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

PLoS Comput Biol. 2017 Jun 26;13(6):e1005614. doi: 10.1371/journal.pcbi.1005614. eCollection 2017 Jun.


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.

MeSH terms

  • Algorithms*
  • Binding Sites
  • Computer Simulation
  • Models, Chemical*
  • Models, Molecular
  • Peptides / chemistry*
  • Protein Binding
  • Protein Interaction Mapping / methods*
  • Proteins / chemistry*
  • Proteins / ultrastructure
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Analysis, Protein / methods*
  • Software


  • Peptides
  • Proteins

Grants and funding

This work was funded by National Science Foundation grant to SDK (Grant MCB1330760). Additionally, this material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1433187 (ABR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.