Peter Kountouris
Predicting ?-turns and their types using predicted backbone dihedral angles and secondary structures
Kountouris, Peter; Hirst, J.D.
Authors
Professor JONATHAN HIRST JONATHAN.HIRST@NOTTINGHAM.AC.UK
Professor of Computational Chemistry
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 ?-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 | Springer Verlag |
Peer Reviewed | Peer Reviewed |
Volume | 11 |
Public URL | https://nottingham-repository.worktribe.com/output/1012802 |
Publisher URL | http://dx.doi.org/10.1186/1471-2105-11-407 |
Related Public URLs | http://comp.chem.nottingham.ac.uk/debt/ |
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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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