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

## TL;DR

PredLyP is a new computational tool that predicts peptides generated by phagolysosomal proteases, helping study immune responses and disease.

## Contribution

PredLyP introduces a novel approach using Position Specific Scoring Matrices and sequential protease cutting to predict tissue-specific lysosomal peptides.

## Key findings

- PredLyP outperforms existing tools in predicting complete and partial digestion fragments from proteins.
- The tool incorporates amino acid sequences, physical properties, and structural features for accurate predictions.
- PredLyP's sequential cutter mimics protease action, offering insights into substrate fragment generation.

## Abstract

Peptides are versatile tools in immunotherapy, serving as vaccines and targets for specific immunotherapeutic strategies. Peptides engage immune cells like macrophages and T cells, enabling precise modulation of immune responses. In this context, we highlight the utility of macrophages, innate immune cells involved in constant surveillance, for detecting their phagolysosomal content as a minimally-invasive biomarker strategy. Analyzing proteolytic patterns in phagolysosomes offers a high-sensitivity approach to assess tissue homeostasis and tissue disruption, such as in cancer. Despite their potential, a major challenge lies in the lack of comprehensive tools for predicting cutting sites across phagolysosomal proteases.

Therefore, we developed the computational tool PredLyP (abbreviation for “prediction of lysosomal proteases”) to identify cutting sites of phagolysosomal proteases, which are essential enzymes involved in protein degradation within (phago)lysosomes, to predict the potential peptides generated from the input proteins. Unlike existing tools, PredLyP utilizes Position Specific Scoring Matrices derived from amino acid sequences, physical (charge and hydropathy) and structural (secondary structure and solvent accessibility) features. Moreover, it incorporates a sequential cutter functionality that mimics the ordered action of proteases, providing predictive insights into substrate fragment generation. Comparisons with other tools demonstrate the superior sensitivity of PredLyP, enabling accurate prediction of complete and partial digestion fragments, a critical requirement for real-world applications in proteomics, antibody development, and immune system research. Overall, PredLyP represents a robust tool for advancing our understanding of proteolytic processes in phagolysosomes and their implications in health and disease.