RaptorX-Property: a web server for protein structure property prediction

• Published in: Nucleic acids research Vol. 44; no. W1; pp. W430 - W435
• Main Authors: Wang, Sheng, Li, Wei, Liu, Shiwang, Xu, Jinbo
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 08.07.2016
• Subjects: Amino Acid Sequence; Area Under Curve; Caspases - chemistry; Databases, Protein; Internet; Machine Learning; Neural Networks (Computer); Protein Structure, Secondary; Proteins - chemistry; ROC Curve; Software; Solvents - chemistry; Time Factors; Web Server issue
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkw306

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.