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BMCBI
2007

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

10 years 4 months ago
Improved residue contact prediction using support vector machines and a large feature set
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 Prediction (CASP7) experiment and was evaluated along with seven other contact map predictors. SVMcon was ranked as one of the top predictors, yielding the second best coverage and accuracy for contacts with sequence ...
Jianlin Cheng, Pierre Baldi
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Jianlin Cheng, Pierre Baldi
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