  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.
