Campus Units

Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Bioinformatics and Computational Biology, Baker Center for Bioinformatics and Biological Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

10-1-2007

Journal or Book Title

Bioinformatics

Volume

23

Issue

19

First Page

2628

Last Page

2630

DOI

10.1093/bioinformatics/btm379

Abstract

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, the 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 the 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.

Comments

This is a manuscript of an article published as Cheng, Haitao, Taner Z. Sen, Robert L. Jernigan, and Andrzej Kloczkowski. "Consensus data mining (CDM) protein secondary structure prediction server: combining GOR V and fragment database mining (FDM)." Bioinformatics 23, no. 19 (2007): 2628-2630. doi:10.1093/bioinformatics/btm379. Posted with permission.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Published Version

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