Elia, M. | Search bioRxiv

bioRxiv2025

Humanized Caffeine-Inducible Systems for Controlling Cellular Functions

Current cell therapies are limited by the lack of tools for controlling gene expression using humanized systems responsive to non-toxic stimuli. Starting from nanobodies that homodimerize in response to caffeine, we computationally designed inducible heterodimers and humanized the best-performing pairs. We used the resulting caffeine-inducible domains in engineered cytokine receptors for caffeine-inducible STAT3 signaling and in split transcription factors (caff-TFs) containing human-derived zinc-finger proteins. Heterodimerization of split transcription factors drastically enhanced their performance compared to homodimerization. We demonstrate that caff-TFs are compatible with lentiviral and retroviral delivery to Jurkat T-cells and enable inducible expression of therapeutic genes such as chimeric antigen receptors (CARs) in response to caffeine concentrations consistent with normal coffee consumption. By using the common, non-toxic molecule caffeine, and exclusively humanized protein components, these systems promise to be a safer alternative to existing systems and may be used in synthetic biology applications and for safer, more effective cell therapies.

synthetic biology↗

bioRxiv2024

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.

biochemistry↗