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Fragment‐HMM: A new approach to protein structure prediction
Author(s) -
Li Shuai Cheng,
Bu Dongbo,
Xu Jinbo,
Li Ming
Publication year - 2008
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.036442.108
Subject(s) - hidden markov model , fragment (logic) , computer science , benchmark (surveying) , protein structure prediction , position (finance) , selection (genetic algorithm) , simple (philosophy) , artificial intelligence , algorithm , machine learning , computational biology , data mining , pattern recognition (psychology) , protein structure , biology , geography , biochemistry , philosophy , geodesy , epistemology , finance , economics
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 Å 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.