A machine learning approach for the prediction of protein surface loop flexibility
- 29 April 2011
- journal article
- research article
- Published by Wiley in Proteins-Structure Function and Bioinformatics
- Vol. 79 (8), 2467-2474
- https://doi.org/10.1002/prot.23070
Abstract
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. Proteins 2011;Keywords
This publication has 32 references indexed in Scilit:
- Blind predictions of protein interfaces by docking calculations in CAPRIProteins-Structure Function and Bioinformatics, 2010
- Principles of flexible protein–protein dockingProteins-Structure Function and Bioinformatics, 2008
- Ordered conformational change in the protein backbone: Prediction of conformationally variable positions from sequence and low-resolution structural dataProteins-Structure Function and Bioinformatics, 2008
- Docking and scoring protein complexes: CAPRI 3rd EditionProteins-Structure Function and Bioinformatics, 2007
- Flexible protein–protein dockingCurrent Opinion in Structural Biology, 2006
- On residues in the disallowed region of the Ramachandran mapPeptide Science, 2002
- Skewed distribution of protein secondary structure contents over the conformational triangleProtein Engineering, Design and Selection, 1999
- The atomic structure of protein-protein recognition sitesJournal of Molecular Biology, 1999
- Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling toolJournal of Molecular Biology, 1997
- Backbone-dependent Rotamer Library for Proteins Application to Side-chain PredictionJournal of Molecular Biology, 1993