CRHunter: integrating multifaceted information to predict catalytic residues in enzymes.

ABSTRACT: A variety of algorithms have been developed for catalytic residue prediction based on either feature- or template-based methodology. However, no studies have systematically compared these two strategies and further considered whether their combination could improve the prediction performance. Herein, we developed an integrative algorithm named CRHunter by simultaneously using the complementarity between feature- and template-based methodologies and that between structural and sequence information. Several novel structural features were generated by the Delaunay triangulation and Laplacian transformation of enzyme structures. Combining these features with traditional descriptors, we invented two support vector machine feature predictors based on both structural and sequence information. Fur

SUBMITTER: Sun J 

PROVIDER: S-EPMC5036049 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature