Protein-protein interactions are essential for life processes. Many proteins function by interacting either with copies of themselves or with other proteins. Thus, modeling of proteins and protein assemblies is important for understanding the fundamental principles and specific aspects of protein interactions. Until recently, a reliable three-dimensional structure database of models of proteins of interest was a dream. Such structure model or prediction has become reality as a result of a collaboration between the company DeepMind and the European Molecular Biology Laboratory. The resulting protein models are often as accurate as experimentally determined structures as showcased in the 14th Critical Assessment of Protein Structure Prediction (CASP14) experiments.
Given the success of DeepMind’s AlphaFold2 for protein structure prediction, the scientific community is now anticipating methods/algorithms that will enable us to reliably predict the structures of protein complexes. Recent studies showed that AlphaFold2 is not just a tool for modeling monomeric structures but can also model protein complexes. Most recently released software, AlphaFold-Multimer and RoseTTAFold demonstrate that the deep learning neural network architecture-based methods can model protein complexes in addition to individual proteins. However, accurate prediction of protein complexes remains a challenge.
The aim of this Research Topic is to highlight promising and compelling advanced computational/experimental approaches for the prediction/characterization of protein-protein interactions and protein assemblies. This topic collection will inspire, inform, and provide direction to researchers in the field of PPIs. We welcome the submission of original research articles, reviews, opinions & perspectives, and brief reports. The research areas to be covered in this Research Topic may include, but are not limited to:
• Protein structure, dynamics, and function.
• Development and application of docking methodologies.
• Challenges in structural modeling of protein-protein interactions (PPIs).
• Modelling and simulations of protein structures and prediction of protein complexes.
• New strategies and methods to include conformational flexibility of proteins, protein assembly, and molecular crowding effects.
• Deep learning techniques for protein structure modeling, i.e., AlphaFold and RoseTTAFold.
• Graph-based methods for prediction of protein-protein interactions
• Discovery and development of novel drug-like small molecules targeting protein-protein interactions
• Biological and biophysical technologies/methods to explore PPIs
• Construction of protein interaction networks and databases
Protein-protein interactions are essential for life processes. Many proteins function by interacting either with copies of themselves or with other proteins. Thus, modeling of proteins and protein assemblies is important for understanding the fundamental principles and specific aspects of protein interactions. Until recently, a reliable three-dimensional structure database of models of proteins of interest was a dream. Such structure model or prediction has become reality as a result of a collaboration between the company DeepMind and the European Molecular Biology Laboratory. The resulting protein models are often as accurate as experimentally determined structures as showcased in the 14th Critical Assessment of Protein Structure Prediction (CASP14) experiments.
Given the success of DeepMind’s AlphaFold2 for protein structure prediction, the scientific community is now anticipating methods/algorithms that will enable us to reliably predict the structures of protein complexes. Recent studies showed that AlphaFold2 is not just a tool for modeling monomeric structures but can also model protein complexes. Most recently released software, AlphaFold-Multimer and RoseTTAFold demonstrate that the deep learning neural network architecture-based methods can model protein complexes in addition to individual proteins. However, accurate prediction of protein complexes remains a challenge.
The aim of this Research Topic is to highlight promising and compelling advanced computational/experimental approaches for the prediction/characterization of protein-protein interactions and protein assemblies. This topic collection will inspire, inform, and provide direction to researchers in the field of PPIs. We welcome the submission of original research articles, reviews, opinions & perspectives, and brief reports. The research areas to be covered in this Research Topic may include, but are not limited to:
• Protein structure, dynamics, and function.
• Development and application of docking methodologies.
• Challenges in structural modeling of protein-protein interactions (PPIs).
• Modelling and simulations of protein structures and prediction of protein complexes.
• New strategies and methods to include conformational flexibility of proteins, protein assembly, and molecular crowding effects.
• Deep learning techniques for protein structure modeling, i.e., AlphaFold and RoseTTAFold.
• Graph-based methods for prediction of protein-protein interactions
• Discovery and development of novel drug-like small molecules targeting protein-protein interactions
• Biological and biophysical technologies/methods to explore PPIs
• Construction of protein interaction networks and databases
