Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins

TitleStrand-loop-strand motifs: prediction of hairpins and diverging turns in proteins
Publication TypeJournal Article
Year of Publication2004
AuthorsKuhn, M., Meiler J., & Baker D.
JournalProteins
Volume54
Issue2
Pagination282-8
Date Published2004 Feb 1
ISSN1097-0134
KeywordsAlgorithms, Amino Acid Motifs, Computational Biology, Computer Simulation, Databases, Protein, Hydrogen Bonding, Internet, Models, Molecular, Neural Networks (Computer), Primary Publication, Protein Structure, Secondary, Proteins, Sensitivity and Specificity, Software
Abstract

Beta-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent beta-strands into beta-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of beta-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 +/- 4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3 +/- 6.1%, diverging turns with an accuracy of 73.9 +/- 6.0%. Incorporation of the beta-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-beta-proteins and 3 alphabeta-proteins. The beta-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003; www.jens-meiler.de/turnpred.html).

Alternate JournalProteins
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