title: Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.
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.
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, IL.|Department of Human Genetics, University of Chicago, Chicago, IL.
authors.forename: Sheng
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL.
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, IL.
authors.forename: Jianzhu
authors.initials: J
authors.lastname: Ma
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, IL.
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins