# A suite of macrocyclic peptide inhibitors and substrate probes for arginine methyltransferases

## TL;DR

Researchers developed new macrocyclic peptides to study arginine methyltransferases, which are enzymes linked to cancer and other diseases.

## Contribution

A new workflow for analyzing mRNA display data uncovered potent and selective inhibitors and substrates for PRMTs.

## Key findings

- A new workflow identified PRMT4-selective inhibitors and broader PRMT-active peptides.
- Structural analysis revealed unique binding modes and conformational changes in substrates.
- Full-dataset analysis expanded hit diversity in genetically encoded library discovery.

## Abstract

Arginine methyltransferases (PRMTs) are key regulators of chromatin structure, RNA processing, and signal transduction, and their dysregulation has been linked to cancer and other diseases. The development of potent and selective chemical probes for individual PRMTs remains a major challenge. Here we report a discovery campaign using mRNA display under a reprogrammed genetic code that yielded new macrocyclic peptide inhibitors and substrate probes for coactivator-associated arginine methyltransferase 1 (CARM1/PRMT4) and related family members. To fully exploit the sequencing data from these selections, we were necessitated to develop and implement a workflow that analyses complete datasets without arbitrary abundance cut-offs, based on rapid sequence clustering for redundancy reduction and followed by alignment to retain representative diversity for evolutionary analysis. Whereas conventional abundance-based analysis identified a dominant but weakly active sequence family, our comprehensive approach uncovered potent PRMT4-selective inhibitors, broader PRMT-active peptides, and efficient substrate sequences. This unexpected recovery of efficient substrates prompted structural investigation by NMR and molecular dynamics, which revealed distinct binding modes, including interactions outside the canonical substrate-binding cleft and conformational rearrangements upon binding. Overall, these results provide a new set of chemical biology tools for studying arginine methyltransferases and illustrate how full-dataset analysis can expand the diversity of hits from genetically encoded library discovery. With the growing prominence of mRNA display in both academic and industrial settings, this work highlights its value for identifying bioactive macrocycles with diverse functional profiles.

A broad panel of substrate and inhibitor peptides, with varied subtype selectivity, were identified by mRNA display against cofactor-associated arginine methyltransferase 1 (CARM1), enabled by efficient data processing of the full sequencing output.