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PredLyP: A computational tool for predicting tissue-specific (phago-)lysosomal post-digestion peptides.

Mattijn Wagt1, Cristina Teodosio1,2,3,4,5,6, Anniek L de Jager1

  • 1Department of Immunology, Leiden University Medical Center, Leiden, the Netherlands.

Computational and Structural Biotechnology Journal
|November 17, 2025
PubMed
Summary

We developed PredLyP, a computational tool to predict peptide generation by lysosomal proteases. This aids in understanding immune responses and developing new biomarkers for diseases like cancer.

Keywords:
(phago)lysosomal digestionComputational toolPeptide predictionPost-digestion peptide

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Area of Science:

  • Immunology
  • Computational Biology
  • Biochemistry

Background:

  • Peptides are crucial in immunotherapy, engaging immune cells like macrophages and T cells.
  • Macrophages, as innate immune cells, offer a pathway for minimally-invasive biomarker strategies by analyzing phagolysosomal content.
  • Assessing proteolytic patterns in phagolysosomes can reveal tissue homeostasis and disruption, relevant to diseases like cancer.

Purpose of the Study:

  • To address the lack of tools for predicting protease cutting sites in phagolysosomes.
  • To develop a computational tool for identifying phagolysosomal protease cleavage sites and predicting generated peptides.
  • To enhance the understanding of proteolytic processes within phagolysosomes for applications in health and disease.

Main Methods:

  • Development of PredLyP (prediction of lysosomal proteases), a computational tool for predicting protease cleavage sites.
  • Utilizing Position Specific Scoring Matrices, physical features (charge, hydropathy), and structural features (secondary structure, solvent accessibility).
  • Incorporating a sequential cutter functionality to simulate ordered protease action and predict substrate fragment generation.

Main Results:

  • PredLyP accurately identifies cutting sites of phagolysosomal proteases.
  • The tool predicts potential peptides generated from input proteins, including complete and partial digestion fragments.
  • PredLyP demonstrates superior sensitivity compared to existing tools in predicting proteolytic patterns.

Conclusions:

  • PredLyP is a robust computational tool for predicting peptide generation by phagolysosomal proteases.
  • The tool advances the understanding of proteolytic processes in phagolysosomes and their role in health and disease.
  • PredLyP has significant implications for proteomics, antibody development, and immune system research.