Abstract

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.

Keywords

Hidden Markov modelProtein structure predictionProtein secondary structureFold (higher-order function)Computational biologyProtein structureMarkov chainThreading (protein sequence)CASPComputer scienceLoop modelingArtificial intelligencePattern recognition (psychology)BiologyMachine learningBiochemistry

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Publication Info

Year
1997
Type
article
Volume
29
Issue
S1
Pages
123-128
Citations
34
Access
Closed

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Valentina Di Francesco, V. Geetha, Jean Garnier et al. (1997). Fold recognition using predicted secondary structure sequences and hidden Markov models of protein folds. Proteins Structure Function and Bioinformatics , 29 (S1) , 123-128. https://doi.org/10.1002/(sici)1097-0134(1997)1+<123::aid-prot16>3.0.co;2-q

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DOI
10.1002/(sici)1097-0134(1997)1+<123::aid-prot16>3.0.co;2-q