OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks.

Bioinformatics
Gang XuJianpeng Ma

Abstract

Predictions of protein backbone torsion angles (ϕ and ψ) and secondary structure from sequence are crucial subproblems in protein structure prediction. With the development of deep learning approaches, their accuracies have been significantly improved. To capture the long-range interactions, most studies integrate bidirectional recurrent neural networks into their models. In this study, we introduce and modify a recently proposed architecture named Transformer to capture the interactions between the two residues theoretically with arbitrary distance. Moreover, we take advantage of multitask learning to improve the generalization of neural network by introducing related tasks into the training process. Similar to many previous studies, OPUS-TASS uses an ensemble of models and achieves better results. OPUS-TASS uses the same training and validation sets as SPOT-1D. We compare the performance of OPUS-TASS and SPOT-1D on TEST2016 (1213 proteins) and TEST2018 (250 proteins) proposed in the SPOT-1D paper, CASP12 (55 proteins), CASP13 (32 proteins) and CASP-FM (56 proteins) proposed in the SAINT paper, and a recently released PDB structure collection from CAMEO (93 proteins) named as CAMEO93. On these six test sets, OPUS-TASS achieves...Continue Reading

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Apr 24, 2020·Journal of Chemical Theory and Computation·Gang XuJianpeng Ma

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Citations

Nov 12, 2020·Scientific Reports·Fereshteh MataeimoghadamAbdul Sattar
May 27, 2021·Journal of Chemical Information and Modeling·Jaswinder SinghYaoqi Zhou
Jul 3, 2021·Mathematical Biosciences and Engineering : MBE·Ying Xu, Jinyong Cheng
Nov 20, 2020·Journal of Chemical Information and Modeling·Gang XuJianpeng Ma

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