Abstract
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/.
Highlights
Protein-protein interactions are a fundamental part of all major biological processes
InterPep has proven a powerful tool for protein-peptide interaction site prediction
We show that a majority (81.3%) of protein-peptide interactions in the test set have structural templates without significant sequence similarity, and that a large proportion (67.7%) of the protein-peptide interactions have templates from protein-protein interactions
Summary
Protein-protein interactions are a fundamental part of all major biological processes. A interesting class of protein-protein interactions are those involving interaction including intrinsically disordered regions These regions are often the size of small peptide fragments 5 to 25 residues long[1] and part of proteins involved in regulation, recognition, and signaling requiring dynamic and specific responses[2,3]. The sampling problem can remedied to some degree by coarse-graining using side-chain centroids like in Rosetta FlexPepDock[25] or the CABS force field in CABS-dock[27] Another way to tackle the sampling problem is to first predict the peptide binding site on the protein surface[28,29,30,31,32]. PeptiMap[30] predicts peptide binding by adapting the fragment mapping (FTmap) for ligand peptide binding site prediction[33] to peptide binding characteristics, PEP-SiteFinder[31] combines peptide 3D conformation generation and fast rigid body docking, and ACCLUSTER32 clusters surface residues that have good chemical interactions with amino acid probes
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