Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-05

AUTHORS

Sheng Wang, Jian Peng, Jianzhu Ma, Jinbo Xu

ABSTRACT

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. More... »

PAGES

18962

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep18962

DOI

http://dx.doi.org/10.1038/srep18962

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036395831

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26752681


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