Abstract
Peptides mediate up to 40 % of protein–protein interactions in a variety of cellular processes and are also attractive drug candidates. Thus, predicting peptide binding sites on the given protein structure is of great importance for mechanistic investigation of protein–peptide interactions and peptide therapeutics development. In this chapter, we describe the usage of our web server, referred to as ACCLUSTER, for peptide binding site prediction for a given protein structure. ACCLUSTER is freely available for users without registration at http://zougrouptoolkit.missouri.edu/accluster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Petsalaki E, Russell RB (2008) Peptide-mediated interactions in biological systems: new discoveries and applications. Curr Opin Biotechnol 19:344–350
Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450:1001–1009
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242
Yan C, Zou X (2015) Predicting peptide binding sites on protein surfaces by clustering chemical interactions. J Comput Chem 36:49–61
Chen R, Li L, Weng, Z (2003) ZDOCK: an initial-stage protein-docking algorithm. Proteins 52:80–87
Pierce BG, Hourai Y, Weng, Z (2011) Accelerating protein docking in ZDOCK using an advanced 3D convolution library. PLoS One 6:e24657
Huang S-Y, Zou X (2008) An iterative knowledge-based scoring function for protein-protein recognition. Proteins 72:557–579
Ester M, Kriegel HP, Sander J, Xu, X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD, vol 96, pp 226–231
Rego N, Koes, D (2014) 3Dmol.js: molecular visualization with WebGL. Bioinformatics 31:1322–1324. doi:10.1093/bioinformatics/btu829
Sheng Y, Saridakis V, Sarkari F, Duan S, Wu T, Arrowsmith CH, Frappier, L (2006) Molecular recognition of p53 and MDM2 by USP7/HAUSP. Nat Struct Mol Biol 13:285–291
Hu M, Gu L, Li M, Jeffrey PD, Gu W, Shi, Y (2006) Structural basis of competitive recognition of p53 and MDM2 by HAUSP/USP7: implications for the regulation of the p53–MDM2 pathway. PLoS Biol 4:e27
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera–a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612
Acknowledgements
This work is supported by NSF CAREER Award DBI-0953839 and the NIH R01GM109980 (Xiaoqin Zou). The computations were performed on the high performance computing infrastructure supported by NSF CNS-1429294 (PI: Chi-Ren Shyu) and the HPC resources supported by the University of Missouri Bioinformatics Consortium (UMBC).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Yan, C., Xu, X., Zou, X. (2017). The Usage of ACCLUSTER for Peptide Binding Site Prediction. In: Schueler-Furman, O., London, N. (eds) Modeling Peptide-Protein Interactions. Methods in Molecular Biology, vol 1561. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6798-8_1
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6798-8_1
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-6796-4
Online ISBN: 978-1-4939-6798-8
eBook Packages: Springer Protocols