A machine learning approach for the prediction of protein surface loop flexibility.

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 dis

SUBMITTER: Hwang H 

PROVIDER: S-EPMC3341935 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature