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- W2102284736 abstract "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 the present study, we developed a novel computational approach, referred to as ACCLUSTER, for predicting the peptide binding sites on protein surfaces. Specifically, we use the twenty 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 (DBSCAN) 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 non-redundant 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 small molecular binding site prediction and protein-protein interface prediction. Notably, ACCLUSTER is based on physical interactions rather than informatics training, and can easily be extended to other macromolecular systems, including systems like peptide-RNA complexes which lack sufficient structural data and therefore pose challenges to machine-learning based methods." @default.
- W2102284736 created "2016-06-24" @default.
- W2102284736 creator A5053084378 @default.
- W2102284736 creator A5081082371 @default.
- W2102284736 date "2015-01-01" @default.
- W2102284736 modified "2023-09-28" @default.
- W2102284736 title "Predicting Peptide Binding Sites on Protein Surfaces by Clustering Chemical Interactions" @default.
- W2102284736 doi "https://doi.org/10.1016/j.bpj.2014.11.1189" @default.
- W2102284736 hasPublicationYear "2015" @default.
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