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Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

Kountouris, Peter; Hirst, J.D.

Authors

Peter Kountouris



Abstract

Background: β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.

Results: We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and “non-specific”, achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods.

Conclusions: We have created an accurate predictor of b-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Citation

Kountouris, P., & Hirst, J. (2010). Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures. BMC Bioinformatics, 11,

Journal Article Type Article
Publication Date Jan 1, 2010
Deposit Date Jan 26, 2011
Publicly Available Date Jan 26, 2011
Journal BMC Bioinformatics
Electronic ISSN 1471-2105
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 11
Public URL http://eprints.nottingham.ac.uk/id/eprint/1426
Publisher URL http://dx.doi.org/10.1186/1471-2105-11-407
Related Public URLs http://comp.chem.nottingham.ac.uk/debt/
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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