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Literature DB >> 21805636

Protein 8-class secondary structure prediction using conditional neural fields.

Zhiyong Wang¹, Feng Zhao, Jian Peng, Jinbo Xu.

Abstract

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.

Entities: Chemical Disease Gene Mutation

Mesh：

Substances：
Proteins

Year: 2011 PMID： 21805636 PMCID： PMC3341732 DOI： 10.1002/pmic.201100196

Source DB: PubMed Journal: Proteomics ISSN： 1615-9853 Impact factor: 3.984

31 in total

Protein 8-class secondary structure prediction using conditional neural fields.

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6. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

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