Journal Article

PreJuSER-54190

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Support Vector Machines for Prediction of Dihedral Angle Regions

Zimmermann, O.FZJ* ; Hansmann, U. H. E.FZJ*

2006
Oxford University Press Oxford

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Please use a persistent id in citations: doi:10.1093/bioinformatics/btl489

Abstract: 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.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.DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb

Keyword(s): Algorithms (MeSH) ; Amino Acid Sequence (MeSH) ; Artificial Intelligence (MeSH) ; Computer Simulation (MeSH) ; Models, Chemical (MeSH) ; Models, Molecular (MeSH) ; Molecular Sequence Data (MeSH) ; Pattern Recognition, Automated: methods (MeSH) ; Protein Structure, Secondary (MeSH) ; Proteins: chemistry (MeSH) ; Proteins: ultrastructure (MeSH) ; Sequence Alignment: methods (MeSH) ; Sequence Analysis, Protein: methods (MeSH) ; Proteins ; J

Note: Record converted from VDB: 12.11.2012

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Contributing Institute(s):

1. John von Neumann - Institut für Computing (NIC)

Research Program(s):

1. Scientific Computing (P41)

Appears in the scientific report 2006

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