TY - JOUR
AU - Zimmermann, O.
AU - Hansmann, U. H. E.
TI - LOCUSTRA: Accurate Prediction of Local Protein Structure Using a Two-Layer Support Vector Machine Approach
JO - Journal of Chemical Information and Modeling
VL - 48
SN - 1549-9596
M1 - PreJuSER-1115
SP - 1903 - 1908
PY - 2008
N1 - Record converted from VDB: 12.11.2012
AB - 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.
KW - Algorithms
KW - Computer Simulation
KW - Databases, Factual
KW - Models, Biological
KW - Models, Molecular
KW - Peptidyl Transferases: chemistry
KW - Predictive Value of Tests
KW - Protein Structure, Tertiary
KW - Proteins: chemistry
KW - Quantitative Structure-Activity Relationship
KW - Streptomyces: enzymology
KW - Proteins (NLM Chemicals)
KW - Peptidyl Transferases (NLM Chemicals)
KW - J (WoSType)
LB - PUB:(DE-HGF)16
C6 - pmid:18763837
UR - <Go to ISI:>//WOS:000259398500016
DO - DOI:10.1021/ci800178a
UR - https://juser.fz-juelich.de/record/1115
ER -