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BMC Bioinformatics. 2009 Dec 22;10:437. doi: 10.1186/1471-2105-10-437.

Prediction of backbone dihedral angles and protein secondary structure using support vector machines.

Author information

1
School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk

Abstract

BACKGROUND:

The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.

RESULTS:

We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.

CONCLUSIONS:

We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

PMID:
20025785
PMCID:
PMC2811710
DOI:
10.1186/1471-2105-10-437
[Indexed for MEDLINE]
Free PMC Article

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