Structure- and deep learning-guided engineering of a size-minimized CRISPR/Cas.
TL;DR
UCRISPRa achieved selective activation of olfr544 among more than a thousand homologous olfactory receptor genes in skeletal muscle cells, leading to enhanced mitochondrial biogenesis and broad potential for precise gene regulation and functional studies of complex macromolecular systems.
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.