LOCUSTRA: Accurate prediction of local protein structure using a two-layer support vector machine approach
Constraint generation for 3d structure prediction and structure-based database searches benefit from finegrained 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 φ(ψ) error of 24.74°(38.35°) and a median RMSDA (root-mean-square deviation of the (dihedral) angles) per protein chain of 52.1°. 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. © 2008 American Chemical Society.
Journal of Chemical Information and Modeling
LOCUSTRA: Accurate prediction of local protein structure using a two-layer support vector machine approach.
Journal of Chemical Information and Modeling,
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