RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning
By Yujuan Gao, Sheng Wang, Minghua Deng, Jinbo Xu. 2018
44 citations recorded by the source corpus.
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. 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. Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.
Subfields: Molecular Biology
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In Molecular Biology
- Kuldip K. Paliwal
- Jack Hanson
- Yaoqi Zhou
- Thomas Litfin
- Yuedong Yang
- Claudio Mirabello
- Björn Wallner
- Liam J. McGuffin
Citations in the corpus
- Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks (2018, methodology): “…cf797/MUFoldSS, respectively) (Fang et al. , 2018a,b), RaptorX-Angles (available at https://github.com/lacus2009/RaptorX-Angle) (Gao et al. , 2018) and SPIDER3 and SPIDER3-Single (available: http://sparks-lab.org/ yueyang/download/index.php) (Heffernan et al. , 2017, 2018).”
- rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete (2018, background): “In the last couple of decades, a variety of machine learning methods have been developed to predict a number of structural properties of proteins: secondary structure (SS) [9–14], relative solvent accessibility (RSA) [14–17], backbone dihedrals [18], disorder [14,19,20], disorder-to-order transition”