Structure- and deep learning-guided engineering of a size-minimized CRISPR/Cas.

Abstract

Multidomain proteins play central roles in cellular regulation, yet their intrinsic flexibility and structural instability often hinder optimization for biotechnological applications. Here, we present an integrated structure-guided and deep learning-assisted engineering strategy that combines structure modeling with Protein Message Passing Neural Network (ProteinMPNN)-based sequence design to generate an ultracompact CRISPR activator (uCRISPRa) derived from the miniature CRISPR/Cas12f. Structural and computational analyses identified flexible, nonessential regions within both Cas12f and its single-guide RNA (sgRNA), enabling rational truncation and sequence redesign while preserving DNA-targeting capability. When delivered as mRNA encapsulated in lipid nanoparticles, uCRISPRa achieved selective activation of olfr544 among more than a thousand homologous olfactory receptor genes in skeletal muscle cells, leading to enhanced mitochondrial biogenesis. These findings demonstrate that the integration of structure-based protein engineering with deep learning sequence optimization provides a powerful framework for developing compact and efficient CRISPR effectors, offering broad potential for precise gene regulation and functional studies of complex macromolecular systems.