Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: Dec 1, 2021

Abstract

BACKGROUND: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models.

Authors

  • Xianfang Wang
    School of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, People's Republic of China. 2wangfang@163.com.
  • Yifeng Liu
    Department of Clinical Medicine, Chengdu Medical College, Sichuan, China.
  • Zhiyong Du
    School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, 453003, China.
  • Mingdong Zhu
    School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, 453003, China.
  • Aman Chandra Kaushik
    Wuxi School of Medicine, Jiangnan University, Li Lake Avenue, Wuxi, Jiangsu 214122, China.
  • Xue Jiang
    Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
  • Dongqing Wei
    School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. dqwei@sjtu.edu.cn.

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