MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory

Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.Author summary: Across biology, many proteins that recognize peptides are multispecific; they interact with multiple binding partners of disparate sequence. Computational prediction of these multiple peptide partners would enable greater understanding of individual protein-recognition domains. Additionally, the ability to customize protein-recognition domains by designing them to recognize and act upon a new set of peptides and not bind their original binding partners would be useful in drug design and biotechnology. Current methods for predicting multispecificity operate on a timescale that is too slow to be used for design. Here, we present a method, MFPred, for predicting multispecificity. MFPred robustly recapitulates protein-recognition domain specificity for a range of proteins, at comparable accuracy and with considerable speed-up relative to current methods. We apply MFPred to predicting altered multispecificity in a mutant protease to demonstrate its relevance to design. The rapidity and accuracy of MFPred should enable its use in investigating and modulating biological processes.