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Updated: Sep 17, 2025

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Charting γ-secretase substrates by explainable AI.

Stephan Breimann1,2,3, Frits Kamp1, Gabriele Basset1

  • 1Biomedical Center (BMC), Division of Metabolic Biochemistry, Faculty of Medicine, LMU Munich, München, Germany.

Nature Communications
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

We developed Comparative Physicochemical Profiling (CPP), a new algorithm to identify protease substrates even without clear sequence motifs. CPP accurately predicts gamma-secretase substrates, improving disease and pathway understanding.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Proteomics

Background:

  • Proteases identify substrates via sequence information, a challenge when motifs are absent.
  • Gamma-secretase, an intramembrane protease, is linked to Alzheimer's disease and cancer.
  • Understanding protease-substrate interactions is crucial for cellular processes.

Purpose of the Study:

  • To develop a novel sequence-based algorithm for identifying protease substrates without relying on traditional recognition motifs.
  • To decipher the substrate signature for gamma-secretase, a key protease implicated in neurodegenerative and oncological diseases.
  • To predict the full scope of human gamma-secretase substrates and uncover novel biological associations.

Main Methods:

  • Development of Comparative Physicochemical Profiling (CPP), a sequence-based algorithm.
  • Utilizing machine learning for predicting gamma-secretase substrate scope.
  • Experimental validation of predicted substrates and identified pathways.

Main Results:

  • CPP identifies interpretable physicochemical features for protease substrate recognition with single-residue resolution.
  • The algorithm accurately predicts gamma-secretase substrate signatures, explaining substrate conformational changes.
  • Machine learning prediction identified numerous novel gamma-secretase substrates, improving accuracy from 60% to 90% with 88% experimental validation success.
  • New pathways and diseases associated with gamma-secretase activity were uncovered.

Conclusions:

  • Comparative Physicochemical Profiling (CPP) effectively decodes protease substrate signatures beyond sequence motifs.
  • The approach significantly advances the prediction of gamma-secretase substrates and their biological relevance.
  • CPP offers a broadly applicable framework for understanding diverse molecular recognition processes.