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Fold recognition and ab initio structure predictions using hidden markov models and β‐strand pair potentials
Author(s) -
Hubbard Tim J.,
Park J.
Publication year - 1995
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.340230313
Subject(s) - hidden markov model , ab initio , markov chain , topology (electrical circuits) , computational biology , sequence (biology) , threading (protein sequence) , homology (biology) , computer science , artificial intelligence , algorithm , protein structure , mathematics , biology , physics , genetics , combinatorics , machine learning , gene , biochemistry , quantum mechanics
Protein structure predictions were submitted for 9 of the target sequences in the competition that ran during 1994. Targets sequences were selected that had no known homology with any sequence of known structure and were members of a reasonably sized family of related but divergent sequences. The objective was either to recognize a compatible fold for the target sequence in the database of known structures or to predict ab initio its rough 3D topology. The main tools used were Hidden Markov models (HMM) for fold recognition, a β‐ strand pair potential to predict β‐sheet topology, and the PHD server for secondary structure prediction. Compatible folds were correctly identified in a number of cases and the β‐strand pair potential was shown to be a useful tool for ab initio topology prediction. © 1995 Wiley‐Liss, Inc.