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Fold recognition using predicted secondary structure sequences and hidden Markov models of protein folds
Author(s) -
Di Francesco Valentina,
Geetha V.,
Garnier Jean,
Munson Peter J.
Publication year - 1997
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/(sici)1097-0134(1997)1+<123::aid-prot16>3.0.co;2-q
Subject(s) - hidden markov model , protein structure prediction , protein secondary structure , fold (higher order function) , computational biology , protein structure , markov chain , threading (protein sequence) , casp , computer science , loop modeling , artificial intelligence , pattern recognition (psychology) , biology , machine learning , biochemistry , programming language
We present an analysis of the blind predictions submitted to the fold recognition category for the second meeting on the Critical Assessment of techniques for protein Structure Prediction. Our method achieves fold recognition from predicted secondary structure sequences using hidden Markov models (HMMs) of protein folds. HMMs are trained only with experimentally derived secondary structure sequences of proteins having similar fold, therefore protein structures are described by the models at a remarkably simplified level. We submitted predictions for five target sequences, of which four were later found to be suitable for threading. Our approach correctly predicted the fold for three of them. For a fourth sequence the fold could have been correctly predicted if a better model for its structure was available. We conclude that we have additional evidence that secondary structure information represents an important factor for achieving fold recognition. Proteins, Suppl. 1:123–128, 1997. Published 1998 Wiley‐Liss, Inc. This article is a US government work and, as such, is in the public domain in the United States of America.