Deep learning methods for protein torsion angle prediction

BMC Bioinformatics

Methodology Article

Qiang Lyu¹  Haiou Li¹  Jie Hou²  Jianlin Cheng²  Badri Adhikari³ 

[1] Department of Computer Science and Technology, Soochow University, 215006, Suzhou, Jiangsu, China;Department of Electrical Engineering and Computer Science, University of Missouri, 65211, Columbia, MO, USA;Department of Mathematics and Computer Science, University of Missouri–St. Louis, 1 University Blvd. 311 Express Scripts Hall, 63121, St. Louis, MO, USA;

关键词: Deep learning;    Deep recurrent neural network;    Restricted Boltzmann machine;    Protein torsion angle prediction;   

DOI  :  10.1186/s12859-017-1834-2

 received in 2017-04-14, accepted in 2017-09-11,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundDeep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins.ResultsWe design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20–21° and 29–30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method.ConclusionsOur experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

【 授权许可】

CC BY   
© The Author(s). 2017

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