Deep learning guided design of protease substrates
Open accessPublished version of a preprint
Abstract
Proteases, enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cleaved by target proteases is essential for studying protease activity and for harnessing it in protease-activated diagnostics and therapeutics. However, the vast design space of possible substrates (c.a. 20 10 amino acid combinations for a 10-mer peptide) and the limited accessibility of high-throughput activity profiling tools hinder the speed and success of substrate design. We present CleaveNet, an end-to-end AI pipeline for the design of protease substrates. Applied to matrix metalloproteinases, CleaveNet enhances the scale, tunability, and efficiency of substrate design. CleaveNet generates peptide substrates that exhibit sound biophysical properties and capture not only well-established but also previously-uncharacterized cleavage motifs. To control substrate design, CleaveNet incorporates a conditioning tag that steers peptide generation towards desired cleavage profiles, enabling targeted design of efficient and selective substrates. CleaveNet-generated substrates were validated experimentally through a large-scale in vitro screen, even in the challenging case of designing highly selective substrates for MMP13. We envision that CleaveNet will accelerate our ability to study and capitalize on protease activity, paving the way for in silico design tools across enzyme classes.
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- Authors
- Martin-Alonso, C., Alamdari, S., Samad, T. S., Yang, K. K., Bhatia, S. N., & Amini, A. P.
- Affiliations
- Harvard–MIT Division of Health Sciences and Technology (US)
- Microsoft Research New England (United States) (US)
- Koch Institute for Integrative Cancer Research At MIT (US)
- Broad Institute (US)
- Microsoft (United States) (US)
- Corresponding author
- Bhatia, Sangeeta N.
- Amini, Ava P.
- ORCID
- Venue
- Nature Communications
- Published
- 2026-01-06
- Volume
- 17
- Issue
- 1
- Pages
- 54-54
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12775482
- OpenAlex
- W7119034859
- Cited by
- 14
- References
- 68
- Citations by year
- 2026: 13 · 2025: 1
- FWCI
- 41.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- "la Caixa" Foundation
- Virginia and D.K. Ludwig Fund for Cancer Research
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Protease and Inhibitor Mechanisms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- matrix metalloproteinases, MMP-13, peptide production, deep learning
- MeSH terms
- Deep Learning, Humans, Peptide Hydrolases, Peptides, Substrate Specificity, Matrix Metalloproteinase 13, Proteolysis
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