Enabling Next-Generation Mass Spectrometry-Based Proteomics: Standards, Proteoform Resolution, and FAIR, Reproducible, and Quantitative Analysis.

Recent advances in mass spectrometry, data-independent acquisition, proteoform-resolving workflows, and multi-omics integration have significantly expanded the scale and scope of proteomics. However, the reuse and translational application of these datasets are limited by inconsistent standards, insufficient metadata, and inadequate computational interoperability. Proteoform-centric approaches provide higher molecular resolution by capturing intact protein variants and patterns of post-translational modification. Computational methods, including selected applications of machine learning and large language models (LLMs), are increasingly used for tasks such as spectral prediction and pattern discovery in clinical proteomics datasets. Despite these advancements, FAIR (Findable, Accessible, Interoperable, and Reusable) data practices, proteoform biology, and AI analytics are often pursued independently. This work presents an integrated framework for next-generation proteomics in which standardization and FAIR (Findable, Accessible, Interoperable, and Reusable) principles establish machine-actionable foundations for proteoform-resolved analysis and computational inference. It examines community efforts to promote data sharing and interoperability, as well as strategies for characterizing proteoforms using bottom-up, middle-down, and top-down approaches. It also highlights emerging AI and ML applications within the proteomics workflow. The framework emphasizes the importance of treating proteoforms as primary computational entities and adopting FAIR practices during data collection to enable reproducible and interpretable modeling. Finally, it introduces an architectural model that integrates FAIR infrastructures and proteoform resolution. In addition, practical recommendations for making AI-ready proteomics, including a minimal community checklist to support reproducibility, benchmarking, and translational scalability, are provided.