期刊
BMC BIOINFORMATICS
卷 19, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-018-2065-x
关键词
Dihedral angle prediction; Protein structure prediction; Clustering; Residual network; Deep learning
类别
资金
- National Key Research and Development Program of China [2016YFA0502303]
- National Key Basic Research Project of China [2015CB910303]
- National Natural Science Foundation of China [31471246]
- China Scholarship Council
- National Institutes of Health [R01GM089753]
- National Science Foundation [DBI-1564955]
- Nvidia Inc.
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1564955] Funding Source: National Science Foundation
Background: Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging. Results: In this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds. Conclusions: Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.
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