LassoESM a tailored language model for enhanced lasso peptide property prediction

Xuenan Mi, Susanna E. Barrett, Douglas A. Mitchell () and Diwakar Shukla ()
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Xuenan Mi: University of Illinois Urbana-Champaign
Susanna E. Barrett: University of Illinois Urbana-Champaign
Douglas A. Mitchell: Vanderbilt University School of Medicine
Diwakar Shukla: University of Illinois Urbana-Champaign

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a diverse group of natural products. The lasso peptide class of RiPPs adopt a unique [1]rotaxane conformation formed by a lasso cyclase, conferring diverse bioactivities and remarkable stability. The prediction of lasso peptide properties, such as substrate compatibility with a particular lasso cyclase or desired biological activity, remains challenging due to limited experimental data and the complexity of substrate fitness landscapes. Here, we develop LassoESM, a tailored language model that improves lasso peptide property prediction. LassoESM embeddings enable accurate prediction of substrate compatibility, facilitate identification of novel non-cognate cyclase–substrate pairs, and enhance prediction of RNA polymerase inhibitory activity, a biological activity of several known lasso peptides. We anticipate that LassoESM and future iterations will be instrumental in the rational design and discovery of lasso peptides with tailored functions.

Date: 2025
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DOI: 10.1038/s41467-025-63412-3

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