標題: Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets
作者: Yu, CS
Wang, JY
Yang, JM
Lyu, PC
Lin, CJ
Hwang, JK
生物科技學系
Department of Biological Science and Technology
關鍵字: support vector machines;fine-grained fold prediction;global sequence-coding scheme;n-peptide
公開日期: 1-Mar-2003
摘要: In the coarse-grained fold assignment of major protein classes, such as all-alpha, all-beta, alpha + beta, alpha/beta 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. (C) 2003 Wiley-Liss, Inc.
URI: http://dx.doi.org/10.1002/prot.10313
http://hdl.handle.net/11536/28045
ISSN: 0887-3585
DOI: 10.1002/prot.10313
期刊: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume: 50
Issue: 4
起始頁: 531
結束頁: 536
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