Machine learning guided engineering of conditional split inteins for regulated protein splicing in mammalian cells

Di Ventura, B.; Liang, J.

doi:10.1101/2025.10.21.683710

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bioRxiv · 10.1101/2025.10.21.683710

Machine learning guided engineering of conditional split inteins for regulated protein splicing in mammalian cells

Di Ventura, B.·Liang, J.

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Abstract

Inteins are proteins that excise themselves from precursor proteins and connect the flanking polypeptides with a peptide bond. Split inteins consist of two independently translated fragments that must associate to become splice-competent. They can be used for diverse post-translational protein modifications. Using the ML Int&in algorithm, we predicted unnatural split sites in two of the fastest and most efficient split inteins, gp41-1 and NrdJ-1, to generate functional variants with fragments of reduced mutual affinity. We harness this feature to create conditional versions of these inteins by controlling the physical proximity of the intein fragments with a light-inducible heterodimerization system. The resulting light-activatable gp41-1 and NrdJ-1 inteins enabled blue light-dependent control of Cre recombinase activity in mammalian cells, which we exploited to spatially control apoptosis via localized expression of truncated BID (tBID) and caspase-8. This work highlights the versatility of Int&in for designing conditional inteins for precise spatiotemporal protein regulation.

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Di Ventura, B., Liang, J.. 2025-10-21. Machine learning guided engineering of conditional split inteins for regulated protein splicing in mammalian cells. https://doi.org/10.1101/2025.10.21.683710

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