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MPCutter: Predicting Protease-specific Substrate Cleavage Sites Using a Protein Language Model.

Zhe Wang1, Tuoyu Liu2, Guoshun Xu1

  • 1State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100089, China.

Genomics, Proteomics & Bioinformatics
|December 23, 2025
PubMed
Summary
This summary is machine-generated.

We developed MPCutter, a novel bioinformatics tool that accurately predicts protease cleavage sites. This tool enhances understanding of proteolysis and identifies new therapeutic targets by analyzing protein sequences.

Keywords:
MPCutterProteaseProtease proteolytic eventsProtease-substrate cleavage predictionProtein sequence language model

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Proteases are enzymes that cleave peptide bonds, playing critical roles in protein degradation and physiological processes.
  • Accurate identification of protease-specific substrates and cleavage sites is essential for understanding cellular functions.
  • Existing bioinformatics tools for cleavage site prediction have limitations in accuracy and update frequency.

Purpose of the Study:

  • To develop an advanced computational tool for predicting protease-substrate cleavage events.
  • To overcome the limitations of existing prediction tools regarding accuracy and data scope.
  • To provide a robust platform for high-throughput prediction of protease cleavage sites across diverse protease families.

Main Methods:

  • Fine-tuning a general-purpose protein sequence language model to create MPCutter.
  • Utilizing sequence and structural information for enhanced cleavage site prediction.
  • Benchmarking MPCutter against existing tools using independent test datasets.

Main Results:

  • MP Cutter demonstrated superior performance compared to existing generic tools in predicting protease cleavage sites.
  • The tool accurately identified the majority of cleavage sites in case studies, including five validated caspase-3 sites.
  • MP Cutter successfully applied to the human proteome, revealing potential new substrates and insights into protease functions.

Conclusions:

  • MP Cutter offers enhanced accuracy and broader coverage for protease-substrate cleavage site prediction.
  • The tool is valuable for high-throughput substrate identification and exploring protease proteolytic events.
  • MP Cutter is freely available, facilitating further research in proteomics and drug discovery.