1483-Structure-Guided Design of Minimal RNA-Guided Nucleases

Researchers have developed a novel protein design strategy to create synthetic RNA-guided nucleases, known as SynTnpBs, which diverge significantly from natural evolutionary sequences. By combining inverse-folding AI models with evolutionary data to preserve essential DNA and RNA binding sites, the team successfully engineered complex, multidomain enzymes with high catalytic activity. High-throughput screening identified several artificial variants that match or surpass wild-type performance in bacterial, plant, and human cells. Structural analysis via cryo-electron microscopy confirmed that these AI-generated proteins maintain functional stability through new electrostatic networks and conformational flexibility. This approach expands the designable protein space, offering a robust framework for developing advanced programmable genome-editing tools beyond what exists in nature.References:Skopintsev P, Esain-Garcia I, DeTurk E C, et al. Structure and evolution-guided design of minimal RNA-guided nucleases[J]. Science, 2026, 393(6808): 313-318.前往小宇宙评论区与主播互动