title: De novo protein conformational sampling using a probabilistic graphical model.
abstract: Efficient exploration of protein conformational space remains challenging especially for large proteins when assembling discretized structural fragments extracted from a protein structure data database. We propose a fragment-free probabilistic graphical model, FUSION, for conformational sampling in continuous space and assess its accuracy using 'blind' protein targets with a length up to 250 residues from the CASP11 structure prediction exercise. The method reduces sampling bottlenecks, exhibits strong convergence, and demonstrates better performance than the popular fragment assembly method, ROSETTA, on relatively larger proteins with a length of more than 150 residues in our benchmark set. FUSION is freely available through a web server at http://protein.rnet.missouri.edu/FUSION/.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Debswapna
authors.initials: D
authors.lastname: Bhattacharya
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Informatics Institute, University of Missouri, Columbia, MO 65211, USA.|Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D003196:Computer Graphics; D008958:Models, Molecular; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins