Title: A deep aggregated model for protein secondary structure prediction

Authors: Yu Hu; Tiezheng Nie; Derong Shen; Ge Yu

Addresses: Distributed Database Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang City, Liaoning Province, China ' Distributed Database Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang City, Liaoning Province, China ' Distributed Database Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang City, Liaoning Province, China ' Department of Computer Science, School of Computer Science and Engineering, Northeastern University, Shenyang City, Liaoning Province, China

Abstract: Protein sequence analysis is an important research subject that has drawn increasing attention from biomedical researchers. In this research field, Protein Secondary Structure Predication (PSSP) is a significant sub-project for studying protein spatial structure and biochemical function. However, when only the amino acid residues sequence information can be used as the input, it is a challenge problem to predict the spatial structure of the protein. Recently, the deep learning technology achieves great success in information mining. In this paper, we propose a Deep Neural Block Cascade Network (DeepNBCN) for Protein Secondary Structure Predication. This model is constructed by stacking multiple free-adjusted blocks, each for aggregating Feature Extractor Module and Concate and Activate (C&A) Module. The homogeneous and multi-branch architecture can model the complex internal relationship between amino acid sequence and protein secondary structure sequence. We use two publicly available protein datasets to evaluate the proposed model. Experimental results show that our model can obtain 85% Q₃ accuracy, 86% SOV score, and 75% Q₈ accuracy, respectively, achieving better performance compared with the currently popular predictors.

Keywords: sequence translating; PSSP; protein secondary structure predication; machine learning; cascade model; deep learning.

DOI: 10.1504/IJDMB.2019.100624

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.3, pp.231 - 249

Received: 09 Apr 2019
Accepted: 02 May 2019
Published online: 05 Jul 2019 *

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