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DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures
  • Zhiye Guo,
  • Jie Hou,
  • Jianlin Cheng
Zhiye Guo
University of Missouri
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Jie Hou
Saint Louis University
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Jianlin Cheng
University of Missouri
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Peer review status:IN REVISION

04 Mar 2020Submitted to PROTEINS: Structure, Function, and Bioinformatics
09 Mar 2020Submission Checks Completed
09 Mar 2020Assigned to Editor
09 Mar 2020Reviewer(s) Assigned
29 Apr 2020Review(s) Completed, Editorial Evaluation Pending
07 May 2020Editorial Decision: Revise Major
07 Jul 20201st Revision Received
08 Jul 2020Assigned to Editor
08 Jul 2020Submission Checks Completed

Abstract

Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (i) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict secondary structure, and (ii) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on two independent test datasets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the 82 protein targets of 2018 CASP13 experiment and achieved the best Q3 score of 83.74% and SOV score of 72.46%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.