Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning

Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning. Gao, Y., Wang, S., Deng, M., & Xu, J. bioRxiv, December, 2017.

Paper doi abstract bibtex

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.

@article{gao_real-value_2017,
	title = {Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning},
	copyright = {© 2017, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	url = {https://www.biorxiv.org/content/early/2017/12/20/236851},
	doi = {10.1101/236851},
	abstract = {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.},
	language = {en},
	urldate = {2017-12-20},
	journal = {bioRxiv},
	author = {Gao, Yujuan and Wang, Sheng and Deng, Minghua and Xu, Jinbo},
	month = dec,
	year = {2017},
	pages = {236851},
}

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