Bioinformatics Advance Access originally published online on September 27, 2006
Bioinformatics 2006 22(24):3009-3015; doi:10.1093/bioinformatics/btl489
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Support vector machines for prediction of dihedral angle regions
1 John v. Neumann Institute for Computing, FZ Jülich 52425 Jülich, Germany
2 Department of Physics, Michigan Technological University Houghton, MI 49931-1295, USA
*To whom correspondence should be addressed.
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, dihedral prediction (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.
Availability: DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb
Contact: u.hansmann{at}fz-juelich.de
Received on March 29, 2006; revised on August 18, 2006; accepted on September 21, 2006
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