About this Research Topic
This Research Topic is Volume II of a series. The previous volume can be found here: Computational Drug Discovery for Targeting of Protein-Protein Interfaces
Protein-protein and protein-peptide recognition are among the most important processes that make signal transmission and transduction possible in a cell. Theoretical approaches in the study of these processes from a structural and thermodynamic viewpoint have focused on methods and bioinformatic procedures aimed at modelling, predicting, and characterizing the interface between the interacting proteins. Due to the complexity and dimension of these kinds of system, the use of experimental data can drive progress towards a more precise model of interaction between the proteins. This information could be translated directly to a chemical structure that is able to mimic one of the interacting partners and bind to the other. In this way, small molecules can be developed as drug candidates that can enhance, modulate, or inhibit recognition phenomena in the cell that might be involved in altered pathways due to pathological conditions such as the formation and growth of amyloid fibrils, apoptosis regulation, chaperone-client interaction, tumor angiogenesis, and virus capsid formation.
This Research Topic will focus on the development and application of novel computational methods for drug design of small molecules for the targeting of protein-protein interfaces. To fit within the scope of the journal, all computational studies must demonstrate a clear use in medicinal chemistry through comparison with experimental data. We welcome Original Research, Perspective, and Review articles on the following topics:
• Target identification: detection of druggable interfaces or hotspot, and binding site identification
• Virtual screening and docking for protein interfaces supported by experimental data
• Exploration of conformational dynamics of binding surfaces
• Computational-experimental integration
• Machine Learning approaches to targeting protein-protein interactions
• Classes of ligands/targets/interactions include:
(i)Peptide-based inhibitors
(ii) Intrinsically disorder protein and protein regions
(iii) Capsid proteins
(iv) Chaperone-client inhibitors
(v) Antimicrobial peptides
Protein-protein and protein-peptide recognition are among the most important processes that make signal transmission and transduction possible in a cell. Theoretical approaches in the study of these processes from a structural and thermodynamic viewpoint have focused on methods and bioinformatic procedures aimed at modelling, predicting, and characterizing the interface between the interacting proteins. Due to the complexity and dimension of these kinds of system, the use of experimental data can drive progress towards a more precise model of interaction between the proteins. This information could be translated directly to a chemical structure that is able to mimic one of the interacting partners and bind to the other. In this way, small molecules can be developed as drug candidates that can enhance, modulate, or inhibit recognition phenomena in the cell that might be involved in altered pathways due to pathological conditions such as the formation and growth of amyloid fibrils, apoptosis regulation, chaperone-client interaction, tumor angiogenesis, and virus capsid formation.
This Research Topic will focus on the development and application of novel computational methods for drug design of small molecules for the targeting of protein-protein interfaces. To fit within the scope of the journal, all computational studies must demonstrate a clear use in medicinal chemistry through comparison with experimental data. We welcome Original Research, Perspective, and Review articles on the following topics:
• Target identification: detection of druggable interfaces or hotspot, and binding site identification
• Virtual screening and docking for protein interfaces supported by experimental data
• Exploration of conformational dynamics of binding surfaces
• Computational-experimental integration
• Machine Learning approaches to targeting protein-protein interactions
• Classes of ligands/targets/interactions include:
(i)Peptide-based inhibitors
(ii) Intrinsically disorder protein and protein regions
(iii) Capsid proteins
(iv) Chaperone-client inhibitors
(v) Antimicrobial peptides
Keywords: drug discovery, theoretical approaches, computational methods, Protein – protein interface
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
