A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

Journal: BMC bioinformatics
Published Date: Dec 28, 2017

Abstract

BACKGROUND: Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building.

Authors

  • Lei Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Chao Fan
    College of Management Science, Chengdu University of Technology, Chengdu, China.
  • Zhiwen Zeng
    School of Information Science and Engineering, Central South University, No.932 South Lushan Road, Changsha, 410083, China. zengzhiwen@csu.edu.cn.

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