Single‐Position Peptide Clustering for Peptidomics Reveals Novel Disease Biomarkers and Dysregulated Proteolytic Characteristics

Abstract Mass spectrometry‐based peptidomics provides a comprehensive platform for mapping global proteolytic alterations and identifying disease biomarkers. However, existing analytical frameworks often lack the precision to capture disease‐specific signatures. Here, a single‐position peptide clustering strategy is introduced, leveraging the amino acid score (aa‐score) method, and applying it to plasma peptidomics in β‐thalassemia. By integrating grouped aa‐scores with tailored visualization, a clear and interpretable profile of protein degradation is generated from otherwise redundant datasets. Importantly, the use of heavy‐labeled peptides or reference samples in targeted quantitative peptidomics enabled, for the first time, the proposal of aa position‐based peptide cluster biomarkers. Combined with proteomics and complementary analyses, this strategy revealed disease‐specific peptide‐protein‐protease relationships. Furthermore, the robustness of the aa‐score framework is demonstrated by applying an individualized algorithm based on reference samples in an independent cohort study, highlighting its capacity to address missing values and improve overall performance.