Template-Based Prediction of Protein-Peptide Interactions by Using GalaxyPepDock.

Abstract

We introduce a web server called GalaxyPepDock that predicts protein-peptide interactions based on templates. With the continuously increasing size of the protein structure database, the probability of finding related proteins for templates is increasing. GalaxyPepDock takes a protein structure and a peptide sequence as input and returns protein-peptide complex structures as output. Templates for protein-peptide complex structures are selected from the structure database considering similarity to the target protein structure and to putative protein-peptide interactions as estimated by protein structure alignment and peptide sequence alignment. Complex structures are then built from the template structures by template-based modeling. By further structure refinement that performs energy-based optimization, structural aspects that are missing in the template structures or that are not compatible with the given protein and peptide are refined. During the refinement, flexibilities of both protein and peptide induced by binding are considered. The atomistic protein-peptide interactions predicted by GalaxyPepDock can offer important clues for designing new peptides with desired binding properties.