Abstract
SH3 domains mediate signal transduction by recognizing short peptides. Understanding of the driving forces in peptide recognitions will help us to predict the binding specificity of the domain-peptide recognition and to understand the molecular interaction networks of cells. However, accurate calculation of the binding energy is a tough challenge. In this study, we propose three ideas for improving our ability to predict the binding energy between SH3 domains and peptides: (1) utilizing the structural ensembles sampled from a molecular dynamics simulation trajectory, (2) utilizing multiple peptide templates, and (3) optimizing the sequence-structure mapping. We tested these three ideas on ten previously studied SH3 domains for which SPOT analysis data were available. The results indicate that calculating binding energy using the structural ensemble was most effective, clearly increasing the prediction accuracy, while the second and third ideas tended to give better binding energy predictions. We applied our method to the five SH3 targets in DREAM4 Challenge and selected the best performing method.
Highlights
Peptide recognition domains (PRDs) recognize peptides and relay the signals
We developed three ideas: utilizing the structural ensemble sampled from a molecular dynamics simulation trajectory, using multiple peptide templates, and optimizing the sequence-structure mapping
We tested the validity of the three ideas which we expected would improve our ability to predict the binding energy between SH3 domains and peptides: (1) utilizing the structural ensembles sampled from a molecular dynamics (MD) simulation trajectory, (2) using multiple peptide templates and (3) optimizing the sequence-structure mapping
Summary
Peptide recognition domains (PRDs) recognize peptides and relay the signals. There are diverse PRDs that are involved in diverse signal transduction pathways. They work in a modular fashion [1,2], which implies that the interaction between peptides and PRDs can be dealt with separately Due to their importance in cellular signal transduction, numerous experimental and computational studies have been performed to characterize their binding specificity. A 3D QSAR method was used to predict the affinity of peptides on MHC [9] Physical energy terms, such as van der Waals, electrostatic, and desolvation energies, were used to predict the energy of domain peptide interactions [10]. Protein-protein interaction data such as yeast two-hybrid was used to generate probabilistic models that can predict peptide sequences binding to SH3 domains [14,15]
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