Work

De novo protein conformational sampling using a probabilistic graphical model

Year: 2015

Type: article

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/.

Source: Scientific Reports

DOI: 10.1038/srep16332

Authors: Debswapna Bhattacharya, Jianlin Cheng

Institution: University of Missouri

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Institutes of Health, National Institute of General Medical Sciences