title: 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 identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.
authors.affiliation: Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Schneider
authors.affiliation: Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Brock
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D019143:Evolution, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D060388:Support Vector Machine
chemical_list: D011506:Proteins