title: Fragment-HMM: a new approach to protein structure prediction.
abstract: 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.
authors: David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario N2L3G1, Canada.
authors: Shuai Cheng
authors: SC
authors: Li
authors: Dongbo
authors: D
authors: Bu
authors: Jinbo
authors: J
authors: Xu
authors: Ming
authors: M
authors: Li
mesh_terms: 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