Combining physicochemical and evolutionary information for protein contact prediction.

ABSTRACT: We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to

SUBMITTER: Schneider M 

PROVIDER: S-EPMC4206277 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature