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Cited by 23vaccines and immunoinformatics approachesCancer Immunotherapy and BiomarkersImmunotherapy and Immune Responses

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

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Jiang, Dawei, Xi, Binbin, Tan, Wenchong, Chen, Zixi, Wei, Jinfen, Hu, Meiling, Lu, Xiaoyun, Chen, Dong, Cai, Hongmin, Du, Hongli·2024·Bioinformatics
DOI 10.1093/bioinformatics/btae547PMC PMC11419954
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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. Results: We present NeoaPred, a deep-learning framework for neoantigen prediction. NeoaPred accurately constructs pHLA-I complex structures, with 82.37% of the predicted structures showing an RMSD of < 1 Å. Using these structures, NeoaPred integrates differences in surface, structural, and atom group features between the mutant peptide and its wild-type counterpart to predict a foreignness score. This foreignness score is an effective factor for neoantigen prediction, achieving an AUROC (Area Under the Receiver Operating Characteristic Curve) of 0.81 and an AUPRC (Area Under the Precision-Recall Curve) of 0.54 in the test set, outperforming existing methods. Availability and implementation: The source code is released under an Apache v2.0 license and is available at the GitHub repository (https://github.com/Dulab2020/NeoaPred).

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Metadata

Authors
Jiang, D., Xi, B., Tan, W., Chen, Z., Wei, J., Hu, M., Lu, X., Chen, D., Cai, H., & Du, H.
Affiliations
  • South China University of Technology (CN)
  • Jinan University (CN)
Corresponding author
Du, Hongli
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2024-09
Volume
40
Issue
9
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btae547
PMC
PMC11419954
OpenAlex
W4402553705
Cited by
23
References
5
Citations by year
2026: 12 · 2025: 11
FWCI
2.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Key R&D Program of Guangdong Province (2019B020226001)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Keywords
neoantigen prediction, immunogenicity, cancer immunotherapy, somatic mutations, deep learning, surface features, structural features, protein structure prediction, T cell epitopes
MeSH terms
Deep Learning, Antigens, Neoplasm, HLA Antigens, Humans, Neoplasms, Peptides, Software, Histocompatibility Antigens Class I, Computational Biology

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