Researchers developed an artificial intelligence system that predicts where lysine residues undergo post-translational modifications by combining protein language models with three-dimensional structural information. The framework achieved superior accuracy compared to existing methods in identifying modification sites across different lysine PTM types. This computational breakthrough addresses a persistent bottleneck in protein research, where experimental identification of these modifications remains technically demanding and time-intensive. The ability to accurately predict lysine modifications has profound implications for understanding cellular regulation and disease mechanisms. These modifications control everything from gene expression to metabolic pathways, making their precise mapping essential for therapeutic development. The integration of language models—which capture evolutionary patterns in protein sequences—with structural context represents a methodological advance that could accelerate drug discovery timelines. For researchers investigating aging and longevity, this tool opens new avenues for identifying how post-translational modifications change with age and contribute to cellular dysfunction. The framework's predictive capabilities could help prioritize experimental targets and reveal previously unknown regulatory networks governing healthspan.