Deep-learning-based design of an orthogonal self-labeling protein from K-Ras(G12C)

Authors

Mou, J., Leong, J. W., Gao, S., Slaw, K., McGill, M., Donovan, K. A., Metivier, R. J., Sendker, F. L., Kong, Q., Lin, D., Dharani, A. M., Chen, X. D., Haigis, K. M., Fischer, E. S., Chen, F., Polizzi, N. F.

Abstract

Self-labeling protein (SLP) tags enable versatile labeling of proteins in live cells, yet only two SLPs are commonly used, limiting multiplexing. Here, we repurposed the oncoprotein K-Ras(G12C) and its covalent inhibitors to create a third, orthogonal SLP system. We used a deep-learning-based approach to radically redesign the sequence of K-Ras(G12C) while preserving its covalent-inhibitor binding pocket. Our top design, LUCI-tag, is a 19 kDa, monomeric, thermostable SLP that rapidly and covalently reacts with commercially available K-Ras(G12C) inhibitors bearing diverse payloads. Unlike existing SLPs, LUCI-tag exhibited payload-agnostic labeling kinetics, outperforming HaloTag7 and SNAP-tag for a negatively charged payload. X-ray crystal structures of drug-bound and drug-free LUCI-tag showed the design was structurally accurate and contained a preorganized inhibitor-binding pocket that could explain its rapid labeling kinetics. Proteomics and cell-signaling experiments confirmed LUCI-tag is biologically inert. LUCI-tag enabled rapid, wash-free, live-cell imaging and simultaneous three-color multiplexed experiments with HaloTag7 and SNAP-tag. This work establishes LUCI-tag as an immediately useful orthogonal SLP and demonstrates that covalent drug-target pairs can be repurposed into a broadly applicable platform for protein labeling.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 29 Sep 2026.

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