Improved residue contact prediction using support vector machines and a large feature set.

ABSTRACT:

Background

Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved.

Results

Here we develop a new contact map predictor (SVMcon) that uses support vector machines to predict medium- and long-range contacts. SVMcon integrates profiles, secondary structure, relative solvent accessibility, contact potentials, and other useful features. On the same test data set, SVMcon's accuracy is 4% higher than the latest version of the CMAPpro contact map predictor. SVMcon recently participated in the seventh edition of the Critical Assessment of Techniques for Protein Structure Predicti

SUBMITTER: Cheng J 

PROVIDER: S-EPMC1852326 | biostudies-literature | 2007 Apr

REPOSITORIES: biostudies-literature