For decades, a central challenge in peptidomics has been extracting disease-related "fingerprint" features from tens of thousands of seemingly disordered peptide fragments.

In a new collaborative study published in Advanced Science on November 18, researchers from the Institute of Biophysics of the Chinese Academy of Sciences and Henan University have introduced a new single-position peptide clustering-based strategy for peptidomics analysis.

The key innovation of this strategy is the introduction of an "amino acid score" (aa-score) algorithm that leverages both the complexity of peptides produced by protein proteolysis and the specificity of cleavage sites.

Instead of analyzing each peptide individually, the approach integrates all redundant peptides originating from the same protein and sharing the same characteristic amino acid position into a unified quantitative score.

By doing so, the strategy systematically organizes the diversity of proteolytic peptides into single-position peptide clusters, evaluating each cluster as a functional unit.

At the same time, it directly links substrates, specific protease activities, and their pathophysiological implications in disease, providing a more biologically interpretable analytical framework and fundamentally reshaping the analysis of disease-associated peptidomic data.

The researchers also proposed the concept of "amino-acid-position-based peptide-cluster biomarkers", transforming large amounts of seemingly chaotic peptide information into clear and meaningful "diagnostic codes."

In a clinical application study of β-thalassemia, the researchers identified novel peptide-cluster biomarkers and offered mechanistic insights into processes such as dysregulated proteolysis of AHSG.

This work brings a fresh perspective to the field of peptidomics and is expected to advance disease diagnosis and therapeutic development.

The peptidomic strategy based on single-position peptide clustering (Image by YANG Fuquan's group)