NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

Journal: Bioinformatics (Oxford, England)
PMID: 39276157

Abstract

MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen. Identifying immunogenic neoantigens is crucial for cancer immunotherapy development. However, the accuracy of current bioinformatic methods remains unsatisfactory. Surface and structural features of peptide-HLA class I (pHLA-I) complexes offer valuable insight into the immunogenicity of neoantigens.

Authors

  • Dawei Jiang
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
  • Binbin Xi
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
  • Wenchong Tan
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
  • Zixi Chen
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
  • Jinfen Wei
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
  • Meiling Hu
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
  • Xiaoyun Lu
    College of Pharmacy, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China. zhouyang@jnu.edu.cn.
  • Dong Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Hongmin Cai
    School of Computer Science& Engineering, South China University of Technology, Guangdong, China. hmcai@scut.edu.cn.
  • Hongli Du
    School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.

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