ArticleBioinformatics (Oxford, England)2024
NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Therapeutic cancer vaccines: development, challenges, and future perspectives.Acta pharmacologica Sinica · 2026Review
- Chimeric antigen receptor (CAR)-T cell therapy for solid tumors in pediatric patients: current breakthroughs, dilemmas, and strategies.Experimental hematology & oncology · 2026Review
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- A Deep Learning-Driven Framework Integrating Organoid-Based Functional Validation Identifies Universal Neoantigens from Recurrent Glioma Mutations.Cancer research · 2026Article
- The research progress of gastric cancer vaccines: a narrative review.Translational cancer research · 2026Review
- Neoantigen Targeting as a Novel Approach for Therapy-Resistant Tumors.Molecular diagnosis & therapy · 2026Review
- Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026Review
- Review
- Personalised neoantigen vaccines for precision oncology in Southeast Asia: clinical opportunities, infrastructure challenges, and policy perspectives.Frontiers in immunology · 2026Review
- mRNA vaccines in oncology: personalized cancer immunization and neoantigen targeting.Molecular & cellular oncology · 2026Review
- A machine learning-based method to optimize the immunogenicity of human leukocyte antigen class I-restricted neoantigens.Briefings in bioinformatics · 2025Article
- Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions.Cell systems · 2025Article
- Identifying T cell antigen at the atomic level with graph convolutional network.Nature communications · 2025Article
- Artificial intelligence and machine learning in the development of vaccines and immunotherapeutics-yesterday, today, and tomorrow.Frontiers in artificial intelligence · 2025Review
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Authors and funding
10 authors.
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Abstract
motivationNeoantigens, 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.
resultsWe 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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