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This Article ACCEPTED PREPRINT Full Text (PDF) All Versions of this Article: ps.036442.108v1 ps.036442.108v2 17/11/1925    most recent Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Li, S. C. Articles by Li, M. PubMed PubMed Citation Articles by Li, S. C. Articles by Li, M. Social Bookmarking                   What's this?

Fragment-HMM: A New Approach To Protein Structure Prediction

¹ School of Computer Science, University of Waterloo;
² Toyota Technological Institute at Chicago

(RECEIVED May 20, 2008; ACCEPTED July 22, 2008)

We design 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 Angstrom of the native structures for 100% decoys on all 6 standard benchmark proteins used in ROSETTA which achieved only 14% to 94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better.

Keywords: Protein Structure/Folding; Computational Analysis of Protein Structure; Hidden Markov Model; Iteration; Primal_Dual; Sampling

³ E-mail: mli{at}cs.uwaterloo.ca

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