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| Hauptseite > Publikationsdatenbank > LOCUSTRA: Accurate Prediction of Local Protein Structure Using a Two-Layer Support Vector Machine Approach |
| Hauptseite > Publikationsdatenbank > LOCUSTRA: Accurate Prediction of Local Protein Structure Using a Two-Layer Support Vector Machine Approach |
| Journal Article | PreJuSER-1115 |
;
2008
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Please use a persistent id in citations: doi:10.1021/ci800178a
Abstract: 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.
Keyword(s): Algorithms (MeSH) ; Computer Simulation (MeSH) ; Databases, Factual (MeSH) ; Models, Biological (MeSH) ; Models, Molecular (MeSH) ; Peptidyl Transferases: chemistry (MeSH) ; Predictive Value of Tests (MeSH) ; Protein Structure, Tertiary (MeSH) ; Proteins: chemistry (MeSH) ; Quantitative Structure-Activity Relationship (MeSH) ; Streptomyces: enzymology (MeSH) ; Proteins ; Peptidyl Transferases ; J
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