which are in good agreement with experiment (Tong et al., 2015).

Current Limitations and Future Directions

MMPBSA methods are widely applied to calculate binding affinities at a reasonable computational cost. These computational analyses have provided a large volume of valuable predictive results in a wide variety of studies. Even though MMPBSA is known to be less accurate than some of the more computationally expensive methods, like the free energy perturbation and thermodynamic integration methods, the qualitative agreement is often good enough to aid collaborative efforts involving both computational and experimental researchers. Developers are also actively working to improve MMPBSA methods for higher accuracy and efficiency by introducing better solute and solvent models, by porting the expensive energy computation (mostly involving the solvation terms) to faster GPU platforms, and by improving entropy estimations. Efforts are also needed to extend the MMPBSA method for various screening purposes that involve a large number of ligands and/or mutations to achieve a higher overall level of accuracy and efficiency. It is apparent that applications of the MMPBSA method have grown considerably in many different areas of biomolecular study. Most of these applications involve protein-ligand binding affinity calculations due to their utility in drug discovery research efforts. There are also many applications in the study of biomacromolecular complexes. It is also noteworthy that a few guest-host and nano systems utilized MMPBSA calculations, indicating a wider development space for this method in the future.

Author Contributions

CW: Did the literature research and drafted the review; DG: Extensively revised the review; LX: Wrote the membrane protein extension review; RQ: Wrote the solvation term review; RL: Designed the overall structure of the review and was involved in drafting the review. All people polished the final version.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer WC and handling Editor declared their shared affiliation.

Acknowledgments

This work was supported by National Institute of Health/NIGMS (GM093040 & GM079383 to RL).

References

Abbat, S., Jain, V., and Bharatam, P. V. (2015). Origins of the specificity of inhibitor P218 toward wild-type and mutant PfDHFR: a molecular dynamics analysis. J. Biomol. Struct. Dyn. 33, 1913–1928. doi: 10.1080/07391102.2014.979231

PubMed Abstract | CrossRef Full Text | Google Scholar

Abro, A., and Azam, S. S. (2016). Binding free energy based analysis of arsenic (+3 oxidation state) methyltransferase with S-adenosylmethionine. J. Mol. Liq. 220, 375–382. doi: 10.1016/j.molliq.2016.04.109

CrossRef Full Text | Google Scholar

Ai, H. X., Zhang, L., Chang, A. K., Wei, H. Y., Che, Y. C., and Liu, H. S. (2014). Virtual screening of potential inhibitors from TCM for the CPSF30 binding site on the NS1A protein of influenza A virus. J. Mol. Model. 20:10. doi: 10.1007/s00894-014-2142-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Anbarasu, K., and Jayanthi, S. (2017). Designing and optimization of novel human LMTK3 inhibitors against breast cancer - a computational approach. J. Recept. Signal Transduct. 37, 51–59. doi: 10.3109/10799893.2016.1155069

PubMed Abstract | CrossRef Full Text | Google Scholar

Aqvist, J., and Marelius, J. (2001). The linear interaction energy method for predicting ligand binding free energies. Comb. Chem. High Throughput Screen. 4, 613–626. doi: 10.2174/1386207013330661

PubMed Abstract | CrossRef Full Text | Google Scholar

Arba, M., and Tjahjono, D. H. (2015). The binding modes of cationic porphyrin-anthraquinone hybrids to DNA duplexes: in silico study. J. Biomol. Struct. Dyn. 33, 657–665. doi: 10.1080/07391102.2014.887480

PubMed Abstract | CrossRef Full Text | Google Scholar

Arba, M., Ihsan, S., Ramadhan, L. A. N., and Tjahjono, D. H. (2017). In silico study of porphyrin-anthraquinone hybrids as CDK2 inhibitor. Comput. Biol. Chem. 67, 9–14. doi: 10.1016/j.compbiolchem.2016.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Arba, M., Kartasasmita, R. E., and Tjahjono, D. H. (2015). “Molecular docking and molecular dynamics simulation of the interaction of cationic imidazolium porphyrin-anthraquinone and Hsp90,” in Proceedings of the 3rd International Conference on Computation for Science and Technology, ed D. H. Tjahjono (Paris: Atlantis Press), 1–5. doi: 10.2991/iccst-15.2015.1

CrossRef Full Text | Google Scholar

Arba, M., Kartasasmita, R. E., and Tjahjono, D. H. (2016). Molecular docking and dynamics simulations on the interaction of cationic porphyrin-anthraquinone hybrids with DNA G-quadruplexes. J. Biomol. Struct. Dyn. 34, 427–438. doi: 10.1080/07391102.2015.1033015

PubMed Abstract | CrossRef Full Text | Google Scholar

Arfeen, M., Patel, R., Khan, T., and Bharatam, P. V. (2015). Molecular dynamics simulation studies of GSK-3 beta ATP competitive inhibitors: understanding the factors contributing to selectivity. J. Biomol. Struct. Dyn. 33, 2578–2593. doi: 10.1080/07391102.2015.1063457

CrossRef Full Text | Google Scholar

Ashbaugh, H. S., Kaler, E. W., and Paulaitis, M. E. (1999). A “universal” surface area correlation for molecular hydrophobic phenomena. J. Am. Chem. Soc. 121, 9243–9244. doi: 10.1021/ja992119h

CrossRef Full Text | Google Scholar

Barra, P. A., Ribeiro, A. J. M., Ramos, M. J., Jimenez, V. A., Alderete, J. B., and Fernandes, P. A. (2017). Binding free energy calculations on E-selectin complexes with sLe(x) oligosaccharide analogs. Chem. Biol. Drug Des. 89, 114–123. doi: 10.1111/cbdd.12837

CrossRef Full Text | Google Scholar

Bashford, D., and Karplus, M. (1990). Pkas of ionizable groups in proteins - atomic detail from a continuum electrostatic model. Biochemistry 29, 10219–10225. doi: 10.1021/bi00496a010

CrossRef Full Text | Google Scholar

Begum, J., Skamnaki, V. T., Moffatt, C., Bischler, N., Sarrou, J., Skaltsounis, A. L., et al. (2015). An evaluation of indirubin analogues as phosphorylase kinase inhibitors. J. Mol. Graph. Model. 61, 231–242. doi: 10.1016/j.jmgm.2015.07.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Bennett, C. H. (1976). Efficient estimation of free-energy differences from monte-carlo data. J. Comput. Phys. 22, 245–268. doi: 10.1016/0021-9991(76)90078-4

CrossRef Full Text | Google Scholar

Ben-Shalornit, I. Y., Pfeiffer-Marek, S., Baringhaus, K. H., and Gohlket, H. (2017). Efficient approximation of ligand rotational and translational entropy changes upon binding for use in MM-PBSA calculations. J. Chem. Inf. Model. 57, 170–189. doi: 10.1021/acs.jcim.6b00373

CrossRef Full Text | Google Scholar

Berhanu, W. M., and Masunov, A. E. (2014). Full length amylin oligomer aggregation: insights from molecular dynamics simulations and implications for design of aggregation inhibitors. J. Biomol. Struct. Dyn. 32, 1651–1669. doi: 10.1080/07391102.2013.832635

PubMed Abstract | CrossRef Full Text | Google Scholar

Berhanu, W. M., and Masunov, A. E. (2015). Atomistic mechanism of polyphenol amyloid aggregation inhibitors: molecular dynamics study of Curcumin, Exifone, and Myricetin interaction with the segment of tau peptide oligomer. J. Biomol. Struct. Dyn. 33, 1399–1411. doi: 10.1080/07391102.2014.951689

PubMed Abstract | CrossRef Full Text | Google Scholar

Bernardo, C. E. P., and Silva, P. J. (2014). Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition. PeerJ 2:18. doi: 10.7717/peerj.470

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhakat, S., and Soderhjelm, P. (2017). Resolving the problem of trapped water in binding cavities: prediction of host-guest binding free energies in the SAMPL5 challenge by funnel metadynamics. J. Comput. Aided Mol. Des. 31, 119–132. doi: 10.1007/s10822-016-9948-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhattacharjee, R., Devi, A., and Mishra, S. (2015). Molecular docking and molecular dynamics studies reveal structural basis of inhibition and selectivity of inhibitors EGCG and OSU-03012 toward glucose regulated protein-78 (GRP78) overexpressed in glioblastoma. J. Mol. Model. 21:17. doi: 10.1007/s00894-015-2801-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhavaraju, M., and Hansmann, U. H. E. (2015). Effect of single point mutations in a form of systemic amyloidosis. Protein Sci. 24, 1451–1462. doi: 10.1002/pro.2730

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhavaraju, M., Phillips, M., Bowman, D., Aceves-Hernandez, J. M., and Hansmann, U. H. E. (2016). Binding of ACE-inhibitors to in vitro and patient-derived amyloid-beta fibril models. J. Chem. Phys. 144:13. doi: 10.1063/1.4938261

PubMed Abstract | CrossRef Full Text | Google Scholar

Bian, X. L., Dong, W. Q., Zhao, Y., Sun, R., Kong, W. J., and Li, Y. P. (2014). Definition of the binding mode of phosphoinositide 3-kinase alpha-selective inhibitor A-66S through molecular dynamics simulation. J. Mol. Model. 20:10. doi: 10.1007/s00894-014-2166-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Biswas, A., Shukla, A., Vijayan, R. S. K., Jeyakanthan, J., and Sekar, K. (2017). Crystal structures of an archaeal thymidylate kinase from Sulfolobus tokodaii provide insights into the role of a conserved active site Arginine residue. J. Struct. Biol. 197, 236–249. doi: 10.1016/j.jsb.2016.12.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Borkotoky, S., Meena, C. K., and Murali, A. (2016). Interaction analysis of T7 RNA polymerase with heparin and its low molecular weight derivatives - an in silico approach. Bioinform. Biol. Insights 10, 155–166. doi: 10.4137/BBI.S40427

PubMed Abstract | CrossRef Full Text | Google Scholar

Botello-Smith, W. M., and Luo, R. (2015). Applications of MMPBSA to membrane proteins i: efficient numerical solutions of periodic poisson-boltzmann equation. J. Chem. Inf. Model. 55, 2187–2199. doi: 10.1021/acs.jcim.5b00341

PubMed Abstract | CrossRef Full Text | Google Scholar

Botello-Smith, W. M., Liu, X., Cai, Q., Li, Z., Zhao, H., and Luo, R. (2012). Numerical poisson-boltzmann model for continuum membrane systems. Chem. Phys. Lett. 555, 274–281. doi: 10.1016/j.cplett.2012.10.081

PubMed Abstract | CrossRef Full Text | Google Scholar

Cai, Q., Hsieh, M.-J., Wang, J., and Luo, R. (2010). Performance of nonlinear finite-difference poisson-boltzmann solvers. J. Chem. Theory Comput. 6, 203–211. doi: 10.1021/ct900381r

PubMed Abstract | CrossRef Full Text | Google Scholar

Cai, Q., Wang, J., Zhao, H.-K., and Luo, R. (2009). On removal of charge singularity in Poisson-Boltzmann equation. J. Chem. Phys. 130:145101. doi: 10.1063/1.3099708

PubMed Abstract | CrossRef Full Text | Google Scholar

Cai, Q., Ye, X., Wang, J., and Luo, R. (2011). On-the-fly numerical surface integration for finite-difference poisson-boltzmann methods. J. Chem. Theory Comput. 7, 3608–3619. doi: 10.1021/ct200389p

PubMed Abstract | CrossRef Full Text | Google Scholar

Calvaresi, M., Eckhart, L., and Alibardi, L. (2016). The molecular organization of the beta-sheet region in Corneous beta proteins (beta-keratins) of sauropsids explains its stability and polymerization into filaments. J. Struct. Biol. 194, 282–291. doi: 10.1016/j.jsb.2016.03.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Calvaresi, M., Furini, S., Domene, C., Bottoni, A., and Zerbetto, F. (2015). Blocking the passage: C-60 geometrically clogs K+ channels. ACS Nano 9, 4827–4834. doi: 10.1021/nn506164s

CrossRef Full Text | Google Scholar

Cao, R., Huang, N., and Wang, Y. L. (2014). Evaluation and application of MD-PB/SA in structure-based hierarchical virtual screening. J. Chem. Inf. Model. 54, 1987–1996. doi: 10.1021/ci5003203

PubMed Abstract | CrossRef Full Text | Google Scholar

Case, D. A., Betz, R. M., Botello-Smith, W., Cerutti, D. S., Cheatham, T. E., Darden, T. A., et al. (2016). “Amber 2016.” San Francisco, CA: University of California.

Chang, C. E. A., Chen, W., and Gilson, M. K. (2007). Ligand configurational entropy and protein binding. Proc. Natl. Acad. Sci. U.S.A. 104, 1534–1539. doi: 10.1073/pnas.0610494104

PubMed Abstract | CrossRef Full Text | Google Scholar

Chang, S., Zhang, D. W., Xu, L., Wan, H., Hou, T. J., and Kong, R. (2016). Exploring the molecular basis of RNA recognition by the dimeric RNA-binding protein via molecular simulation methods. RNA Biol. 13, 1133–1143. doi: 10.1080/15476286.2016.1223007

PubMed Abstract | CrossRef Full Text | Google Scholar

Chaubey, A. K., Dubey, K. D., and Ojha, R. P. (2015). MD simulation of LNA-modified human telomeric G-quadruplexes: a free energy calculation. Med. Chem. Res. 24, 753–763. doi: 10.1007/s00044-014-1182-y

CrossRef Full Text | Google Scholar

Chaudhary, N., and Aparoy, P. (2017). Deciphering the mechanism behind the varied binding activities of COXIBs through Molecular Dynamic Simulations, MM-PBSA binding energy calculations and per-residue energy decomposition studies. J. Biomol. Struct. Dyn. 35, 868–882. doi: 10.1080/07391102.2016.1165736

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, F., Liu, H., Sun, H. Y., Pan, P. C., Li, Y. Y., Li, D., et al. (2016). Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking. Phys. Chem. Chem. Phys. 18, 22129–22139. doi: 10.1039/C6CP03670H

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, J. Z. (2016). Drug resistance mechanisms of three mutations V32I, I47V and V82I in HIV-1 protease toward inhibitors probed by molecular dynamics simulations and binding free energy predictions. RSC Adv. 6, 58573–58585. doi: 10.1039/C6RA09201B

CrossRef Full Text | Google Scholar

Chen, J. Z. (2017). Clarifying binding difference of ATP and ADP to extracellular signal-regulated kinase 2 by using molecular dynamics simulations. Chem. Biol. Drug Des. 89, 548–558. doi: 10.1111/cbdd.12877

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, J. Z., Liang, Z. Q., Wang, W., Yi, C. H., Zhang, S. L., and Zhang, Q. G. (2014a). Revealing origin of decrease in potency of darunavir and amprenavir against HIV-2 relative to HIV-1 protease by molecular dynamics simulations. Sci. Rep. 4:6872. doi: 10.1038/srep06872

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, J. Z., Wang, J. N., and Zhu, W. L. (2014b). Binding modes of three inhibitors 8CA, F8A and I4A to A-FABP studied based on molecular dynamics simulation. PLoS ONE 9:e99862. doi: 10.1371/journal.pone.0099862

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, J. Z., Wang, X. Y., Zhu, T., Zhang, Q. G., and Zhang, J. Z. H. (2015). A comparative insight into amprenavir resistance of mutations V32I, G48V, I50V, I54V, and I84V in HIV-1 protease based on thermodynamic integration and MM-PBSA methods. J. Chem. Inf. Model. 55, 1903–1913. doi: 10.1021/acs.jcim.5b00173

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, L., Zheng, Q. C., and Zhang, H. X. (2015). Insights into the effects of mutations on Cren7-DNA binding using molecular dynamics simulations and free energy calculations. Phys. Chem. Chem. Phys. 17, 5704–5711. doi: 10.1039/C4CP05413J

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, W., Chang, C. E., and Gilson, M. K. (2004). Calculation of cyclodextrin binding affinities: energy, entropy, and implications for drug design. Biophys. J. 87, 3035–3049. doi: 10.1529/biophysj.104.049494

PubMed Abstract | CrossRef Full Text | Google Scholar

Chintakrindi, A. S., Martis, E. A., Gohil, D. J., Kothari, S. T., Chowdhary, A. S., Coutinho, E. C., et al. (2016). A computational model for docking of noncompetitive neuraminidase inhibitors and probing their binding interactions with neuraminidase of influenza virus H5N1. Curr. Comput. Aided Drug Des. 12, 272–281. doi: 10.2174/1573409912666160713111242

PubMed Abstract | CrossRef Full Text | Google Scholar

Chong, W. L., Zain, S. M., Abd Rahman, N., Othman, R., Othman, S. B., Nimmanpipug, P., et al. (2015). “Exploration of residue binding energy of potential ankyrin for Dengue virus II from MD simulations,” in Proceedings of the 3rd International Conference on Computation for Science and Technology, ed D. H. Tjahjono (Paris: Atlantis Press), 100–103.

Google Scholar

Colmenares, J., Galizia, A., Ortiz, J., Clematis, A., and Rocchia, W. (2014a). A combined MPI-CUDA parallel solution of linear and nonlinear Poisson-Boltzmann equation. Biomed Res. Int. 2014:560987. doi: 10.1155/2014/560987

PubMed Abstract | CrossRef Full Text | Google Scholar

Colmenares, J., Ortiz, J., and Rocchia, W. (2014b). GPU linear and non-linear Poisson-Boltzmann solver module for DelPhi. Bioinformatics 30, 569–570. doi: 10.1093/bioinformatics/btt699

PubMed Abstract | CrossRef Full Text | Google Scholar

Czelen, P. (2016). Molecular dynamics study on inhibition mechanism of CDK-2 and GSK-3 beta by CHEMBL272026 molecule. Struct. Chem. 27, 1807–1818. doi: 10.1007/s11224-016-0803-0

CrossRef Full Text | Google Scholar

Davis, M. E., and McCammon, J. A. (1990). Electrostatics in biomolecular structure and dynamics. Chem. Rev. 90, 509–521. doi: 10.1021/cr00101a005

CrossRef Full Text | Google Scholar

Dehghanian, F., Haghshenas, H., Kay, M., and Tavakol, H. (2016). A molecular dynamics investigation on the interaction properties of AzrC and its cofactor. J. Iran. Chem. Soc. 13, 2143–2153. doi: 10.1007/s13738-016-0932-9

CrossRef Full Text | Google Scholar

Dehury, B., Maharana, J., Sahoo, B. R., Sahu, J., Sen, P., Modi, M. K., et al. (2015). Molecular recognition of avirulence protein (avrxa5) by eukaryotic transcription factor xa5 of rice (Oryza sativa L.): insights from molecular dynamics simulations. J. Mol. Graph. Model. 57, 49–61. doi: 10.1016/j.jmgm.2015.01.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Dong, K. K., Wang, X., Yang, X. Y., and Zhu, X. L. (2016a). Binding mechanism of CDK5 with roscovitine derivatives based on molecular dynamics simulations and MM/PBSA methods. J. Mol. Graph. Model. 68, 57–67. doi: 10.1016/j.jmgm.2016.06.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Dong, K. K., Yang, X. Y., Zhao, T. T., and Zhu, X. L. (2016b). Exploring the selectivity of Tetrahydropyrido 1,2-a isoindolone derivatives to GSK3 beta and CDK5 by computational methods. Chin. J. Inorg. Chem. 32, 1919–1930. doi: 10.11862/CJIC.2016.263

CrossRef Full Text | Google Scholar

Dong, K. K., Yang, X. Y., Zhao, T. T., and Zhu, X. L. (2017). An insight into the inhibitory selectivity of 4-(Pyrazol-4-yl)-pyrimidines to CDK4 over CDK2. Mol. Simul. 43, 599–609. doi: 10.1080/08927022.2017.1279283

CrossRef Full Text | Google Scholar

Du, J., Wang, X., Dong, C. H., Yang, J. M., and Yao, X. J. (2016). Computational study of the binding mechanism of actin-depolymerizing factor 1 with actin in Arabidopsis thaliana. PLoS ONE 11:e159053. doi: 10.1371/journal.pone.0159053

PubMed Abstract | CrossRef Full Text | Google Scholar

Duan, L., Liu, X., and Zhang, J. Z. H. (2016). Interaction entropy: a new paradigm for highly efficient and reliable computation of protein–ligand binding free energy. J. Am. Chem. Soc. 138, 5722–5728. doi: 10.1021/jacs.6b02682

PubMed Abstract | CrossRef Full Text | Google Scholar

Duan, L. L., Feng, G. Q., and Zhang, Q. G. (2016). Large-scale molecular dynamics simulation: effect of polarization on thrombin-ligand binding energy. Sci. Rep. 6:31488. doi: 10.1038/srep31488

PubMed Abstract | CrossRef Full Text | Google Scholar

Duan, L. L., Feng, G. Q., Wang, X. W., Wang, L. Z., and Zhang, Q. G. (2017). Effect of electrostatic polarization and bridging water on CDK2-ligand binding affinities calculated using a highly efficient interaction entropy method. Phys. Chem. Chem. Phys. 19, 10140–10152. doi: 10.1039/C7CP00841D

PubMed Abstract | CrossRef Full Text | Google Scholar

Dubey, K. D., Tiwari, G., and Ojha, R. P. (2017). Targeting domain-III hinging of dengue envelope (DENV-2) protein by MD simulations, docking and free energy calculations. J. Mol. Model. 23:102. doi: 10.1007/s00894-017-3259-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Dzubiella, J., Swanson, J. M. J., and McCammon, J. A. (2006). Coupling nonpolar and polar solvation free energies in implicit solvent models. J. Chem. Phys. 124:084905. doi: 10.1063/1.2171192

PubMed Abstract | CrossRef Full Text | Google Scholar

Edinger, S. R., Cortis, C., Shenkin, P. S., and Friesner, R. A. (1997). Solvation free energies of peptides: comparison of approximate continuum solvation models with accurate solution of the Poisson-Boltzmann equation. J. Phys. Chem. B 101, 1190–1197. doi: 10.1021/jp962156k

CrossRef Full Text | Google Scholar

El-Barghouthi, M. I., Abdel-Halim, H. M., Haj-Ibrahim, F. J., Bodoor, K., and Assaf, K. I. (2015). Molecular dynamics of nor-Seco-cucurbit 10 uril complexes. J. Incl. Phenom. Macrocycl. Chem. 82, 323–333. doi: 10.1007/s10847-015-0488-9

CrossRef Full Text | Google Scholar

El-Sayed, A. S. A., Abdel-Azeim, S., Ibrahim, H. M., Yassin, M. A., Abdel-Ghany, S. E., Esener, S., et al. (2015). Biochemical stability and molecular dynamic characterization of Aspergillus fumigatus cystathionine gamma-lyase in response to various reaction effectors. Enzyme Microb. Technol. 81, 31–46. doi: 10.1016/j.enzmictec.2015.08.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Ercan, S., Arslan, N., Kocakaya, S. O., Pirinccioglu, N., and Williams, A. (2014). Experimental and theoretical study of the mechanism of hydrolysis of substituted phenyl hexanoates catalysed by globin in the presence of surfactant. J. Mol. Model. 20:2096. doi: 10.1007/s00894-014-2096-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Fang, L., Wang, X. J., Xi, M. Y., Liu, T. Q., and Yin, D. L. (2016). Assessing the ligand selectivity of sphingosine kinases using molecular dynamics and MM-PBSA binding free energy calculations. Mol. Biosyst. 12, 1174–1182. doi: 10.1039/C6MB00067C

PubMed Abstract | CrossRef Full Text | Google Scholar

Fisicaro, G., Genovese, L., Andreussi, O., Marzari, N., and Goedecker, S. (2016). A generalized Poisson and Poisson-Boltzmann solver for electrostatic environments. J. Chem. Phys. 144:014103. doi: 10.1063/1.4939125

PubMed Abstract | CrossRef Full Text | Google Scholar

Floris, F. M., Tomasi, J., and Ahuir, J. L. P. (1991). Dispersion and repulsion contributions to the solvation energy - refinements to a simple computational model in the continuum approximation. J. Comput. Chem. 12, 784–791. doi: 10.1002/jcc.540120703

CrossRef Full Text | Google Scholar

Floris, F., and Tomasi, J. (1989). Evaluation of the dispersion contribution to the solvation energy - a simple computational model in the continuum approximation. J. Comput. Chem. 10, 616–627. doi: 10.1002/jcc.540100504

CrossRef Full Text | Google Scholar

Fong, C. W. (2015). Binding energies of tyrosine kinase inhibitors: error assessment of computational methods for imatinib and nilotinib binding. Comput. Biol. Chem. 58, 40–54. doi: 10.1016/j.compbiolchem.2015.05.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, J. J., and Wei, J. (2015). Molecular dynamics study on drug resistance mechanism of HCV NS3/4A protease inhibitor: BI201335. Mol. Simul. 41, 674–682. doi: 10.1080/08927022.2014.917298

CrossRef Full Text | Google Scholar

Galindo-Murillo, R., and Cheatham, T. E. (2014). DNA binding dynamics and energetics of cobalt, nickel, and copper metallopeptides. ChemMedChem 9, 1252–1259. doi: 10.1002/cmdc.201402020

PubMed Abstract | CrossRef Full Text | Google Scholar

Gallicchio, E., and Levy, R. M. (2004). AGBNP: An analytic implicit solvent model suitable for molecular dynamics simulations and high-resolution modeling. J. Comput. Chem. 25, 479–499. doi: 10.1002/jcc.10400

PubMed Abstract | CrossRef Full Text | Google Scholar

Gallicchio, E., Kubo, M. M., and Levy, R. M. (2000). Enthalpy-entropy and cavity decomposition of alkane hydration free energies: numerical results and implications for theories of hydrophobic solvation. J. Phys. Chem. B 104, 6271–6285. doi: 10.1021/jp0006274

CrossRef Full Text | Google Scholar

Gallicchio, E., Zhang, L. Y., and Levy, R. M. (2002). The SGB/NP hydration free energy model based on the surface generalized born solvent reaction field and novel nonpolar hydration free energy estimators. J. Comput. Chem. 23, 517–529. doi: 10.1002/jcc.10045

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, C. X., Grotli, M., and Eriksson, L. A. (2015). Characterization of interactions and pharmacophore development for DFG-out inhibitors to RET tyrosine kinase. J. Mol. Model. 21:167. doi: 10.1007/s00894-015-2708-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Gautam, V., Chong, W. L., Wisitponchai, T., Nimmanpipug, P., Zain, S. M., Rahman, N. A., et al. (2014). “GPU-enabled molecular dynamics simulations of ankyrin kinase complex,” in 3rd International Conference on Fundamental and Applied Sciences, eds S. C. Dass, B. H. Guan, A. H. Bhat, I. Faye, H. Soleimani, and N. Yahya (Melville, WA: American Institute of Physics), 112–115.

Google Scholar

Gautam, V., Sabri, N. H., Chong, W. L., Zain, S. M., Rahman, N. A., Lee, V. S., et al. (2015). “Computational alanine scanning mutagenesis: characterizing the hotspots of ILK-ankyrin repeat and PINCH1 complex,” in Proceedings of the 3rd International Conference on Computation for Science and Technology, ed D. H. Tjahjono (Paris: Atlantis Press), 92–94.

Google Scholar

Genheden, S., and Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 10, 449–461. doi: 10.1517/17460441.2015.1032936

PubMed Abstract | CrossRef Full Text | Google Scholar

Georgescu, R. E., Alexov, E. G., and Gunner, M. R. (2002). Combining conformational flexibility and continuum electrostatics for calculating pK(a)s in proteins. Biophys. J. 83, 1731–1748. doi: 10.1016/S0006-3495(02)73940-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Gesteira, T. F., Coulson-Thomas, V. J., Yuan, Y., Zhang, J. H., Nader, H. B., and Kao, W. W. Y. (2017). Lumican peptides: rational design targeting ALK5/TGFBRI. Sci. Rep. 7:42057. doi: 10.1038/srep42057

PubMed Abstract | CrossRef Full Text | Google Scholar

Getov, I., Petukh, M., and Alexov, E. (2016). SAAFEC: predicting the effect of single point mutations on protein folding free energy using a knowledge-modified MM/PBSA approach. Int. J. Mol. Sci. 17:512. doi: 10.3390/ijms17040512

PubMed Abstract | CrossRef Full Text | Google Scholar

Ghosh, S., Jana, J., Kar, R. K., Chatterjee, S., and Dasgupta, D. (2015). Plant alkaloid chelerythrine induced aggregation of human telomere sequence-A unique mode of association between a small molecule and a quadruplex. Biochemistry 54, 974–986. doi: 10.1021/bi501117x

PubMed Abstract | CrossRef Full Text | Google Scholar

Gilson, M. K. (1995). Theory of electrostatic interactions in macromolecules. Curr. Opin. Struct. Biol. 5, 216–223. doi: 10.1016/0959-440X(95)80079-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Gohlke, H., and Case, D. A. (2004). Converging free energy estimates: MM-PB(GB)SA studies on the protein-protein complex Ras-Raf. J. Comput. Chem. 25, 238–250. doi: 10.1002/jcc.10379

PubMed Abstract | CrossRef Full Text | Google Scholar

Gonzalez-Andrade, M., Rodriguez-Sotres, R., Madariaga-Mazon, A., Rivera-Chavez, J., Mata, R., Sosa-Peinado, A., et al. (2016). Insights into molecular interactions between CaM and its inhibitors from molecular dynamics simulations and experimental data. J. Biomol. Struct. Dyn. 34, 78–91. doi: 10.1080/07391102.2015.1022225

PubMed Abstract | CrossRef Full Text | Google Scholar

Grauffel, C., Stote, R. H., and Dejaegere, A. (2015). Molecular dynamics for computational proteomics of methylated histone H3. Biochim. Biophys. Acta 1850, 1026–1040. doi: 10.1016/j.bbagen.2014.09.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Greene, D., Botello-Smith, W. M., Follmer, A., Xiao, L., Lambros, E., and Luo, R. (2016). Modeling membrane protein-ligand binding interactions: the human purinergic platelet receptor. J. Phys. Chem. B 120, 12293–12304. doi: 10.1021/acs.jpcb.6b09535

PubMed Abstract | CrossRef Full Text | Google Scholar

Grewal, B. K., and Sobhia, M. E. (2014). Scaffold hopping for identification of novel PKC beta II inhibitors based on ligand and structural approaches, virtual screening and molecular dynamics study. Comb. Chem. High Throughput Screen. 17, 2–11. doi: 10.2174/1386207311301010008

CrossRef Full Text | Google Scholar

Gu, Y. L., Wang, W., Zhu, X. L., and Dong, K. K. (2014). Molecular dynamic simulations reveal the mechanism of binding between xanthine inhibitors and DPP-4. J. Mol. Model. 20:2075. doi: 10.1007/s00894-014-2075-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Guan, S. S., Han, W. W., Zhang, H., Wang, S., and Shan, Y. M. (2016). Insight into the interactive residues between two domains of human somatic Angiotensin-converting enzyme and Angiotensin II by MM-PBSA calculation and steered molecular dynamics simulation. J. Biomol. Struct. Dyn. 34, 15–28. doi: 10.1080/07391102.2015.1007167

PubMed Abstract | CrossRef Full Text | Google Scholar

Guruge, A. G., Udawatte, C., and Weerasinghe, S. (2016). An in silico approach of coumarin-derived inhibitors for human DNA topoisomerase I. Aust. J. Chem. 69, 1005–1015. doi: 10.1071/CH16232

CrossRef Full Text | Google Scholar

Haghshenas, H., Kay, M., Dehghanian, F., and Tavakol, H. (2016). Molecular dynamics study of biodegradation of azo dyes via their interactions with AzrC azoreductase. J. Biomol. Struct. Dyn. 34, 453–462. doi: 10.1080/07391102.2015.1039585

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, D., Su, M., Tan, J. J., Li, C. H., Zhang, X. Y., and Wang, C. X. (2016). Structure-activity relationship and binding mode studies for a series of diketo-acids as HIV integrase inhibitors by 3D-QSAR, molecular docking and molecular dynamics simulations. RSC Adv. 6, 27594–27606. doi: 10.1039/C6RA00713A

CrossRef Full Text | Google Scholar

Head, M. S., Given, J. A., and Gilson, M. K. (1997). ”Mining minima”: direct computation of conformational free energy. J. Phys. Chem. A 101, 1609–1618. doi: 10.1021/jp963817g

CrossRef Full Text | Google Scholar

Henriksen, N. M., Hayatshahi, H. S., Davis, D. R., and Cheatham, T. E. (2014). Structural and energetic analysis of 2-Aminobenzimidazole inhibitors in complex with the Hepatitis C virus IRES RNA using molecular dynamics simulations. J. Chem. Inf. Model. 54, 1758–1772. doi: 10.1021/ci500132c

PubMed Abstract | CrossRef Full Text | Google Scholar

Homeyer, N., and Gohlke, H. (2015). Extension of the free energy workflow FEW towards implicit solvent/implicit membrane MM-PBSA calculations. Biochim. Biophys. Acta 1850, 972–982. doi: 10.1016/j.bbagen.2014.10.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Homeyer, N., Ioannidis, H., Kolarov, F., Gauglitz, G., Zikos, C., Kolocouris, A., et al. (2016). Interpreting thermodynamic profiles of aminoadamantane compounds inhibiting the M2 proton channel of Influenza A by free energy calculations. J. Chem. Inf. Model. 56, 110–126. doi: 10.1021/acs.jcim.5b00467

PubMed Abstract | CrossRef Full Text | Google Scholar

Hong, W., Li, J. Y., Laughton, C. A., Yap, L. F., Paterson, I. C., and Wang, H. (2014). Investigating the binding preferences of small molecule inhibitors of human protein arginine methyltransferase 1 using molecular modelling. J. Mol. Graph. Model. 51, 193–202. doi: 10.1016/j.jmgm.2014.05.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Honig, B., and Nicholls, A. (1995). Classical electrostatics in biology and chemistry. Science 268, 1144–1149. doi: 10.1126/science.7761829

PubMed Abstract | CrossRef Full Text | Google Scholar

Hsieh, M. J., and Luo, R. (2004). Physical scoring function based on AMBER force field and Poisson-Boltzmann implicit solvent for protein structure prediction. Proteins 56, 475–486. doi: 10.1002/prot.20133

PubMed Abstract | CrossRef Full Text | Google Scholar

Hsieh, M. J., and Luo, R. (2011). Exploring a coarse-grained distributive strategy for finite-difference Poisson-Boltzmann calculations. J. Mol. Model. 17, 1985–1996. doi: 10.1007/s00894-010-0904-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, G. D., Ma, A. J., and Wang, J. H. (2017). Ligand selectivity mechanism and conformational changes in guanine riboswitch by molecular dynamics simulations and free energy calculations. J. Chem. Inf. Model. 57, 918–928. doi: 10.1021/acs.jcim.7b00139

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, G. D., Ma, A. J., Dou, X. H., Zhao, L. L., and Wang, J. H. (2016). Computational studies of a mechanism for binding and drug resistance in the wild type and four mutations of HIV-1 protease with a GRL-0519 inhibitor. Int. J. Mol. Sci. 17:819. doi: 10.3390/ijms17060819

PubMed Abstract | CrossRef Full Text | Google Scholar

Hummer, G. (1999). Hydrophobic force field as a molecular alternative to surface-area models. J. Am. Chem. Soc. 121, 6299–6305. doi: 10.1021/ja984414s

CrossRef Full Text | Google Scholar

Ishida, H., and Matsumoto, A. (2016). Mechanism for verification of mismatched and homoduplex DNAs by nucleotides-bound MutS analyzed by molecular dynamics simulations. Proteins 84, 1287–1303. doi: 10.1002/prot.25077

PubMed Abstract | CrossRef Full Text | Google Scholar

Islam, B., Stadlbauer, P., Neidle, S., Haider, S., and Sponer, J. (2016). Can we execute reliable MM-PBSA free energy computations of relative stabilities of different guanine quadruplex folds? J. Phys. Chem. B 120, 2899–2912. doi: 10.1021/acs.jpcb.6b01059

PubMed Abstract | CrossRef Full Text | Google Scholar

Ivanisenko, N. V., Tregubchak, T. V., Saik, O. V., Ivanisenko, V. A., and Shchelkunov, S. N. (2014). Exploring interaction of TNF and orthopoxviral CrmB protein by surface plasmon resonance and free energy calculation. Protein Pept. Lett. 21, 1273–1281.

PubMed Abstract | Google Scholar

Izadyar, M., Khavani, M., and Housaindokht, M. R. (2015). A combined molecular dynamic and quantum mechanic study of the solvent and guest molecule effect on the stability and length of heterocyclic peptide nanotubes. Phys. Chem. Chem. Phys. 17, 11382–11391. doi: 10.1039/C5CP00973A

PubMed Abstract | CrossRef Full Text | Google Scholar

Jalili, S., and Maddah, M. (2017). Molecular dynamics simulation of the sliding of distamycin anticancer drug along DNA: interactions and sequence selectivity. J. Iran. Chem. Soc. 14, 531–540. doi: 10.1007/s13738-016-1001-0

CrossRef Full Text | Google Scholar

Jalili, S., Maddah, M., and Schofield, J. (2016). Molecular dynamics simulation and free energy analysis of the interaction of platinum-based anti-cancer drugs with DNA. J. Theor. Comput. Chem. 15:1650054. doi: 10.1142/S0219633616500541

CrossRef Full Text | Google Scholar

Jamshidi, S., Rafii-Tabar, H., and Jalili, S. (2014). Investigation into mechanism of orotidine 5 '-monophosphate decarboxylase enzyme by MM-PBSA/MM-GBSA and molecular docking. Mol. Simul. 40, 469–476. doi: 10.1080/08927022.2013.819579

CrossRef Full Text | Google Scholar

Jeancharles, A., Nicholls, A., Sharp, K., Honig, B., Tempczyk, A., Hendrickson, T. F., et al. (1991). Electrostatic contributions to solvation energies - comparison of free-energy perturbation and continuum calculations. J. Am. Chem. Soc. 113, 1454–1455. doi: 10.1021/ja00004a079

CrossRef Full Text | Google Scholar

Kamsri, P., Koohatammakun, N., Srisupan, A., Meewong, P., Punkvang, A., Saparpakorn, P., et al. (2014). Rational design of InhA inhibitors in the class of diphenyl ether derivatives as potential anti-tubercular agents using molecular dynamics simulations. SAR QSAR Environ. Res. 25, 473–488. doi: 10.1080/1062936X.2014.898690

PubMed Abstract | CrossRef Full Text | Google Scholar

Kan, W., Fang, F. Q., Chen, L., Wang, R. G., and Deng, Q. G. (2016). Influence of the R823W mutation on the interaction of the ANKS6-ANKS3: insights from molecular dynamics simulation and free energy analysis. J. Biomol. Struct. Dyn. 34, 1113–1122. doi: 10.1080/07391102.2015.1071281

PubMed Abstract | CrossRef Full Text | Google Scholar

Kang, Y. K., Nemethy, G., and Scheraga, H. A. (1987). Free-energies of hydration of solute molecules. 1. Improvement of the hydration shell-model by exact computations of overlapping volumes. J. Phys. Chem. 91, 4105–4109. doi: 10.1021/j100299a032

CrossRef Full Text | Google Scholar

Karaman, B., and Sippl, W. (2015). Docking and binding free energy calculations of sirtuin inhibitors. Eur. J. Med. Chem. 93, 584–598. doi: 10.1016/j.ejmech.2015.02.045

PubMed Abstract | CrossRef Full Text | Google Scholar

Karim, H. A. A., Tayapiwatana, C., Nimmanpipug, P., Zain, S. M., Rahman, N. A., and Lee, V. S. (2015). “Molecular dynamics simulation on designed antibodies of HIV-1 capsid protein (p24),” in Proceedings of the 3rd International Conference on Computation for Science and Technology, ed D. H. Tjahjono (Paris: Atlantis Press), 85–88.

Google Scholar

Kassem, S., Ahmed, M., El-Sheikh, S., and Barakat, K. H. (2015). Entropy in bimolecular simulations: a comprehensive review of atomic fluctuations-based methods. J. Mol. Graph. Model. 62, 105–117. doi: 10.1016/j.jmgm.2015.09.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Khavani, M., Izadyar, M., and Housaindokht, M. R. (2015). Theoretical design of the cyclic lipopeptide nanotube as a molecular channel in the lipid bilayer, molecular dynamics and quantum mechanics approach. Phys. Chem. Chem. Phys. 17, 25536–25549. doi: 10.1039/C5CP03136B

PubMed Abstract | CrossRef Full Text | Google Scholar

Khavani, M., Izadyar, M., and Housaindokht, M. R. (2017). Glucose derivatives substitution and cyclic peptide diameter effects on the stability of the self-assembled cyclic peptide nanotubes; a joint QM/MD study. J. Mol. Graph. Model. 71, 28–39. doi: 10.1016/j.jmgm.2016.10.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, D. H., Im, H., Jee, J. G., Jang, S. B., Yoon, H. J., Kwon, A. R., et al. (2014). beta-Arm flexibility of HU from Staphylococcus aureus dictates the DNA-binding and recognition mechanism. Acta Crystallogr. Sec. D Biol. Crystallogr. 70, 3273–3289. doi: 10.1107/S1399004714023931

PubMed Abstract | CrossRef Full Text | Google Scholar

Kocakaya, S. O. (2014). The molecular modeling of novel inhibitors of protein tyrosine phosphatase 1B based on catechol by MD and MM-GB (PB)/SA calculations. Bull. Korean Chem. Soc. 35, 1769–1776. doi: 10.5012/bkcs.2014.35.6.1769

CrossRef Full Text

Kocakaya, S. O., Turgut, Y., and Pirinccioglu, N. (2015). Enantiomeric discrimination of chiral organic salts by chiral aza-15-crown-5 ether with C-1 symmetry: experimental and theoretical approaches. J. Mol. Model. 21:13. doi: 10.1007/s00894-015-2604-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S. H., Chong, L., et al. (2000). Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res. 33, 889–897. doi: 10.1021/ar000033j

PubMed Abstract | CrossRef Full Text | Google Scholar

Kong, X. T., Pan, P. C., Li, D., Tian, S., Li, Y. Y., and Hou, T. J. (2015). Importance of protein flexibility in ranking inhibitor affinities: modeling the binding mechanisms of piperidine carboxamides as Type I1/2 ALK inhibitors. Phys. Chem. Chem. Phys. 17, 6098–6113. doi: 10.1039/C4CP05440G

PubMed Abstract | CrossRef Full Text | Google Scholar

Kongkaew, S., Yotmanee, P., Rungrotmongkol, T., Kaiyawet, N., Meeprasert, A., Kaburaki, T., et al. (2015). Molecular dynamics simulation reveals the selective binding of human leukocyte antigen alleles associated with BehCet's disease. PLoS ONE 10:e135575. doi: 10.1371/journal.pone.0135575

PubMed Abstract | CrossRef Full Text | Google Scholar

Korman, T. P., Tan, Y.-H., Wong, J., Luo, R., and Tsai, S.-C. (2008). Inhibition kinetics and emodin cocrystal structure of a type II polyketide ketoreductase. Biochemistry 47, 1837–1847. doi: 10.1021/bi7016427

PubMed Abstract | CrossRef Full Text | Google Scholar

Kossmann, B. R., Abdelmalak, M., Lopez, S., Tender, G., Yan, C. L., Pommier, Y., et al. (2016). Discovery of selective inhibitors of tyrosyl-DNA phosphodiesterase 2 by targeting the enzyme DNA-binding cleft. Bioorg. Med. Chem. Lett. 26, 3232–3236. doi: 10.1016/j.bmcl.2016.05.065

PubMed Abstract | CrossRef Full Text | Google Scholar

Koukoulitsa, C., Villalonga-Barber, C., Csonka, R., Alexi, X., Leonis, G., Dellis, D., et al. (2016). Biological and computational evaluation of resveratrol inhibitors against Alzheimer's disease. J. Enzyme Inhib. Med. Chem. 31, 67–77. doi: 10.3109/14756366.2014.1003928

PubMed Abstract | CrossRef Full Text | Google Scholar

Kragh-Hansen, U., Minchiotti, L., Coletta, A., Bienk, K., Galliano, M., Schiott, B., et al. (2016). Mutants and molecular dockings reveal that the primary L-thyroxine binding site in human serum albumin is not the one which can cause familial dysalbuminemic hyperthyroxinemia. Biochim. Biophys. Acta 1860, 648–660. doi: 10.1016/j.bbagen.2016.01.001

CrossRef Full Text | Google Scholar

Kumar, D. T., Doss, C. G. P., Sneha, P., Tayubi, I. A., Siva, R., Chakraborty, C., et al. (2017). Influence of V54M mutation in giant muscle protein titin: a computational screening and molecular dynamics approach. J. Biomol. Struct. Dyn. 35, 917–928. doi: 10.1080/07391102.2016.1166456

CrossRef Full Text | Google Scholar

Kumar, R., Maurya, R., and Saran, S. (2017). Identification of novel inhibitors of the translationally controlled tumor protein (TCTP): insights from molecular dynamics. Mol. Biosyst. 13, 510–524. doi: 10.1039/C6MB00850J

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumari, R., Kumar, R., Lynn, A., and Open Source Drug Discovery, C. (2014). g_mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 54, 1951–1962. doi: 10.1021/ci500020m

PubMed Abstract | CrossRef Full Text | Google Scholar

Kurt, B. Z., Gazioglu, I., Dag, A., Salmas, R. E., Kayik, G., Durdagi, S., et al. (2017). Synthesis, anticholinesterase activity and molecular modeling study of novel carbamate-substituted thymol/carvacrol derivatives. Bioorg. Med. Chem. 25, 1352–1363. doi: 10.1016/j.bmc.2016.12.037

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, J., Jung, S. W., and Cho, A. E. (2016). Molecular insights into the adsorption mechanism of human beta-Defensin-3 on bacterial membranes. Langmuir 32, 1782–1790. doi: 10.1021/acs.langmuir.5b04113

PubMed Abstract | CrossRef Full Text | Google Scholar

Lei, B. L., Liu, J. Y., and Yao, X. J. (2015). Unveiling the molecular mechanism of brassinosteroids: insights from structure-based molecular modeling studies. Steroids 104, 111–117. doi: 10.1016/j.steroids.2015.09.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Leong, P., Amaro, R. E., and Li, W. W. (2015). Molecular dynamics analysis of antibody recognition and escape by human H1N1 influenza hemagglutinin. Biophys. J. 108, 2704–2712. doi: 10.1016/j.bpj.2015.04.025

CrossRef Full Text | Google Scholar

Leonis, G., Avramopoulos, A., Salmas, R. E., Durdagi, S., Yurtsever, M., and Papadopoulos, M. G. (2014). Elucidation of conformational states, dynamics, and mechanism of binding in human kappa-opioid receptor complexes. J. Chem. Inf. Model. 54, 2294–2308. doi: 10.1021/ci5002873

PubMed Abstract | CrossRef Full Text | Google Scholar

Levy, R. M., Zhang, L. Y., Gallicchio, E., and Felts, A. K. (2003). On the nonpolar hydration free energy of proteins: surface area and continuum solvent models for the solute-solvent interaction energy. J. Am. Chem. Soc. 125, 9523–9530. doi: 10.1021/ja029833a

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, D., Zhang, Y., Zhao, R. N., Fan, S., and Han, J. G. (2014). Investigation on the mechanism for the binding and drug resistance of wild type and mutations of G86 residue in HIV-1 protease complexed with Darunavir by molecular dynamic simulation and free energy calculation. J. Mol. Model. 20:2122. doi: 10.1007/s00894-014-2122-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, M. H., Petukh, M., Alexov, E., and Panchenko, A. R. (2014). Predicting the impact of missense mutations on protein-protein binding affinity. J. Chem. Theory Comput. 10, 1770–1780. doi: 10.1021/ct401022c

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, X. L., Wang, X. W., Tian, Z. B., Zhao, H. L., Liang, D., Li, W. S., et al. (2014). Structural basis of valmerins as dual inhibitors of GSK3 beta/CDK5. J. Mol. Model. 20:2407. doi: 10.1007/s00894-014-2407-1

CrossRef Full Text | Google Scholar

Li, Y. Y., Liu, X. D., Dong, X. Y., Zhang, L., and Sun, Y. (2014). Biomimetic design of affinity peptide ligand for capsomere of virus-like particle. Langmuir 30, 8500–8508. doi: 10.1021/la5017438

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Li, X., and Dong, Z. G. (2015). Statistical analysis of EGFR structures' performance in virtual screening. J. Comput. Aided Mol. Des. 29, 1045–1055. doi: 10.1007/s10822-015-9877-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Liao, S. Y., Mo, G. Q., Chen, J. C., and Zheng, K. C. (2014a). Docking and molecular dynamics studies of the binding between Peloruside A and tubulin. J. Enzyme Inhib. Med. Chem. 29, 702–709. doi: 10.3109/14756366.2013.845816

PubMed Abstract | CrossRef Full Text | Google Scholar

Liao, S. Y., Mo, G. Q., Chen, J. C., and Zheng, K. C. (2014b). Exploration of the binding mode between (-)-zampanolide and tubulin using docking and molecular dynamics simulation. J. Mol. Model. 20:2070. doi: 10.1007/s00894-014-2070-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Ling, B. P., Liu, Y. X., Li, X. P., Wang, Z. G., and Bi, S. W. (2016). Identification of the active site of human mitochondrial malonyl-coenzyme a decarboxylase: a combined computational study. Proteins 84, 792–802. doi: 10.1002/prot.25029

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, C. M., Zhu, Y. Y., and Tang, M. S. (2016). Theoretical studies on binding modes of copper-based nucleases with DNA. J. Mol. Graph. Model. 64, 11–29. doi: 10.1016/j.jmgm.2015.12.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, F. J., Zhang, J. Z. H., and Mei, Y. (2016). The origin of the cooperativity in the streptavidin-biotin system: a computational investigation through molecular dynamics simulations. Sci. Rep. 6:27190. doi: 10.1038/srep27190

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, H. L., Han, R., Li, J. Z., Liu, H. X., and Zheng, L. F. (2016). Molecular mechanism of R-bicalutamide switching from androgen receptor antagonist to agonist induced by amino acid mutations using molecular dynamics simulations and free energy calculation. J. Comput. Aided Mol. Des. 30, 1189–1200. doi: 10.1007/s10822-016-9992-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, H. M., Chen, L. C., Li, Q., Zheng, M. Z., and Liu, J. S. (2014). Computational study on substrate specificity of a novel cysteine Protease 1 precursor from Zea mays. Int. J. Mol. Sci. 15, 10459–10478. doi: 10.3390/ijms150610459

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, H., and Hou, T. J. (2016). CaFE: a tool for binding affinity prediction using end-point free energy methods. Bioinformatics 32, 2216–2218. doi: 10.1093/bioinformatics/btw215

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, X., Liu, J. F., Zhu, T., Zhang, L. J., He, X., and Zhang, J. Z. H. (2016). PBSA_E: a PBSA-based free energy estimator for protein-ligand binding affinity. J. Chem. Inf. Model. 56, 854–861. doi: 10.1021/acs.jcim.6b00001

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, X., Wang, C., Wang, J., Li, Z., Zhao, H., and Luo, R. (2013). Exploring a charge-central strategy in the solution of Poisson's equation for biomolecular applications. Phys. Chem. Chem. Phys. 15, 129–141. doi: 10.1039/C2CP41894K

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, H. T., Huang, X. Q., Abdulhameed, M. D. M., and Zhan, C. G. (2014). Binding free energies for nicotine analogs inhibiting cytochrome P450 2A6 by a combined use of molecular dynamics simulations and QM/MM-PBSA calculations. Bioorg. Med. Chem. 22, 2149–2156. doi: 10.1016/j.bmc.2014.02.037

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, J., Zhang, Z. Y., Ni, Z., Shen, H. J., Tu, Z. G., Liu, H. Q., et al. (2015). The non-additive contribution of hydroxyl substituents to Akt kinase-apigenin affinity. Mol. Simul. 41, 653–662. doi: 10.1080/08927022.2014.913099

CrossRef Full Text | Google Scholar

Lu, Q., and Luo, R. (2003). A Poisson-Boltzmann dynamics method with nonperiodic boundary condition. J. Chem. Phys. 119, 11035–11047. doi: 10.1063/1.1622376

CrossRef Full Text | Google Scholar

Lum, K., Chandler, D., and Weeks, J. D. (1999). Hydrophobicity at small and large length scales. J. Phys. Chem. B 103, 4570–4577. doi: 10.1021/jp984327m

CrossRef Full Text | Google Scholar

Luo, R., and Gilson, M. K. (2000). Synthetic adenine receptors: direct calculation of binding affinity and entropy. J. Am. Chem. Soc. 122, 2934–2937. doi: 10.1021/ja994034m

CrossRef Full Text | Google Scholar

Luo, R., David, L., and Gilson, M. K. (2002). Accelerated Poisson-Boltzmann calculations for static and dynamic systems. J. Comput. Chem. 23, 1244–1253. doi: 10.1002/jcc.10120

PubMed Abstract | CrossRef Full Text | Google Scholar

Luo, R., David, L., Hung, H., Devaney, J., and Gilson, M. K. (1999). Strength of solvent-exposed salt-bridges. J. Phys. Chem. B 103, 727–736. doi: 10.1021/jp982715i

CrossRef Full Text | Google Scholar

Luo, R., Gilson, H. S. R., Potter, M. J., and Gilson, M. K. (2001). The physical basis of nucleic acid base stacking in water. Biophys. J. 80, 140–148. doi: 10.1016/S0006-3495(01)76001-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Luo, R., Head, M. S., Moult, J., and Gilson, M. K. (1998). pK(a) shifts in small molecules and HIV protease: electrostatics and conformation. J. Am. Chem. Soc. 120, 6138–6146. doi: 10.1021/ja974307i

CrossRef Full Text | Google Scholar

Luo, R., Moult, J., and Gilson, M. K. (1997). Dielectric screening treatment of electrostatic solvation. J. Phys. Chem. B 101, 11226–11236. doi: 10.1021/jp9724838

CrossRef Full Text | Google Scholar

Lwin, T. Z., and Luo, R. (2005). Overcoming entropic barrier with coupled sampling at dual resolutions. J. Chem. Phys. 123:194904. doi: 10.1063/1.2102871

PubMed Abstract | CrossRef Full Text | Google Scholar

Lwin, T. Z., and Luo, R. (2006). Force field influences in β-hairpin folding simulations. Protein Sci. 15, 2642–2655. doi: 10.1110/ps.062438006

PubMed Abstract | CrossRef Full Text | Google Scholar

Lwin, T. Z., Zhou, R. H., and Luo, R. (2006). Is Poisson-Boltzmann theory insufficient for protein folding simulations? J. Chem. Phys. 124. doi: 10.1063/1.2161202

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, S. J., Zeng, G. H., Fang, D. Q., Wang, J. P., Wu, W. J., Xie, W. G., et al. (2015). Studies of N-9-arenthenyl purines as novel DFG-in and DFG-out dual Src/Abl inhibitors using 3D-QSAR, docking and molecular dynamics simulations. Mol. Biosyst. 11, 394–406. doi: 10.1039/C4MB00350K

PubMed Abstract | CrossRef Full Text | Google Scholar

Malhis, L. D., Bodoor, K., Assaf, K. I., Al-Sakhen, N. A., and El-Barghouthi, M. I. (2015). Molecular dynamics simulation of a cucurbituril based molecular switch triggered by pH changes. Comput. Theor. Chem. 1066, 104–112. doi: 10.1016/j.comptc.2015.05.010

CrossRef Full Text | Google Scholar

Marsavelski, A., and Vianello, R. (2017). What a difference a methyl group makes: the selectivity of monoamine oxidase B towards histamine and N-Methylhistamine. Chemistry 23, 2915–2925. doi: 10.1002/chem.201605430

PubMed Abstract | CrossRef Full Text | Google Scholar

Marshall, S. A., Vizcarra, C. L., and Mayo, S. L. (2005). One- and two-body decomposable Poisson-Boltzmann methods for protein design calculations. Protein Sci. 14, 1293–1304. doi: 10.1110/ps.041259105

PubMed Abstract | CrossRef Full Text | Google Scholar

Meeprasert, A., Hannongbua, S., Kungwan, N., and Rungrotmongkol, T. (2016). Effect of D168V mutation in NS3/4A HCV protease on susceptibilities of faldaprevir and danoprevir. Mol. Biosyst. 12, 3666–3673. doi: 10.1039/C6MB00610H

PubMed Abstract | CrossRef Full Text | Google Scholar

Meher, B. R., and Wang, Y. X. (2015). Exploring the drug resistance of V32I and M46L mutant HIV-1 protease to inhibitor TMC114: flap dynamics and binding mechanism. J. Mol. Graph. Model. 56, 60–73. doi: 10.1016/j.jmgm.2014.11.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Mikulskis, P., Genheden, S., Rydberg, P., Sandberg, L., Olsen, L., and Ryde, U. (2012). Binding affinities in the SAMPL3 trypsin and host-guest blind tests estimated with the MM/PBSA and LIE methods. J. Comput. Aided Mol. Des. 26, 527–541. doi: 10.1007/s10822-011-9524-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Miller, B. R., Mcgee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., and Roitberg, A. E. (2012). MMPBSA.py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput. 8, 3314–3321. doi: 10.1021/ct300418h

PubMed Abstract | CrossRef Full Text | Google Scholar

Moghaddam, S., Yang, C., Rekharsky, M., Ko, Y. H., Kim, K., Inoue, Y., et al. (2011). New ultrahigh affinity host-guest complexes of cucurbit[7]uril with bicyclo[2.2.2]octane and adamantane guests: thermodynamic analysis and evaluation of M2 affinity calculations. J. Am. Chem. Soc. 133, 3570–3581. doi: 10.1021/ja109904u

PubMed Abstract | CrossRef Full Text | Google Scholar

Mokmak, W., Chunsrivirot, S., Hannongbua, S., Yuthavong, Y., Tongsima, S., and Kamchonwongpaisan, S. (2014). Molecular dynamics of interactions between rigid and flexible antifolates and dihydrofolate reductase from pyrimethamine-sensitive and pyrimethamine-resistant Plasmodium falciparum. Chem. Biol. Drug Des. 84, 450–461. doi: 10.1111/cbdd.12334

PubMed Abstract | CrossRef Full Text | Google Scholar

Moonrin, N., Songtawee, N., Rattanabunyong, S., Chunsrivirot, S., Mokmak, W., Tongsima, S., et al. (2015). Understanding the molecular basis of EGFR kinase domain/MIG-6 peptide recognition complex using computational analyses. BMC Bioinformatics 16:103. doi: 10.1186/s12859-015-0528-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Moreira, C., Ramos, M. J., and Fernandes, P. A. (2016). Glutamine synthetase drugability beyond its active site: exploring oligomerization interfaces and pockets. Molecules 21:e1028. doi: 10.3390/molecules21081028

PubMed Abstract | CrossRef Full Text | Google Scholar

Muddana, H. S., and Gilson, M. K. (2012). Calculation of host-guest binding affinities using a quantum-mechanical energy model. J. Chem. Theory Comput. 8, 2023–2033. doi: 10.1021/ct3002738

PubMed Abstract | CrossRef Full Text | Google Scholar

Muddana, H. S., Yin, J., Sapra, N. V., Fenley, A. T., and Gilson, M. K. (2014). Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method. J. Comput. Aided Mol. Des. 28, 463–474. doi: 10.1007/s10822-014-9726-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Muvva, C., Singam, E. R. A., Raman, S. S., and Subramanian, V. (2014). Structure-based virtual screening of novel, high-affinity BRD4 inhibitors. Mol. Biosyst. 10, 2384–2397. doi: 10.1039/C4MB00243A

PubMed Abstract | CrossRef Full Text | Google Scholar

Nagamani, S., Muthusamy, K., and Marshal, J. J. (2016). E-pharmacophore filtering and molecular dynamics simulation studies in the discovery of potent drug-like molecules for chronic kidney disease. J. Biomol. Struct. Dyn. 34, 2233–2250. doi: 10.1080/07391102.2015.1111168

PubMed Abstract | CrossRef Full Text | Google Scholar

Nantermet, P. G., Barrow, J. C., Newton, C. L., Pellicore, J. M., Young, M. B., Lewis, S. D., et al. (2003). Design and synthesis of potent and selective macrocyclic thrombin inhibitors. Bioorg. Med. Chem. Lett. 13, 2781–2784. doi: 10.1016/S0960-894X(03)00506-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Nguyen, T. T., Tran, D. P., Huy, P. D. Q., Hoang, Z., Carloni, P., Pham, P. V., et al. (2016). Ligand binding to anti-cancer target CD44 investigated by molecular simulations. J. Mol. Model. 22:165. doi: 10.1007/s00894-016-3029-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Ni, G. Y., Wang, Y. J., Cummins, S., Walton, S., Mounsey, K., Liu, X. S., et al. (2017). Inhibitory mechanism of peptides with a repeating hydrophobic and hydrophilic residue pattern on interleukin-10. Hum. Vaccin. Immunother. 13, 518–527. doi: 10.1080/21645515.2016.1238537

PubMed Abstract | CrossRef Full Text | Google Scholar

Nicholls, A., Mobley, D. L., Guthrie, J. P., Chodera, J. D., Bayly, C. I., Cooper, M. D., et al. (2008). Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J. Med. Chem. 51, 769–779. doi: 10.1021/jm070549+

PubMed Abstract | CrossRef Full Text | Google Scholar

Nielsen, J. E., and McCammon, J. A. (2003). On the evaluation and optimization of protein X-ray structures for pKa calculations. Protein Sci. 12, 313–326. doi: 10.1110/ps.0229903

PubMed Abstract | CrossRef Full Text | Google Scholar

Ntie-Kang, F., Kannan, S., Wichapong, K., Owono, L. C. O., Sippl, W., and Megnassan, E. (2014). Binding of pyrazole-based inhibitors to Mycobacterium tuberculosis pantothenate synthetase: docking and MM-GB(PB)SA analysis. Mol. Biosyst. 10, 223–239. doi: 10.1039/C3MB70449A

PubMed Abstract | CrossRef Full Text | Google Scholar

Obiol-Pardo, C., Alcarraz-Vizan, G., Diaz-Moralli, S., Cascante, M., and Rubio-Martinez, J. (2014). Design of an interface peptide as new inhibitor of human glucose-6-phosphate dehydrogenase. J. Mol. Graph. Model. 49, 110–117. doi: 10.1016/j.jmgm.2014.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Odoux, A., Jindal, D., Tamas, T. C., Lim, B. W. H., Pollard, D., and Xu, W. (2016). Experimental and molecular dynamics studies showed that CBP KIX mutation affects the stability of CBP:c-Myb complex. Comput. Biol. Chem. 62, 47–59. doi: 10.1016/j.compbiolchem.2016.03.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Omotuyi, I. O. (2015). Ebola virus envelope glycoprotein derived peptide in human Furin-bound state: computational studies. J. Biomol. Struct. Dyn. 33, 461–470. doi: 10.1080/07391102.2014.981207

PubMed Abstract | CrossRef Full Text | Google Scholar

Omotuyi, O. I. (2014). Methyl-methoxylpyrrolinone and flavinium nucleus binding signatures on falcipain-2 active site. J. Mol. Model. 20:2386. doi: 10.1007/s00894-014-2386-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Paissoni, C., Spiliotopoulos, D., Musco, G., and Spitaleri, A. (2014). GMXPBSA 2.0: a GROMACS tool to perform MM/PBSA and computational alanine scanning. Comput. Phys. Commun. 185, 2920–2929. doi: 10.1016/j.cpc.2014.06.019

CrossRef Full Text | Google Scholar

Parasuraman, P., Murugan, V., Selvin, J. F. A., Gromiha, M. M., Fukui, K., and Veluraja, K. (2014). Insights into the binding specificity of wild type and mutated wheat germ agglutinin towards Neu5Ac alpha(2-3)Gal: a study by in silico mutations and molecular dynamics simulations. J. Mol. Recognit. 27, 482–492. doi: 10.1002/jmr.2369

CrossRef Full Text | Google Scholar

Passos, C. D., Simoes-Pires, C. A., Carrupt, P. A., and Nurisso, A. (2016). Molecular dynamics of zinc-finger ubiquitin binding domains: a comparative study of histone deacetylase 6 and ubiquitin-specific protease 5. J. Biomol. Struct. Dyn. 34, 2581–2598. doi: 10.1080/07391102.2015.1124051

CrossRef Full Text | Google Scholar

Patra, M. C., Rath, S. N., Pradhan, S. K., Maharana, J., and De, S. (2014). Molecular dynamics simulation of human serum paraoxonase 1 in DPPC bilayer reveals a critical role of transmembrane helix H1 for HDL association. Eur. Biophys. J. Biophys. Lett. 43, 35–51. doi: 10.1007/s00249-013-0937-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Perutz, M. F. (1978). Electrostatic effects in proteins. Science 201, 1187–1191. doi: 10.1126/science.694508

PubMed Abstract | CrossRef Full Text | Google Scholar

Pethe, M. A., Rubenstein, A. B., and Khare, S. D. (2017). Large-scale structure-based prediction and identification of novel protease substrates using computational protein design. J. Mol. Biol. 429, 220–236. doi: 10.1016/j.jmb.2016.11.031

PubMed Abstract | CrossRef Full Text | Google Scholar

Petukh, M., Dai, L. G., and Alexov, E. (2016). SAAMBE: Webserver to predict the charge of binding free energy caused by amino acids mutations. Int. J. Mol. Sci. 17:547. doi: 10.3390/ijms17040547

PubMed Abstract | CrossRef Full Text | Google Scholar

Petukh, M., Li, M. H., and Alexov, E. (2015). Predicting binding free energy change caused by point mutations with knowledge-modified MM/PBSA method. PLoS Comput. Biol. 11:e4276. doi: 10.1371/journal.pcbi.1004276

PubMed Abstract | CrossRef Full Text | Google Scholar

Phanich, J., Rungrotmongkol, T., Kungwan, N., and Hannongbua, S. (2016). Role of R292K mutation in influenza H7N9 neuraminidase toward oseltamivir susceptibility: MD and MM/PB(GB)SA study. J. Comput. Aided Mol. Des. 30, 917–926. doi: 10.1007/s10822-016-9981-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Platania, C. B. M., Di Paola, L., Leggio, G. M., Romano, G. L., Drago, F., Salomone, S., et al. (2015). Molecular features of interaction between VEGFA and anti-angiogenic drugs used in retinal diseases: a computational approach. Front. Pharmacol. 6:248. doi: 10.3389/fphar.2015.00248

PubMed Abstract | CrossRef Full Text | Google Scholar

Poongavanam, V., Kongsted, J., and Wustner, D. (2016). Computational analysis of sterol ligand specificity of the niemann pick C2 protein. Biochemistry 55, 5165–5179. doi: 10.1021/acs.biochem.6b00217

PubMed Abstract | CrossRef Full Text | Google Scholar

Pratt, L. R., and Chandler, D. (1977). Theory of hydrophobic effect. J. Chem. Phys. 67, 3683–3704. doi: 10.1063/1.435308

CrossRef Full Text | Google Scholar

Pratt, L. R., and Chandler, D. (1980). Effects of solute-solvent attractive forces on hydrophobic correlations. J. Chem. Phys. 73, 3434–3441. doi: 10.1063/1.440541

CrossRef Full Text | Google Scholar

Qi, R., Botello-Smith, W. M., and Luo, R. (2017). Acceleration of linear finite-difference Poisson-Boltzmann methods on graphics processing units. J. Chem. Theory Comput. 13, 3378–3387. doi: 10.1021/acs.jctc.7b00336

PubMed Abstract | CrossRef Full Text | Google Scholar

Qi, Z. T., Meng, F. C., Zhang, Q. H., Wang, Z. S., Qiao, G., Xu, W., et al. (2017). Structural insights into ligand binding of PGRP1 splice variants in Chinese giant salamander (Andrias davidianus) from molecular dynamics and free energy calculations. J. Mol. Model. 23:315. doi: 10.1007/s00894-017-3315-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Qian, H. Y., Chen, J. J., Pan, Y. L., and Chen, J. Z. (2016). Molecular modeling studies of 11 beta-Hydroxysteroid dehydrogenase type 1 inhibitors through receptor-based 3D-QSAR and molecular dynamics simulations. Molecules 21:e1222. doi: 10.3390/molecules21091222

CrossRef Full Text | Google Scholar

Qian, M. D., Guan, S. S., Shan, Y. M., Zhang, H., and Wang, S. (2016). Structural and molecular basis of cellulase Cel48F by computational modeling: insight into catalytic and product release mechanism. J. Struct. Biol. 194, 347–356. doi: 10.1016/j.jsb.2016.03.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Quevedo, M. A., Ribone, S. R., Brinon, M. C., and Dehaen, W. (2014). Development of a receptor model for efficient in silico screening of HIV-1 integrase inhibitors. J. Mol. Graph. Model. 52, 82–90. doi: 10.1016/j.jmgm.2014.06.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Ren, W., Truong, T. M., and Ai, H. W. (2015). Study of the binding energies between unnatural amino acids and engineered orthogonal tyrosyl-tRNA synthetases. Sci. Rep. 5:12632. doi: 10.1038/srep12632

PubMed Abstract | CrossRef Full Text | Google Scholar

Roy, D., Kumar, V., James, J., Shihabudeen, M. S., Kulshrestha, S., Goel, V., et al. (2015). Evidence that chemical Chaperone 4-Phenylbutyric acid binds to human serum albumin at fatty acid binding sites. PLoS ONE 10:e133012. doi: 10.1371/journal.pone.0133012

PubMed Abstract | CrossRef Full Text | Google Scholar

Sa, R. J., Fang, L., Huang, M. D., Li, Q. H., Wei, Y. Q., and Wu, K. C. (2014). Evaluation of interactions between urokinase plasminogen and inhibitors using molecular dynamic simulation and free-energy calculation. J. Phys. Chem. A 118, 9113–9119. doi: 10.1021/jp5064319

PubMed Abstract | CrossRef Full Text | Google Scholar

Sahoo, B. R., and Fujiwara, T. (2017). Conformational states of HAMP domains interacting with sensory rhodopsin membrane systems: an integrated all-atom and coarse-grained molecular dynamics simulation approach. Mol. Biosyst. 13, 193–207. doi: 10.1039/C6MB00730A

PubMed Abstract | CrossRef Full Text | Google Scholar

Sahoo, B. R., Dikhit, M. R., Bhoi, G. K., Maharana, J., Lenka, S. K., Dubey, P. K., et al. (2015). Understanding the distinguishable structural and functional features in zebrafish TLR3 and TLR22, and their binding modes with fish dsRNA viruses: an exploratory structural model analysis. Amino Acids 47, 381–400. doi: 10.1007/s00726-014-1872-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Sahoo, B. R., Dubey, P. K., Goyal, S., Bhoi, G. K., Lenka, S. K., Maharana, J., et al. (2014a). Exploration of the binding modes of buffalo PGRP1 receptor complexed with meso-diaminopimelic acid and lysine-type peptidoglycans by molecular dynamics simulation and free energy calculation. Chem. Biol. Interact. 220, 255–268. doi: 10.1016/j.cbi.2014.06.028

PubMed Abstract | CrossRef Full Text | Google Scholar

Sahoo, B. R., Maharana, J., Bhoi, G. K., Lenka, S. K., Patra, M. C., Dikhit, M. R., et al. (2014b). A conformational analysis of mouse Nalp3 domain structures by molecular dynamics simulations, and binding site analysis. Mol. Biosyst. 10, 1104–1116. doi: 10.1039/C3MB70600A

PubMed Abstract | CrossRef Full Text | Google Scholar

Sahoo, B. R., Maharana, J., Patra, M. C., Bhoi, G. K., Lenka, S. K., Dubey, P. K., et al. (2014c). Structural and dynamic investigation of bovine folate receptor alpha (FOLR1), and role of ultra-high temperature processing on conformational and thermodynamic characteristics of FOLR1-folate complex. Colloids Surf. B Biointerfaces 121, 307–318. doi: 10.1016/j.colsurfb.2014.05.028

PubMed Abstract | CrossRef Full Text | Google Scholar

Sain, N., Tiwari, G., and Mohanty, D. (2016). Understanding the molecular basis of substrate binding specificity of PTB domains. Sci. Rep. 6:31418. doi: 10.1038/srep31418

PubMed Abstract | CrossRef Full Text | Google Scholar

Salmas, R. E., Seeman, P., Aksoydan, B., Stein, M., Yurtsever, M., and Durdagi, S. (2017). Biological Insights of the dopaminergic stabilizer ACR16 at the binding pocket of dopamine D2 receptor. ACS Chem. Neurosci. 8, 826–836. doi: 10.1021/acschemneuro.6b00396

CrossRef Full Text | Google Scholar

Sang, P., Xie, Y. H., Li, L. H., Ye, Y. J., Hu, W., Wang, J., et al. (2017). Effect of the R119G mutation on human P5CR structure and its interactions with NAD: insights derived from molecular dynamics simulation and free energy analysis. Comput. Biol. Chem. 67, 141–149. doi: 10.1016/j.compbiolchem.2016.12.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Sangpheak, W., Khuntawee, W., Wolschann, P., Pongsawasdi, P., and Rungrotmongkol, T. (2014). Enhanced stability of a naringenin/2,6-dimethyl beta-cyclodextrin inclusion complex: molecular dynamics and free energy calculations based on MM- and QM-PBSA/GBSA. J. Mol. Graph. Model. 50, 10–15. doi: 10.1016/j.jmgm.2014.03.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Santos, G., Giraldez-Alvarez, L. D., Avila-Rodriguez, M., Capani, F., Galembeck, E., Neto, A. G., et al. (2016). SUR1 receptor interaction with hesperidin and linarin predicts possible mechanisms of action of valeriana officinalis in Parkinson. Front. Aging Neurosci. 8:97. doi: 10.3389/fnagi.2016.00097

PubMed Abstract | CrossRef Full Text | Google Scholar

Santoshi, S., and Naik, P. K. (2014). Molecular insight of isotypes specific beta-tubulin interaction of tubulin heterodimer with noscapinoids. J. Comput. Aided Mol. Des. 28, 751–763. doi: 10.1007/s10822-014-9756-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Sarvagalla, S., Cheung, C. H. A., Tsai, J. Y., Hsieh, H. P., and Coumar, M. S. (2016). Disruption of protein-protein interactions: hot spot detection, structure-based virtual screening and in vitro testing for the anti-cancer drug target survivin. RSC Adv. 6, 31947–31959. doi: 10.1039/C5RA22927H

CrossRef Full Text | Google Scholar

Schneider, M., Rosam, M., Glaser, M., Patronov, A., Shah, H., Back, K. C., et al. (2016). BiPPred: combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP. Proteins 84, 1390–1407. doi: 10.1002/prot.25084

PubMed Abstract | CrossRef Full Text | Google Scholar

Shao, H., Xu, L., and Yan, Y. J. (2014). Biochemical characterization of a carboxylesterase from the Archaeon Pyrobaculum sp. 1860 and a rational explanation of its substrate specificity and thermostability. Int. J. Mol. Sci. 15, 16885–16910. doi: 10.3390/ijms150916885

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, P. C., Li, W. W., Wang, Y., He, X., and He, L. Q. (2016). Binding mode of chitin and TLR2 via molecular docking and dynamics simulation. Mol. Simul. 42, 936–941. doi: 10.1080/08927022.2015.1124102

CrossRef Full Text | Google Scholar

Shi, S. H., Zhang, S. L., and Zhang, Q. G. (2015). Probing difference in binding modes of inhibitors to MDMX by molecular dynamics simulations and different free energy methods. PLoS ONE 10:e0141409. doi: 10.1371/journal.pone.0141409

PubMed Abstract | CrossRef Full Text | Google Scholar

Shivakumar, D., Deng, Y. Q., and Roux, B. (2009). Computations of absolute solvation free energies of small molecules using explicit and implicit solvent model. J. Chem. Theory Comput. 5, 919–930. doi: 10.1021/ct800445x

PubMed Abstract | CrossRef Full Text | Google Scholar

Sim, S., Wang, P., Beyer, B. N., Cutrona, K. J., Radhakrishnan, M. L., and Elmore, D. E. (2017). Investigating the nucleic acid interactions of histone-derived antimicrobial peptides. FEBS Lett. 591, 706–717. doi: 10.1002/1873-3468.12574

PubMed Abstract | CrossRef Full Text | Google Scholar

Simoes, I. C. M., Costa, I. P. D., Coimbra, J. T. S., Ramos, M. J., and Fernandes, P. A. (2017). New parameters for higher accuracy in the computation of binding free energy differences upon alanine scanning mutagenesis on protein-protein interfaces. J. Chem. Inf. Model. 57, 60–72. doi: 10.1021/acs.jcim.6b00378

PubMed Abstract | CrossRef Full Text | Google Scholar

Sinha, S., Goyal, S., Somvanshi, P., and Grover, A. (2017). Mechanistic insights into the binding of class IIa HDAC Inhibitors toward spinocerebellar ataxia type-2: A 3D-QSAR and pharmacophore modeling approach. Front. Neurosci. 10:606. doi: 10.3389/fnins.2016.00606

PubMed Abstract | CrossRef Full Text | Google Scholar

Siqueira, A. S., Lima, A. R. J., Dall'agnol, L. T., De Azevedo, J. S. N., Vianez, J., and Goncalves, E. C. (2016). Comparative modeling and molecular dynamics suggest high carboxylase activity of the Cyanobium sp CACIAM14 RbcL protein. J. Mol. Model. 22:68. doi: 10.1007/s00894-016-2943-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Slynko, I., Scharfe, M., Rumpf, T., Eib, J., Metzger, E., Schule, R., et al. (2014). Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development. J. Chem. Inf. Model. 54, 138–150. doi: 10.1021/ci400628q

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, R., and Tanford, C. (1973). Hydrophobicity of long-chain alkyl carboxylic-acids, as measured by their distribution between heptane and aqueous-solutions. Proc. Natl. Acad. Sci. U.S.A. 70, 289–293. doi: 10.1073/pnas.70.2.289

PubMed Abstract | CrossRef Full Text | Google Scholar

Smolko, A., Supljika, F., Martincic, J., Jajcanin-Jozic, N., Grabar-Branilovic, M., Tomic, S., et al. (2016). The role of conserved Cys residues in Brassica rapa auxin amidohydrolase: Cys139 is crucial for the enzyme activity and Cys320 regulates enzyme stability. Phys. Chem. Chem. Phys. 18, 8890–8900. doi: 10.1039/C5CP06301A

PubMed Abstract | CrossRef Full Text | Google Scholar

Sneha, P., and Doss, C. G. P. (2016). Gliptins in managing diabetes - reviewing computational strategy. Life Sci. 166, 108–120. doi: 10.1016/j.lfs.2016.10.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Song, K. Z., Bao, J., Sun, Y. M., and Zhang, J. Z. H. (2014). Binding of N-substituted pyrrole derivatives to HIV-1 gp41. J. Theor. Comput. Chem. 13:1450018. doi: 10.1142/S0219633614500187

CrossRef Full Text | Google Scholar

Srinivasan, J., Cheatham, T. E., Cieplak, P., Kollman, P. A., and Case, D. A. (1998). Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate - DNA helices. J. Am. Chem. Soc. 120, 9401–9409. doi: 10.1021/ja981844+

CrossRef Full Text | Google Scholar

Sroczynski, D., Malinowski, Z., Szczesniak, A. K., and Pakulska, W. (2016). New 1(2H)-phthalazinone derivatives as potent nonpeptidic HIV-1 protease inhibitors: molecular docking studies, molecular dynamics simulation, oral bioavailability and ADME prediction. Mol. Simul. 42, 628–641. doi: 10.1080/08927022.2015.1067808

CrossRef Full Text | Google Scholar

Starovoytov, O. N., Liu, Y. L., Tan, L. X., and Yang, S. Z. (2014). Effects of the hydroxyl group on phenyl based Ligand/ERR gamma protein binding. Chem. Res. Toxicol. 27, 1371–1379. doi: 10.1021/tx500082r

CrossRef Full Text | Google Scholar

Straatsma, T. P., and McCammon, J. A. (1991). Multiconfiguration thermodynamic integration. J. Chem. Phys. 95, 1175–1188. doi: 10.1063/1.461148

CrossRef Full Text | Google Scholar

Su, P. C., Tsai, C. C., Mehboob, S., Hevener, K. E., and Johnson, M. E. (2015). Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F-tularensis enoyl-ACP reductase (FabI). J. Comput. Chem. 36, 1859–1873. doi: 10.1002/jcc.24011

PubMed Abstract | CrossRef Full Text | Google Scholar

Su, Y., and Gallicchio, E. (2004). The non-polar solvent potential of mean force for the dimerization of alanine dipeptide: the role of solute-solvent van der Waals interactions. Biophys. Chem. 109, 251–260. doi: 10.1016/j.bpc.2003.11.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, H. Y., Li, Y. Y., Shen, M. Y., Tian, S., Xu, L., Pan, P. C., et al. (2014a). Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys. Chem. Chem. Phys. 16, 22035–22045. doi: 10.1039/C4CP03179B

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, H. Y., Li, Y. Y., Tian, S., Xu, L., and Hou, T. J. (2014b). Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys. 16, 16719–16729. doi: 10.1039/C4CP01388C

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, Y. P., and Liu, Q. (2015). Differential structural dynamics and antigenicity of two similar influenza H5N1 virus HA-specific HLA-A*0201-restricted CLT epitopes. RSC Adv. 5, 2318–2327. doi: 10.1039/C4RA08874C

CrossRef Full Text | Google Scholar

Suri, C., and Naik, P. K. (2015). Combined molecular dynamics and continuum solvent approaches (MM-PBSA/GBSA) to predict noscapinoid binding to gamma-tubulin dimer. SAR QSAR Environ. Res. 26, 507–519. doi: 10.1080/1062936X.2015.1070200

PubMed Abstract | CrossRef Full Text | Google Scholar

Suri, C., Hendrickson, T. W., Joshi, H. C., and Naik, P. K. (2014). Molecular insight into gamma-gamma tubulin lateral interactions within the gamma-tubulin ring complex (gamma-TuRC). J. Comput. Aided Mol. Des. 28, 961–972. doi: 10.1007/s10822-014-9779-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Suri, C., Joshi, H. C., and Naik, P. K. (2015). Molecular modeling reveals binding interface of gamma-tubulin with GCP4 and interactions with noscapinoids. Proteins 83, 827–843. doi: 10.1002/prot.24773

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, C., Tan, Y. H., and Luo, R. (2007). Implicit nonpolar solvent models. J. Phys. Chem. B 111, 12263–12274. doi: 10.1021/jp073399n

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, C., Yang, L., and Luo, R. (2006). How well does Poisson-Boltzmann implicit solvent agree with explicit solvent? A quantitative analysis. J. Phys. Chem. B 110, 18680–18687. doi: 10.1021/jp063479b

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, Y., and Luo, R. (2009). Structural and functional implications of p53 missense cancer mutations. BMC Biophys. 2:5. doi: 10.1186/1757-5036-2-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, Y.-H., and Luo, R. (2008). Protein stability prediction: a Poisson-Boltzmann approach. J. Phys. Chem. B 112, 1875–1883. doi: 10.1021/jp709660v

PubMed Abstract | CrossRef Full Text | Google Scholar

Tang, C. L., Alexov, E., Pyle, A. M., and Honig, B. (2007). Calculation of pK(a)s in RNA: on the structural origins and functional roles of protonated nucleotides. J. Mol. Biol. 366, 1475–1496. doi: 10.1016/j.jmb.2006.12.001

CrossRef Full Text | Google Scholar

Tazikeh-Lemeski, E. (2016). Binding free energy and the structural changes determination in hGH protein with different concentrations of copper ions (A molecular dynamics simulation study). J. Theor. Comput. Chem. 15:1650045. doi: 10.1142/S0219633616500450

CrossRef Full Text | Google Scholar

Tiwari, G., and Mohanty, D. (2014). Structure-based multiscale approach for identification of interaction partners of PDZ domains. J. Chem. Inf. Model. 54, 1143–1156. doi: 10.1021/ci400627y

PubMed Abstract | CrossRef Full Text | Google Scholar

Tong, M. Q., Wang, Q., Wang, Y., and Chen, G. J. (2015). Structures and energies of the transition between two conformations of the alternate frame folding calbindin-D-9k protein: a theoretical study. RSC Adv. 5, 65798–65810. doi: 10.1039/C5RA11234F

CrossRef Full Text | Google Scholar

Tran, N., Van, T., Nguyen, H., and Le, L. (2015). Identification of Novel Compounds against an R294K Substitution of Influenza A (H7N9) virus using ensemble based drug virtual screening. Int. J. Med. Sci. 12, 163–176. doi: 10.7150/ijms.10826

PubMed Abstract | CrossRef Full Text | Google Scholar

Tumbi, K. M., Nandekar, P. P., Shaikh, N., Kesharwani, S. S., and Sangamwar, A. T. (2014). Molecular dynamics simulation studies for DNA sequence recognition by reactive metabolites of anticancer compounds. J. Mol. Recogn. 27, 138–150. doi: 10.1002/jmr.2342

PubMed Abstract | CrossRef Full Text | Google Scholar

Tzoupis, H., Leonis, G., Avramopoulos, A., Mavromoustakos, T., and Papadopoulos, M. G. (2014). Systematic molecular dynamics, MM-PBSA, and Ab initio approaches to the saquinavir resistance mechanism in HIV-1 PR due to 11 double and multiple mutations. J. Phys. Chem. B 118, 9538–9552. doi: 10.1021/jp502687q

PubMed Abstract | CrossRef Full Text | Google Scholar

Tzoupis, H., Leonis, G., Avramopoulos, A., Reis, H., Czyznikowska, Z., Zerva, S., et al. (2015). Elucidation of the binding mechanism of renin using a wide array of computational techniques and biological assays. J. Mol. Graph. Model. 62, 138–149. doi: 10.1016/j.jmgm.2015.09.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Verma, R., Yadav, M., Pradhan, D., Bhuyan, R., Aggarwal, S., Nayek, A., et al. (2016). Probing binding mechanism of interleukin-6 and olokizumab: in silico design of potential lead antibodies for autoimmune and inflammatory diseases. J. Recept. Signal Trans. 36, 601–616. doi: 10.3109/10799893.2016.1147584

PubMed Abstract | CrossRef Full Text | Google Scholar

Verma, S., Grover, S., Tyagi, C., Goyal, S., Jamal, S., Singh, A., et al. (2016). Hydrophobic interactions are a key to MDM2 inhibition by polyphenols as revealed by molecular dynamics simulations and MM/PBSA free energy calculations. PLoS ONE 11:14. doi: 10.1371/journal.pone.0149014

PubMed Abstract | CrossRef Full Text | Google Scholar

Verma, S., Singh, A., Kumari, A., Goyal, S., Jamal, S., Sinha, S., et al. (2017). Dissecting the role of mutations in chymase inhibition: free energy and decomposition analysis. Gene 609, 68–79. doi: 10.1016/j.gene.2017.01.030

PubMed Abstract | CrossRef Full Text | Google Scholar

Wagoner, J. A., and Baker, N. A. (2006). Assessing implicit models for nonpolar mean solvation forces: the importance of dispersion and volume terms. Proc. Natl. Acad. Sci. U.S.A. 103, 8331–8336. doi: 10.1073/pnas.0600118103

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C. H., Nguyen, P. H., Pham, K., Huynh, D., Le, T. B. N., Wang, H. L., et al. (2016). Calculating protein-ligand binding affinities with MMPBSA: method and error analysis. J. Comput. Chem. 37, 2436–2446. doi: 10.1002/jcc.24467

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C. H., Xiao, L., and Luo, R. (2017). Numerical interpretation of molecular surface field in dielectric modeling of solvation. J. Comput. Chem. 38, 1057–1070. doi: 10.1002/jcc.24782

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C., Wang, J., Cai, Q., Li, Z. L., Zhao, H., and Luo, R. (2013). Exploring high accuracy Poisson-Boltzmann methods for biomolecular simulations. Comput. Theor. Chem. 1024, 34–44. doi: 10.1016/j.comptc.2013.09.021

CrossRef Full Text | Google Scholar

Wang, F. F., Yang, W., Shi, Y. H., and Le, G. W. (2015). 3D-QSAR, molecular docking and molecular dynamics studies of a series of ROR gamma t inhibitors. J. Biomol. Struct. Dyn. 33, 1929–1940. doi: 10.1080/07391102.2014.980321

CrossRef Full Text | Google Scholar

Wang, J. H., Li, Y., Yang, Y. F., Zhang, J. X., Du, J., Zhang, S. W., et al. (2015). Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor. RSC Adv. 5, 78278–78298. doi: 10.1039/C5RA04299B

CrossRef Full Text | Google Scholar

Wang, J. H., Yang, Y. F., Li, Y., and Wang, Y. H. (2016). Computational study exploring the interaction mechanism of benzimidazole derivatives as potent cattle bovine viral diarrhea virus inhibitors. J. Agric. Food Chem. 64, 5941–5950. doi: 10.1021/acs.jafc.6b01067

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J. L., Cheng, L. P., Wang, T. C., Deng, W., and Wu, F. H. (2017). Molecular modeling study of CP-690550 derivatives as JAK3 kinase inhibitors through combined 3D-QSAR, molecular docking, and dynamics simulation techniques. J. Mol. Graph. Model. 72, 178–186. doi: 10.1016/j.jmgm.2016.12.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., and Luo, R. (2010). Assessment of linear finite-difference Poisson-Boltzmann solvers. J. Comput. Chem. 31, 1689–1698. doi: 10.1002/jcc.21456

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., Cai, Q., Li, Z.-L., Zhao, H.-K., and Luo, R. (2009). Achieving energy conservation in Poisson-Boltzmann molecular dynamics: accuracy and precision with finite-difference algorithms. Chem. Phys. Lett. 468, 112–118. doi: 10.1016/j.cplett.2008.12.049

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., Cai, Q., Xiang, Y., and Luo, R. (2012). Reducing Grid Dependence in finite-difference Poisson-Boltzmann calculations. J. Chem. Theor. Comput. 8, 2741–2751. doi: 10.1021/ct300341d

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., Tan, C., Chanco, E., and Luo, R. (2010). Quantitative analysis of Poisson-Boltzmann implicit solvent in molecular dynamics. Phys. Chem. Chem. Phys. 12, 1194–1202. doi: 10.1039/B917775B

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, L., Bao, Q. C., Xu, X. L., Jiang, F., Gu, K., Jiang, Z. Y., et al. (2015). Discovery and identification of Cdc37-derived peptides targeting the Hsp90-Cdc37 protein-protein interaction. RSC Adv. 5, 96138–96145. doi: 10.1039/C5RA20408A

CrossRef Full Text | Google Scholar

Wang, Q., Zheng, Q. C., and Zhang, H. X. (2016). Exploring the mechanism how AF9 recognizes and binds H3K9ac by molecular dynamics simulations and free energy calculations. Biopolymers 105, 779–786. doi: 10.1002/bip.22896

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, R. M., Zhou, H., Siu, S. W. I., Gan, Y., Wang, Y. T., and Ouyang, D. F. (2015). Comparison of three molecular simulation approaches for cyclodextrin-ibuprofen complexation. J. Nanomater. 8:193049. doi: 10.1155/2015/193049

CrossRef Full Text | Google Scholar

Wang, X. L., Li, C. Q., Wang, Y., and Chen, G. J. (2014). Interaction of classical platinum agents with the monomeric and dimeric Atox1 proteins: a molecular dynamics simulation study. Int. J. Mol. Sci. 15, 75–99. doi: 10.3390/ijms15010075

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, X. W., Pan, P. C., Li, Y. Y., Li, D., and Hou, T. J. (2014). Exploring the prominent performance of CX-4945 derivatives as protein kinase CK2 inhibitors by a combined computational study. Mol. Biosyst. 10, 1196–1210. doi: 10.1039/C4MB00013G

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, X., Du, J., and Yao, X. J. (2015). Structural and dynamic basis of acid amido synthetase GH3.1: an investigation of substrate selectivity and major active site access channels. Mol. Biosyst. 11, 809–818. doi: 10.1039/C4MB00608A

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y. T., and Chen, Y. C. (2014). Insights from QM/MM modeling the 3D structure of the 2009 H1N1 influenza A virus neuraminidase and its binding interactions with antiviral drugs. Mol. Inform. 33, 240–249. doi: 10.1002/minf.201300117

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Z. G., and Liu, J. P. (2017). Effects of the central potassium ions on the G-quadruplex and stabilizer binding. J. Mol. Graph. Model. 72, 168–177. doi: 10.1016/j.jmgm.2017.01.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Z., Chen, R., Hou, L., Li, J., and Liu, J. P. (2015). Molecular dynamics and principal components of potassium binding with human telomeric intra-molecular G-quadruplex. Protein Cell 6, 423–433. doi: 10.1007/s13238-015-0155-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Warwicker, J. (2004). Improved pK(a) calculations through flexibility based sampling of a water-dominated interaction scheme. Protein Sci. 13, 2793–2805. doi: 10.1110/ps.04785604

CrossRef Full Text | Google Scholar

Warwicker, J., and Watson, H. C. (1982). Calculation of the electric-potential in the active-site cleft due to alpha-helix dipoles. J. Mol. Biol. 157, 671–679. doi: 10.1016/0022-2836(82)90505-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Weeks, J. D., Chandler, D., and Andersen, H. C. (1971). Role of repulsive forces in determining equilibrium structure of simple liquids. J. Chem. Phys. 54:5237. doi: 10.1063/1.1674820

CrossRef Full Text | Google Scholar

Wen, E. Z., and Luo, R. (2004). Interplay of secondary structures and side-chain contacts in the denatured state of BBA1. J. Chem. Phys. 121, 2412–2421. doi: 10.1063/1.1768151

PubMed Abstract | CrossRef Full Text | Google Scholar

Wen, E. Z., Hsieh, M. J., Kollman, P. A., and Luo, R. (2004). Enhanced ab initio protein folding simulations in Poisson-Boltzmann molecular dynamics with self-guiding forces. J. Mol. Graph. Model. 22, 415–424. doi: 10.1016/j.jmgm.2003.12.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Wichapong, K., Alard, J. E., Ortega-Gomez, A., Weber, C., Hackeng, T. M., Soehnlein, O., et al. (2016). Structure-based design of peptidic inhibitors of the interaction between CC chemokine ligand 5 (CCL5) and human neutrophil peptides 1 (HNP1). J. Med. Chem. 59, 4289–4301. doi: 10.1021/acs.jmedchem.5b01952

PubMed Abstract | CrossRef Full Text | Google Scholar

Wichapong, K., Rohe, A., Platzer, C., Slynko, I., Erdmann, F., Schmidt, M., et al. (2014). Application of docking and QM/MM-GBSA rescoring to screen for novel Myt1 kinase inhibitors. J. Chem. Inf. Model. 54, 881–893. doi: 10.1021/ci4007326

PubMed Abstract | CrossRef Full Text | Google Scholar

Widom, B. (1982). Potential-distribution theory and the statistical-mechanics of fluids. J. Phys. Chem. 86, 869–872. doi: 10.1021/j100395a005

CrossRef Full Text | Google Scholar

Wolf, A., Schoof, S., Baumann, S., Arndt, H. D., and Kirschner, K. N. (2014). Structure-activity relationships of thiostrepton derivatives: implications for rational drug design. J. Comput. Aided Mol. Des. 28, 1205–1215. doi: 10.1007/s10822-014-9797-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Wright, D. W., Hall, B. A., Kenway, O. A., Jha, S., and Coveney, P. V. (2014). Computing clinically relevant binding free energies of HIV-1 protease inhibitors. J. Chem. Theory Comput. 10, 1228–1241. doi: 10.1021/ct4007037

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, X. Y., Wan, S. H., Wang, G. F., Jin, H., Li, Z. H., Tian, Y. X., et al. (2015). Molecular dynamics simulation and free energy calculation studies of kinase inhibitors binding to active and inactive conformations of VEGFR-2. J. Mol. Graph. Model. 56, 103–112. doi: 10.1016/j.jmgm.2014.12.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Xanthopoulos, D., Kritsi, E., Supuran, C. T., Papadopoulos, M. G., Leonis, G., and Zoumpoulakis, P. (2016). Discovery of HIV type1 aspartic protease hit compounds through combined computational approaches. ChemMedChem 11, 1646–1652. doi: 10.1002/cmdc.201600220

CrossRef Full Text | Google Scholar

Xiao, L., Diao, J., Greene, D. A., Wang, J., and Luo, R. (2017). A continuum Poisson-Boltzmann model for membrane channel proteins. J. Chem. Theory Comput. 13, 3398–3412. doi: 10.1021/acs.jctc.7b00382

PubMed Abstract | CrossRef Full Text | Google Scholar

Xie, D. X. (2014). New solution decomposition and minimization schemes for Poisson-Boltzmann equation in calculation of biomolecular electrostatics. J. Comput. Phys. 275, 294–309. doi: 10.1016/j.jcp.2014.07.012

CrossRef Full Text | Google Scholar

Xie, D. X., and Jiang, Y. (2016). A nonlocal modified Poisson-Boltzmann equation and finite element solver for computing electrostatics of biomolecules. J. Comput. Phys. 322, 1–20. doi: 10.1016/j.jcp.2016.06.028

CrossRef Full Text | Google Scholar

Xiong, L., Zhu, X. L., Shen, Y. Q., Wishwa, W., Li, K., and Yang, G. F. (2015). Discovery of N-benzoxazol-5-yl-pyrazole-4-carboxamides as nanomolar SQR inhibitors. Eur. J. Med. Chem. 95, 424–434. doi: 10.1016/j.ejmech.2015.03.060

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, W., Amire-Brahimi, B., Xie, X. J., Huang, L. Y., and Ji, J. Y. (2014). All-atomic molecular dynamic studies of human CDK8: insight into the A-loop, point mutations and binding with its partner CycC. Comput. Biol. Chem. 51, 1–11. doi: 10.1016/j.compbiolchem.2014.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Xue, W. W., Yang, Y., Wang, X. T., Liu, H. X., and Yao, X. J. (2014). Computational study on the inhibitor binding mode and allosteric regulation mechanism in hepatitis C virus NS3/4A protein. PLoS ONE 9:e87077. doi: 10.1371/journal.pone.0087077

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, L. L., Yan, C. L., Qian, K., Su, H. R., Kofsky-Wofford, S. A., Lee, W. C., et al. (2014). Diamidine compounds for selective inhibition of protein arginine methyltransferase 1. J. Med. Chem. 57, 2611–2622. doi: 10.1021/jm401884z

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, T. Y., Wu, J. C., Yan, C. L., Wang, Y. F., Luo, R., Gonzales, M. B., et al. (2011). Virtual screening using molecular simulations. Proteins Struct. Funct. Bioinform. 79, 1940–1951. doi: 10.1002/prot.23018

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, X. Y., Lu, J. R., Ying, M., Mu, J. B., Li, P. C., and Liu, Y. (2017). Docking and molecular dynamics studies on triclosan derivatives binding to FabI. J. Mol. Model. 23:13. doi: 10.1007/s00894-016-3192-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, Z. W., Wu, F., Yuan, X. H., Zhang, L., and Zhang, S. L. (2016). Novel binding patterns between ganoderic acids and neuraminidase: insights from docking, molecular dynamics and MM/PBSA studies. J. Mol. Graph. Model. 65, 27–34. doi: 10.1016/j.jmgm.2016.02.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Ye, X., Cai, Q., Yang, W., and Luo, R. (2009). Roles of boundary conditions in DNA simulations: analysis of ion distributions with the finite-difference Poisson-Boltzmann method. Biophys. J. 97, 554–562. doi: 10.1016/j.bpj.2009.05.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Ye, X., Wang, J., and Luo, R. (2010). A revised density function for molecular surface calculation in continuum solvent models. J. Chem. Theory Comput. 6, 1157–1169. doi: 10.1021/ct900318u

CrossRef Full Text | Google Scholar

Yesudas, J. P., Blinov, N., Dew, S. K., and Kovalenko, A. (2015). Calculation of binding free energy of short double stranded oligonucleotides using MM/3D-RISM-KH approach. J. Mol. Liq. 201, 68–76. doi: 10.1016/j.molliq.2014.11.017

CrossRef Full Text | Google Scholar

Yu, H. J., Fang, Y., Lu, X., Liu, Y. J., and Zhang, H. B. (2014). Combined 3D-QSAR, molecular docking, molecular dynamics simulation, and binding free energy calculation studies on the 5-hydroxy-2h-pyridazin-3-one derivatives as HCV NS5B polymerase inhibitors. Chem. Biol. Drug Des. 83, 89–105. doi: 10.1111/cbdd.12203

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, H. X., Li, D. X., Guo, B. S., Liu, L., Pan, Q., Liu, B. L., et al. (2016a). Effect of Solvent Water Molecules on Human Serum Albumin Complex Docked Paclitaxel by MM-PBSA Method. Singapore: World Scientific Publ Co Pte Ltd.

PubMed Abstract | Google Scholar

Yu, H. X., Li, D. X., Xu, F., Pan, Q., Chai, P., Liu, B. L., et al. (2016b). The binding affinity of human serum albumin and paclitaxel through MMPBSA based on docked complex. Mol. Simul. 42, 1460–1467. doi: 10.1080/08927022.2016.1198479

CrossRef Full Text | Google Scholar

Zacharias, M. (2003). Continuum solvent modeling of nonpolar solvation: improvement by separating surface area dependent cavity and dispersion contributions. J. Phys. Chem. A 107, 3000–3004. doi: 10.1021/jp027598c

CrossRef Full Text | Google Scholar

Zhan, D. L., Yu, L., Jin, H. Y., Guan, S. S., and Han, W. W. (2014). Molecular modeling and MM-PBSA free energy analysis of endo-1,4-beta-xylanase from Ruminococcus albus 8. Int. J. Mol. Sci. 15, 17284–17303. doi: 10.3390/ijms151017284

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhan, M. Z., Guo, M., Jiang, Y. K., and Wang, X. M. (2015). Characterization of the interaction between gallic acid and lysozyme by molecular dynamics simulation and optical spectroscopy. Int. J. Mol. Sci. 16, 14786–14807. doi: 10.3390/ijms160714786

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, H., Lu, J.-R., Mu, J.-B., Liu, J.-B., Yang, X.-Y., Wang, M.-J., et al. (2015). Molecular dynamics simulation and antibacterial mechanism of 3MBA derivatives as FtsZ protein inhibitors. Acta Phys. Chim. Sin. 31, 566–575. doi: 10.3866/PKU.WHXB201501061

CrossRef Full Text | Google Scholar

Zhang, L., and Sun, Y. (2014). Biomimetic design of platelet adhesion inhibitors to block integrin alpha 2 beta 1-collagen interactions: I. Construction of an affinity binding model. Langmuir 30, 4725–4733. doi: 10.1021/la404599s

CrossRef Full Text | Google Scholar

Zhang, L., Tang, R. H., Bai, S., Connors, N. K., Lua, L. H. L., Chuan, Y. P., et al. (2014). Energetic changes caused by antigenic module insertion in a virus-like particle revealed by experiment and molecular dynamics simulations. PLoS ONE 9:e107313. doi: 10.1371/journal.pone.0107313

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, L. H., Liu, T. J., Wang, X., Wang, J. N., Li, G. H., Li, Y., et al. (2014). Insight into the binding mode and the structural features of the pyrimidine derivatives as human A(2A) adenosine receptor antagonists. Biosystems 115, 13–22. doi: 10.1016/j.biosystems.2013.04.003

CrossRef Full Text | Google Scholar

Zhang, L. Y., Li, Y. Z., Yuan, Y., Jiang, Y. Y., Guo, Y. Z., Li, M. L., et al. (2016). Molecular mechanism of carbon nanotube to activate Subtilisin Carlsberg in polar and non-polar organic media. Sci. Rep. 6:36838. doi: 10.1038/srep36838

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, L. Y., Xiao, X. C., Yuan, Y., Guo, Y. Z., Li, M. L., and Pu, X. M. (2015). Probing immobilization mechanism of alpha-chymotrypsin onto carbon nanotube in organic media by molecular dynamics simulation. Sci. Rep. 5:9297. doi: 10.1038/srep09297

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, W., Li, J. Z., Huang, Z. X., Wang, H. Y., Luo, H., Wang, X., et al. (2016). Computer-aided identification of potential TYK2 inhibitors from drug database. J. Mol. Struct. 1122, 309–317. doi: 10.1016/j.molstruc.2016.05.099

CrossRef Full Text | Google Scholar

Zhao, F. L., Yang, G. H., Xiang, S., Gao, D. D., and Zeng, C. (2017). In silico analysis of the effect of mutation on epidermal growth factor receptor in non-small-cell lung carcinoma: from mutational analysis to drug designing. J. Biomol. Struct. Dyn. 35, 427–434. doi: 10.1080/07391102.2016.1146165

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, R. N., Fan, S., Han, J. G., and Liu, G. (2015). Molecular dynamics study of segment peptides of Bax, Bim, and Mcl-1 BH3 domain of the apoptosis-regulating proteins bound to the anti-apoptotic Mcl-1 protein. J. Biomol. Struct. Dyn. 33, 1067–1081. doi: 10.1080/07391102.2014.929028

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, Z. B., Liu, Y., and Yao, Y. (2014). Computational determination of binding structures and free energies of glucose 6-phosphate dehydrogenase with novel steroid inhibitors. J. Mol. Graph. Model. 51, 168–172. doi: 10.1016/j.jmgm.2014.05.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, D.-D., Yu, Y.-Q., Wu, H., Li, Y.-N., and Qiao, J.-J. (2017). Simulation study on the mechanism of molecular chaperone HdeA and SurA. Prog. Biochem. Biophys. 44, 242–252. doi: 10.16476/j.pibb.2016.0347

CrossRef Full Text

Zhou, S. L., Wang, M., Tong, Z. F., and Wang, J. Y. (2016). The recognition mechanism of crizotinib on MTH1: influence of chirality on the bioactivity. Mol. Phys. 114, 2364–2372. doi: 10.1080/00268976.2016.1145750

CrossRef Full Text | Google Scholar

Zhou, Z. G., Yao, Q. Z., Lei, D., Zhang, Q. Q., and Zhang, J. (2014). Investigations on the mechanisms of interactions between matrix metalloproteinase 9 and its flavonoid inhibitors using a combination of molecular docking, hybrid quantum mechanical/molecular mechanical calculations, and molecular dynamics simulations. Can. J. Chem. 92, 821–830. doi: 10.1139/cjc-2014-0180

CrossRef Full Text | Google Scholar

Zhu, X. L., Xiong, L., Li, H., Song, X. Y., Liu, J. J., and Yang, G. F. (2014). Computational and experimental insight into the molecular mechanism of carboxamide inhibitors of succinate-ubquinone oxidoreductase. ChemMedChem 9, 1512–1521. doi: 10.1002/cmdc.201300456

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, X. L., Zhang, M. M., Liu, J. J., Ge, J. M., and Yang, G. F. (2015). Ametoctradin is a Potent Q(o) Site inhibitor of the mitochondria! respiration complex III. J. Agric. Food Chem. 63, 3377–3386. doi: 10.1021/acs.jafc.5b00228

CrossRef Full Text | Google Scholar

Zhu, Y. L., Beroza, P., and Artis, D. R. (2014). Including explicit water molecules as part of the protein structure in MM/PBSA calculations. J. Chem. Inf. Model. 54, 462–469. doi: 10.1021/ci4001794

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhuang, X. Y., Zhao, B., Liu, S., Song, F. R., Cui, F. C., Liu, Z. Q., et al. (2016). Noncovalent interactions between superoxide dismutase and flavonoids studied by native mass spectrometry combined with molecular simulations. Anal. Chem. 88, 11720–11726. doi: 10.1021/acs.analchem.6b03359

PubMed Abstract | CrossRef Full Text | Google Scholar

Zou, Y., Wang, F., Wang, Y., Guo, W. J., Zhang, Y. H., Xu, Q., et al. (2017). Systematic study of imidazoles inhibiting IDO1 via the integration of molecular mechanics and quantum mechanics calculations. Eur. J. Med. Chem. 131, 152–170. doi: 10.1016/j.ejmech.2017.03.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Zwanzig, R. W. (1954). High-temperature equation of state by a perturbation method. I. Nonpolar gases. J. Chem. Phys. 22, 1420–1426. doi: 10.1063/1.1740409

CrossRef Full Text | Google Scholar

Keywords: molecular recognition, binding affinity, free energy simulation, MMPBSA, continuum solvation model

Citation: Wang C, Greene D, Xiao L, Qi R and Luo R (2018) Recent Developments and Applications of the MMPBSA Method. Front. Mol. Biosci. 4:87. doi: 10.3389/fmolb.2017.00087

Received: 18 September 2017; Accepted: 30 November 2017;
Published: 10 January 2018.

Edited by:

Chia-en Chang, University of California, Riverside, United States

Reviewed by:

Wei Chen, University of California, Riverside, United States
Haifeng Chen, Shanghai Jiao Tong University, China

Copyright © 2018 Wang, Greene, Xiao, Qi and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ray Luo, rluo@uci.edu

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