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

TL;DR

A new method for analyzing peptides in blood reveals disease-specific protein breakdown patterns and potential biomarkers for β-thalassemia.

Contribution

Introduces a novel amino acid score-based clustering strategy for peptidomics, enabling precise biomarker discovery and proteolysis profiling.

Findings

Amino acid score-based clustering identifies novel peptide cluster biomarkers validated with heavy-labeled peptides.

The method uncovers disease-specific peptide-protein-protease relationships in β-thalassemia plasma samples.

Reference sample-assisted algorithms improve cohort study performance and handle missing data effectively.

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.

A novel amino acid (aa)‐score‐based single‐position peptide clustering strategy is developed for peptidomics, enabling precise profiling of protein proteolysis in plasma from β‐thalassemia cohort. The method identifies new aa position‐based peptide cluster biomarkers validated by heavy‐labeled peptides, visualizes aggregated changes, uncovers disease‐specific peptide‐protein‐protease features, and improves cohort study performance through reference sample‐assisted individualized algorithms.

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