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

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.