Computational prediction of protein-protein binding affinities

Protein-protein interactions form central elements of almost all cellular processes. Knowledge of the structure of protein-protein complexes but also of the binding affinity is of major importance to understand the biological function of protein-protein interactions. Even weak transient protein-protein interactions can be of functional relevance for the cell during signal transduction or regulation of metabolism. The structure of a growing number of protein-protein complexes has been solved in recent years. Combined with docking approaches or template-based methods, it is possible to generate structural models of many putative protein-protein complexes or to design new protein-protein interactions. In order to evaluate the functional relevance of putative or predicted protein-protein complexes, realistic binding affinity prediction is of increasing importance. Several computational tools ranging from simple force-field or knowledge-based scoring of single protein-protein complexes to ensemble-based approaches and rigorous binding free energy simulations are available to predict relative and absolute binding affinities of complexes. With a focus on molecular mechanics force-field approaches the present review aims at presenting an overview on available methods and discussing advantages, approximations, and limitations of the various methods. This article is categorized under: Molecular and Statistical Mechanics > Molecular Interactions Molecular and Statistical Mechanics > Free Energy Methods Software > Molecular Modeling