Deep learning guided design of protease substrates

TL;DR

CleaveNet is an AI tool that designs efficient and selective protease substrates, improving the study and application of protease activity.

Contribution

CleaveNet introduces an end-to-end AI pipeline for tunable and efficient protease substrate design.

Findings

CleaveNet generates substrates with sound biophysical properties and captures known and new cleavage motifs.

CleaveNet substrates were validated experimentally and showed high selectivity for MMP13.

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. 2010 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.

Effective substrates are key to probing and harnessing protease activity. This work presents CleaveNet, an AI tool that generates efficient, selective substrates, revealing known and distinct cleavage motifs and tuning designs to target activity profiles.

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