Journal
NUCLEIC ACIDS RESEARCH
Volume 44, Issue W1, Pages W430-W435Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkw306
Keywords
-
Categories
Funding
- National Institutes of Health [R01GM0897532]
- National Science Foundation [DBI-0960390]
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1262603] Funding Source: National Science Foundation
Ask authors/readers for more resources
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 similar to 84% Q3 accuracy for 3-state SS, similar to 72% Q8 accuracy for 8-state SS, similar to 66% Q3 accuracy for 3-state solvent accessibility, and similar to 0.89 area under the ROC curve (AUC) for disorder prediction.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available