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Bioinformatics Advance Access published online on July 27, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm379
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Consensus Data Mining (CDM) Protein Secondary Structure Prediction Server: Combining GOR V and Fragment Database Mining (FDM)

Haitao Cheng 1,2, Taner Z. Sen *,1,3, Robert L. Jernigan 1,3 and Andrzej Kloczkowski 1,3

1Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011, USA, 2Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, 50011, USA, 3L .H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA

*To whom correspondence should be addressed. Dr. Taner Z. Sen, E-mail: taner{at}iastate.edu


  Abstract

Summary: One of the challenges in protein secondary structure prediction is to overcome the cross-validated 80% prediction accuracy barrier. Here, we propose a novel approach to surpass this barrier. Instead of using a single algorithm that relies on a limited data set for training, we combine two complementary methods having different strengths: Fragment Database Mining (FDM) and GOR V. FDM harnesses the availability of the known protein structures in the Protein Data Bank and provides highly accurate secondary structure predictions when sequentially similar structural fragments are identified. In contrast, GOR V algorithm is based on information theory, Bayesian statistics, and PSI-BLAST multiple sequence alignments to predict the secondary structure of residues inside a sliding window along a protein chain. A combination of these two different methods benefits from a large number of structures in the PDB and significantly improves the secondary structure prediction accuracy, resulting in Q3 ranging from 67.5 to 93.2%, depending on the availability of highly similar fragments in the Protein Data Bank.

Availability: The CDM server is freely accessible to public users and private institutions at http://gor.bb.iastate.edu/cdm.

Contact: e-mail: taner{at}iastate.edu, phone: +1 (515) 294-7259, fax: +1 (515) 294-3841

Associate Editor: Prof. Anna Tramontano

Received on May 15, 2007; revised on July 6, 2007; accepted on July 14, 2007

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