ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Journal: Bioinformatics (Oxford, England)
Published Date: Dec 1, 2019

Abstract

MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual binding affinities are needed to increase precision and reduce the number of false positive predictions.

Authors

  • Yan Hu
    Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Ziqiang Wang
    Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China.
  • Hailin Hu
    School of Medicine, Tsinghua University, Beijing 100084, China.
  • Fangping Wan
    Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Lin Chen
    College of Sports, Nanjing Tech University, Nanjing, China.
  • Yuanpeng Xiong
    Department of Computer Science and Technology, Tsinghua University, Beijing, China.
  • Xiaoxia Wang
    School of Control and Computer Engineering, North China Electric Power University, Baoding, Hebei Province, China.
  • Dan Zhao
    Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, China.
  • Weiren Huang
    Department of Urology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, International Cancer Center, Shenzhen University School of Medicine, Shenzhen, China.
  • Jianyang Zeng
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China. Electronic address: zengjy321@tsinghua.edu.cn.

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