Abstract
Motivation: Design of protein-protein interaction (PPI) inhibitors is a key challenge in Structural Bioinformatics and Computer Aided Drug Design. Peptides, which partially mimic the interface area of one of the interacting proteins, are natural candidates to form protein-peptide complexes competing with the original PPI. The prediction of such complexes is especially challenging due to the high flexibility of peptide conformations.

Results: In this article we present PepCrawler, a new tool for deriving binding peptides from protein-protein complexes and prediction of peptide-protein complexes, by performing high-resolution docking refinement and estimation of binding-affinity. By using a fast path planning approach, PepCrawler rapidly generates large amounts of flexible peptide conformations, allowing backbone and side-chain flexibility. A newly introduced binding energy funnel “steepness score” was applied for the evaluation of the protein-peptide complexes binding-affinity. PepCrawler simulations predicted high binding- affinity for native protein-peptide complexes benchmark and low affinity for low-energy decoy complexes. In three cases, where wetlab data is available, the PepCrawler predictions were consistent with the data. Comparing to other state of the art flexible peptide-protein structure prediction algorithms, our algorithm is very fast, and takes only minutes to run on a single PC.

Availability: http://bioinfo3d.cs.tau.ac.il/PepCrawler/
Contact: eladdons@tau.ac.il, wolfson@tau.ac.il
From Bioinformatics