Protein 8-class secondary structure prediction using conditional neural fields

被引：69

作者：

Wang, Zhiyong ^{[1
]}

Zhao, Feng ^{[1
]}

Peng, Jian ^{[1
]}

Xu, Jinbo ^{[1
]}

机构：

[1] Toyota Technol Inst, Chicago, IL USA

来源：

PROTEOMICS | 2011年 / 11卷 / 19期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

Bioinformatics; Conditional neural fields; Eight class; Protein; Secondary structure prediction; SUPPORT VECTOR MACHINES; NETWORKS;

D O I：

10.1002/pmic.201100196

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA.

引用

页码：3786 / 3792

页数：7

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