Wang Sheng, Li Wei, Liu Shiwang, Xu Jinbo
Toyota Technological Institute at Chicago, Chicago, IL, USA Department of Human Genetics, University of Chicago, Chicago, IL, USA
School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Zhejiang, China.
Nucleic Acids Res. 2016 Jul 8;44(W1):W430-5. doi: 10.1093/nar/gkw306. Epub 2016 Apr 25.
RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction.
