Predicting peptide binding sites on protein surfaces by clustering chemical interactions

Short peptides play important roles in cellular processes including signal transduction, immune response, and transcription regulation. Correct identification of the peptide binding site on a given protein surface is of great importance not only for mechanistic investigation of these biological processes but also for therapeutic development. In this study, we developed a novel computational approach, referred to as ACCLUSTER, for predicting the peptide binding sites on protein surfaces. Specifically, we use the 20 standard amino acids as probes to globally scan the protein surface. The poses forming good chemical interactions with the protein are identified, followed by clustering with the density-based spatial clustering of applications with noise technique. Finally, these clusters are ranked based on their sizes. The cluster with the largest size is predicted as the putative binding site. Assessment of ACCLUSTER was performed on a diverse test set of 251 nonredundant protein-peptide complexes. The results were compared with the performance of POCASA, a pocket detection method for ligand binding site prediction. Peptidb, another protein-peptide database that contains both bound structures and unbound or homologous structures was used to test the robustness of ACCLUSTER. The performance of ACCLUSTER was also compared with PepSite2 and PeptiMap, two recently developed methods developed for identifying peptide binding sites. The results showed that ACCLUSTER is a promising method for peptide binding site prediction. Additionally, ACCLUSTER was also shown to be applicable to nonpeptide ligand binding site prediction.