RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Journal: Bioinformatics (Oxford, England)
PMID: 38175759

Abstract

MOTIVATION: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.

Authors

  • Xuejiao Wang
    School of Literature and Journalism, Sanjiang University, Nanjing, Jiangsu 210012, China.
  • Tingfang Wu
    1 Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.
  • Yelu Jiang
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Taoning Chen
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Deng Pan
    Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China.
  • Zhi Jin
  • Jingxin Xie
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Lijun Quan
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Qiang Lyu
    Department of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.

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