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/.
Highlights
FUSION, captures local relationships between protein sequence and structural features and allows for probabilistic sampling of conformational space of the protein backbone in full-atomic detail from a continuous space different from the discrete space of fragment assembly
The hidden node (H) is a discrete node that can adopt 30 states where, each of these states specifies which mixture component is chosen among the possible emission distributions
To gain additional insights into the nature of the decoy population, especially for larger proteins, we examined the Gaussian kernel density estimation for the accuracy of decoys generated by FUSION
Summary
FUSION, captures local relationships between protein sequence and structural features and allows for probabilistic sampling of conformational space of the protein backbone in full-atomic detail (i.e., at the same granularity as fragment assembly) from a continuous space different from the discrete space of fragment assembly. Output (i.e., emission) nodes correspond to structural space, modeled using secondary structure (S), dihedral angle pair (D: φ , ψ ), and peptide bond conformation (P: ω ).
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