Fine‐grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple‐parameter sets

Chin‐Sheng Yu; Jung‐Ying Wang; Jinn‐Moon Yang; Ping‐Chiang Lyu; Chih‐Jen Lin; Jenn‐Kang Hwang

doi:10.1002/prot.10313

journal article Open Access Jan 29, 2003

Fine‐grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple‐parameter sets

Chin‐Sheng Yu Jung‐Ying Wang Jinn‐Moon Yang Ping‐Chiang Lyu Chih‐Jen Lin Jenn‐Kang Hwang

Proteins: Structure, Function, and Bioinformatics Vol. 50 No. 4 pp. 531-536 · Wiley

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Abstract

AbstractIn the coarse‐grained fold assignment of major protein classes, such as all‐α, all‐β, α + β, α/β proteins, one can easily achieve high prediction accuracy from primary amino acid sequences. However, the fine‐grained assignment of folds, such as those defined in the Structural Classification of Proteins (SCOP) database, presents a challenge due to the larger amount of folds available. Recent study yielded reasonable prediction accuracy of 56.0% on an independent set of 27 most populated folds. In this communication, we apply the support vector machine (SVM) method, using a combination of protein descriptors based on the properties derived from the composition of n‐peptide and jury voting, to the fine‐grained fold prediction, and are able to achieve an overall prediction accuracy of 69.6% on the same independent set—significantly higher than the previous results. On 10‐fold cross‐validation, we obtained a prediction accuracy of 65.3%. Our results show that SVM coupled with suitable global sequence‐coding schemes can significantly improve the fine‐grained fold prediction. Our approach should be useful in structure prediction and modeling. Proteins 2003;50:531–536. © 2003 Wiley‐Liss, Inc.

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References

Details

Published: Jan 29, 2003
Vol/Issue: 50(4)
Pages: 531-536
License: View

Authors

Cite This Article

Chin‐Sheng Yu, Jung‐Ying Wang, Jinn‐Moon Yang, et al. (2003). Fine‐grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple‐parameter sets. Proteins: Structure, Function, and Bioinformatics, 50(4), 531-536. https://doi.org/10.1002/prot.10313

Fine‐grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple‐parameter sets

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