Campus Units

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

Document Type

Article

Publication Version

Published Version

Publication Date

2008

Journal or Book Title

BMC Bioinformatics

Volume

9

Issue

1

First Page

487

DOI

10.1186/1471-2105-9-487

Abstract

Background

By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.

Results

First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.

Conclusion

By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.

Comments

This article is published as Peto, Myron, Andrzej Kloczkowski, Vasant Honavar, and Robert L. Jernigan. "Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable." BMC bioinformatics 9, no. 1 (2008): 487. doi: 10.1186/1471-2105-9-487. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Peto et al

Language

en

File Format

application/pdf

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