Structure and evolution-guided design of minimal RNA-guided nucleases.

Abstract

The design of RNA-guided nucleases with properties not limited by evolution can expand programmable genome-editing capabilities. However, generating diverse multidomain proteins with robust enzymatic properties remains challenging. Here, we use a protein design strategy that couples a structure-guided inverse-folding model with evolution-informed residue constraints to generate active, divergent variants of TnpB, a minimal CRISPR-Cas12-like nuclease, termed SynTnpBs. High-throughput screening of artificial intelligence-generated variants yielded editors that retained or exceeded wild-type activity in bacterial, plant, and human cells. Cryo-electron microscopy-based structure determination of the most divergent variant revealed stabilizing contacts in the RNA-DNA interfaces across conformations, demonstrating the design potential of this approach. Together, these results establish a strategy for creating non-natural RNA-guided nucleases and conformationally active nucleic acid binders, enlarging the designable protein space.