Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 22, 2016

Abstract

MOTIVATION: Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibility states. However, lacking the confidence score for predicted values has limited their applications. Here we investigated whether or not we can make a reasonable prediction of absolute errors for predicted backbone torsion angles, Cα-atom-based angles and torsion angles, solvent accessibility, contact numbers and half-sphere exposures by employing deep neural networks.

Authors

  • Jianzhao Gao
    School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People's Republic of China.
  • Yuedong Yang
    Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.
  • Yaoqi Zhou
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518106, China. Electronic address: zhouyq@szbl.ac.cn.

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