Abstract
The majority of biological processes are mediated via protein–protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
Highlights
Proteins interact with other proteins, DNA, RNA and small molecules to perform their cellular tasks
Over the past 25 years, there has been a rapid development of computational methods aiming to elucidate protein complexes, such as protein interaction prediction, protein–protein docking and protein interface prediction
The subject of this review is the middle ground between these two problems, protein interface prediction, where one wishes to identify a subset of residues on a protein, which might interact with the presumed binding partner
Summary
A single interface prediction consists of a set of residues believed to constitute the binding site and those that do not. The main novelty of T-PIP is that is the homology between the query protein and its homologues considered and the diversity between the interacting partners of the homologues at each specific binding site In this category, the main attributes that appear to be contributing to the quality of predictions are the structure-based MSAs and the binding partner information. The main aim of methods that use no antibody information is to identify epitope-like sites on proteins as a means to improve vaccine design Their mode of operation is similar in nature to that of general protein–protein interface prediction introduced in the earlier sections. Antigenic epitopes are identified by performing simplified surface matching complemented by antibody-antigen-specific statistical scoring This method (44% recall at 14% precision) outperforms the antibody-ignoring Discotope (23% recall at 14% precision), demonstrating the value of introducing antibody information into predictions. A comprehensive study contrasting different epitopes on a single antigen (e.g. lysozyme) with respect to their binding antibodies could improve our understanding of the specificity of antibodies, providing ground for better epitope predictions
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