A machine learning approach for the prediction of protein surface loop flexibility
UMass Chan Affiliations
Program in Bioinformatics and Integrative BiologyDepartment of Biochemistry and Molecular Pharmacology
Document Type
Journal ArticlePublication Date
2011-08-01Keywords
*Artificial IntelligenceProtein Structure, Secondary
Proteins
Amino Acids, Peptides, and Proteins
Bioinformatics
Computational Biology
Systems Biology
Metadata
Show full item recordAbstract
Proteins often undergo conformational changes when binding to each other. A major fraction of backbone conformational changes involves motion on the protein surface, particularly in loops. Accounting for the motion of protein surface loops represents a challenge for protein-protein docking algorithms. A first step in addressing this challenge is to distinguish protein surface loops that are likely to undergo backbone conformational changes upon protein-protein binding (mobile loops) from those that are not (stationary loops). In this study, we developed a machine learning strategy based on support vector machines (SVMs). Our SVM uses three features of loop residues in the unbound protein structures-Ramachandran angles, crystallographic B-factors, and relative accessible surface area-to distinguish mobile loops from stationary ones. This method yields an average prediction accuracy of 75.3% compared with a random prediction accuracy of 50%, and an average of 0.79 area under the receiver operating characteristic (ROC) curve using cross-validation. Testing the method on an independent dataset, we obtained a prediction accuracy of 70.5%. Finally, we applied the method to 11 complexes that involve members from the Ras superfamily and achieved prediction accuracy of 92.8% for the Ras superfamily proteins and 74.4% for their binding partners.Source
Proteins. 2011 Aug;79(8):2467-74. doi: 10.1002/prot.23070. Epub 2011 Jun 1. Link to article on publisher's site
DOI
10.1002/prot.23070Permanent Link to this Item
http://hdl.handle.net/20.500.14038/25883PubMed ID
21633973Related Resources
ae974a485f413a2113503eed53cd6c53
10.1002/prot.23070