Protein secondary structure prediction by using deep learning method

Wang, Yangxu, Mao, Hua and Yi, Zhang (2017) Protein secondary structure prediction by using deep learning method. Knowledge-Based Systems, 118. pp. 115-123. ISSN 0950-7051

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Wang et al - Protein secondary structure prediction by using deep learning method AAM.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1016/j.knosys.2016.11.015

Abstract

The prediction of protein structures directly from amino acid sequences is one of the biggest challenges in computational biology. It can be divided into several independent sub-problems in which protein secondary structure (SS) prediction is fundamental. Many computational methods have been proposed for SS prediction problem. Few of them can model well both the sequence-structure mapping relationship between input protein features and SS, and the interaction relationship among residues which are both important for SS prediction. In this paper, we proposed a deep recurrent encoder–decoder networks called Secondary Structure Recurrent Encoder–Decoder Networks (SSREDNs) to solve this SS prediction problem. Deep architecture and recurrent structures are employed in the SSREDNs to model both the complex nonlinear mapping relationship between input protein features and SS, and the mutual interaction among continuous residues of the protein chain. A series of techniques are also used in this paper to refine the model’s performance. The proposed model is applied to the open dataset CullPDB and CB513. Experimental results demonstrate that our method can improve both Q3 and Q8 accuracy compared with some public available methods. For Q8 prediction problem, it achieves 68.20% and 73.1% accuracy on CB513 and CullPDB dataset in fewer epochs better than the previous state-of-art method.

Item Type: Article
Uncontrolled Keywords: Deep learning, Secondary structure prediction, Encoder-decoder networks, Recurrent neural networks
Subjects: C100 Biology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2019 12:29
Last Modified: 11 Oct 2019 09:18
URI: http://nrl.northumbria.ac.uk/id/eprint/39675

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