Fragment-HMM: a new approach to protein structure prediction.
We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 A of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%-94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better.
Li
Shuai Cheng
SC
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario N2L3G1, Canada.
Bu
Dongbo
D
Xu
Jinbo
J
Li
Ming
M
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008390:Markov Chains; D008956:Models, Chemical; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software