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Deep learning guided design of protease substrates.

Carmen Martin-Alonso1,2, Sarah Alamdari3, Tahoura S Samad1

  • 1Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.

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Summary
This summary is machine-generated.

CleaveNet, an AI pipeline, designs protease substrates efficiently. This tool accelerates the study and application of protease activity for diagnostics and therapeutics.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Enzymology

Background:

  • Proteases are crucial enzymes involved in various biological processes and diseases.
  • Identifying protease substrates is vital for understanding protease function and developing diagnostics/therapeutics.
  • Current substrate design is limited by vast sequence space and lack of high-throughput tools.

Purpose of the Study:

  • To develop an AI pipeline, CleaveNet, for efficient and tunable design of protease substrates.
  • To enhance the scale and accuracy of protease substrate identification.
  • To enable targeted design of substrates with specific cleavage profiles.

Main Methods:

  • Developed CleaveNet, an end-to-end AI pipeline for protease substrate design.
  • Applied CleaveNet to matrix metalloproteinases (MMPs) for substrate generation.
  • Incorporated a conditioning tag for controlled generation of substrates with desired cleavage profiles.
  • Validated CleaveNet-generated substrates through large-scale in vitro screening.

Main Results:

  • CleaveNet successfully generated peptide substrates with desirable biophysical properties.
  • Identified known and novel cleavage motifs for target proteases.
  • Demonstrated successful design of highly selective substrates, exemplified by MMP13.
  • Experimental validation confirmed the efficacy of CleaveNet-generated substrates.

Conclusions:

  • CleaveNet significantly enhances the efficiency, scale, and tunability of protease substrate design.
  • The AI pipeline can capture complex cleavage motifs and enable targeted substrate generation.
  • CleaveNet shows promise for accelerating research and development in protease-based diagnostics and therapeutics.
  • This approach paves the way for in silico design tools across diverse enzyme classes.