Leong, J. W. | Search bioRxiv

bioRxiv2026

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

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.

bioengineering↗

bioRxiv2025

Generative Design of Cell Type-Specific RNA Splicing Elements for Programmable Gene Regulation

Programmable control of gene expression in specific cell types is essential for both basic discovery and therapeutic intervention, yet current strategies lack scalability across diverse cellular contexts. Here, we introduce SPICE (Splicing Proportions In Cell types), an integrated experimental and computational framework that harnesses alternative RNA splicing as a programmable modality for cell type-specific gene regulation. To power SPICE, we constructed a massively parallel reporter assay (MPRA) comprising 46,372 human-derived sequences and profiled exon skipping across 43 cell lines spanning 10 lineages, uncovering widespread cell type-specific exon skipping. Using this data, we trained deep learning models that both predict splicing in unseen contexts and generate synthetic sequences with programmed, cell type-specific splicing patterns. Leveraging these models, we further engineered sequences that selectively splice in cells harboring oncogenic splicing factor mutations, demonstrating translational potential. SPICE provides a generalizable strategy for dissecting splicing regulation and engineering alternative splicing as a gene expression regulatory layer for research and therapeutic applications. One Sentence SummaryWe introduce SPICE, an integrated framework that couples large-scale splicing assays with generative design to uncover regulatory principles and design programmable, cell-specific gene expression for research and therapeutic applications.

molecular biology↗