Vo Thanh Hoa, McNeela Edel, O'Donovan Orla, Rani Sweta, Mehta Jai Prakash
Department of Science, Pharmaceutical and Molecular Biotechnology Research Center (PMBRC), South East Technological University, Waterford, Ireland.
Department of Applied Science, South East Technological University, Carlow, Ireland.
Proteomics Clin Appl. 2025 Jul 31:e70018. doi: 10.1002/prca.70018.
Immunopeptidomics is the large-scale study of peptides presented by major histocompatibility complex (MHC) molecules and plays a central role in neoantigen discovery and cancer immunotherapy. However, the complexity of mass spectrometry data, the diversity of peptide sources, and variability in immune responses present major challenges in this field.
In recent years, artificial intelligence (AI)-based methods have become central to advancing key steps in immunopeptidomics. It has enabled advances in de novo sequencing, peptide-spectrum matching, spectrum prediction, MHC binding prediction, and T cell recognition modeling. In this review, we examine these applications in detail, highlighting how AI is integrated into each stage of the immunopeptidomics workflow.
This review presents a focused case study on breast cancer, a heterogeneous and historically less immunogenic tumor type, to examine how AI may help overcome limitations in identifying actionable neoantigens.
We discuss current bottlenecks, including challenges in modeling noncanonical peptides, accounting for antigen processing defects, and avoiding on-target off-tumor toxicity. Finally, we outline future directions for improving AI models to support both personalized and off-the-shelf immunotherapy strategies.
Artificial intelligence (AI) is reshaping the immunopeptidomics landscape by overcoming challenges in peptide identification, immunogenicity prediction, and neoantigen prioritization. This review highlights how AI-based tools enhance the detection of MHC-bound peptides-including low-abundance, noncanonical, and post-translationally modified epitopes and improve peptide-spectrum matching and T-cell epitope prediction. By demonstrating a case study on applications in breast cancer, we illustrate the potential of AI to reveal hidden immunogenic features in tumors previously likely considered immunologically "cold." These advancements open new opportunities for expanding neoantigen discovery pipelines and optimizing cancer immunotherapies. Looking ahead, the application of deep learning, transfer learning, and integrated multi-omics models may further elevate the accuracy and scalability of immunopeptidomics, enabling more effective and inclusive vaccine and T-cell therapy development.
免疫肽组学是对主要组织相容性复合体（MHC）分子呈递的肽进行的大规模研究，在新抗原发现和癌症免疫治疗中发挥着核心作用。然而，质谱数据的复杂性、肽来源的多样性以及免疫反应的变异性给该领域带来了重大挑战。
近年来，基于人工智能（AI）的方法已成为推动免疫肽组学关键步骤的核心。它推动了从头测序、肽谱匹配、谱图预测、MHC结合预测和T细胞识别建模等方面的进展。在本综述中，我们详细研究了这些应用，突出了AI如何融入免疫肽组学工作流程的每个阶段。
本综述针对乳腺癌这一异质性且历来免疫原性较低的肿瘤类型展开了重点案例研究，以探讨AI如何帮助克服在识别可操作新抗原方面的局限性。
我们讨论了当前的瓶颈，包括非经典肽建模、考虑抗原加工缺陷以及避免靶上脱瘤毒性等方面的挑战。最后，我们概述了改进AI模型以支持个性化和现成免疫治疗策略的未来方向。
人工智能（AI）正在通过克服肽鉴定、免疫原性预测和新抗原优先级排序方面的挑战，重塑免疫肽组学格局。本综述强调了基于AI的工具如何增强对MHC结合肽的检测，包括低丰度、非经典和翻译后修饰的表位，并改善肽谱匹配和T细胞表位预测。通过展示乳腺癌应用的案例研究，我们阐明了AI揭示先前可能被认为免疫“冷”的肿瘤中隐藏免疫原性特征的潜力。这些进展为扩展新抗原发现管道和优化癌症免疫治疗开辟了新机会。展望未来，深度学习、迁移学习和综合多组学模型的应用可能会进一步提高免疫肽组学的准确性和可扩展性，从而实现更有效和更具包容性的疫苗和T细胞疗法开发。
