Support vector machines for prediction of dihedral angle regions
Document Type
Article
Publication Date
12-15-2006
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, 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. © 2006 Oxford University Press.
Publication Title
Bioinformatics
Recommended Citation
Zimmermann, O.,
&
Hansmann, U.
(2006).
Support vector machines for prediction of dihedral angle regions.
Bioinformatics,
22(24), 3009-3015.
http://doi.org/10.1093/bioinformatics/btl489
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/9776