Campus Units

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

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

5-26-2005

Journal or Book Title

Polymer

Volume

46

Issue

12

First Page

4314

Last Page

4321

DOI

10.1016/j.polymer.2005.02.040

Abstract

A new method for predicting protein secondary structure from amino acid sequence has been developed. The method is based on multiple sequence alignment of the query sequence with all other sequences with known structure from the protein data bank (PDB) by using BLAST. The fragments of the alignments belonging to proteins from the PBD are then used for further analysis. We have studied various schemes of assigning weights for matching segments and calculated normalized scores to predict one of the three secondary structures: α-helix, β-sheet, or coil. We applied several artificial intelligence techniques: decision trees (DT), neural networks (NN) and support vector machines (SVM) to improve the accuracy of predictions and found that SVM gave the best performance. Preliminary data show that combining the fragment mining approach with GOR V (Kloczkowski et al, Proteins 49 (2002) 154–166) for regions of low sequence similarity improves the prediction accuracy.

Comments

This is a manuscript of an article published as Cheng, Haitao, Taner Z. Sen, Andrzej Kloczkowski, Dimitris Margaritis, and Robert L. Jernigan. "Prediction of protein secondary structure by mining structural fragment database." Polymer 46, no. 12 (2005): 4314-4321. doi: 10.1016/j.polymer.2005.02.040. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd

Language

en

File Format

application/pdf

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