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Bioinformatics Advance Access published online on September 27, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl489
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received March 29, 2006
Revised August 18, 2006
Accepted September 21, 2006

Article

Support vector machines for prediction of dihedral angle regions

Olav Zimmermann 1 and Ulrich H. E. Hansmann 2 *

1 John v. Neumann Institute for Computing, FZ Jülich, 52425 Jülich, Germany
2 John v. Neumann Institute for Computing, FZ Jülich, 52425 Jülich, Germany; Dept. of Physics, Michigan Technological University, Houghton, MI 49931-1295, USA

* To whom correspondence should be addressed.
Ulrich H. E. Hansmann, E-mail: u.hansmann{at}fz-juelich.de


  Abstract

Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information.

Results: We propose a multi-step Support Vector Machine (SVM) procedure, DHPRED, to predict the dihedral angle state of residues from sequence. Trained on 20,000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs.

Associate Editor: Anna Tramontano
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