%0 Journal Article
%A Zimmermann, O.
%A Hansmann, U. H. E.
%T LOCUSTRA: Accurate Prediction of Local Protein Structure Using a Two-Layer Support Vector Machine Approach
%J Journal of Chemical Information and Modeling
%V 48
%@ 1549-9596
%M PreJuSER-1115
%P 1903 - 1908
%D 2008
%Z Record converted from VDB: 12.11.2012
%X Constraint generation for 3d structure prediction and structure-based database searches benefit from fine-grained prediction of local structure. In this work, we present LOCUSTRA, a novel scheme for the multiclass prediction of local structure that uses two layers of support vector machines (SVM). Using a 16-letter structural alphabet from de Brevern et al. (Proteins: Struct., Funct., Bioinf. 2000, 41, 271-287), we assess its prediction ability for an independent test set of 222 proteins and compare our method to three-class secondary structure prediction and direct prediction of dihedral angles. The prediction accuracy is Q16=61.0% for the 16 classes of the structural alphabet and Q3=79.2% for a simple mapping to the three secondary classes helix, sheet, and coil. We achieve a mean phi(psi) error of 24.74 degrees (38.35 degrees) and a median RMSDA (root-mean-square deviation of the (dihedral) angles) per protein chain of 52.1 degrees. These results compare favorably with related approaches. The LOCUSTRA web server is freely available to researchers at http://www.fz-juelich.de/nic/cbb/service/service.php.
%K Algorithms
%K Computer Simulation
%K Databases, Factual
%K Models, Biological
%K Models, Molecular
%K Peptidyl Transferases: chemistry
%K Predictive Value of Tests
%K Protein Structure, Tertiary
%K Proteins: chemistry
%K Quantitative Structure-Activity Relationship
%K Streptomyces: enzymology
%K Proteins (NLM Chemicals)
%K Peptidyl Transferases (NLM Chemicals)
%K J (WoSType)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:18763837
%U <Go to ISI:>//WOS:000259398500016
%R 10.1021/ci800178a
%U https://juser.fz-juelich.de/record/1115