CRISPR Cas13 system, with its broad utility in RNA editing, nucleic acid detection, and disease diagnostics, presents tremendous potential. However, its continuous activity can lead to unwanted effects and limits its application as a tool for precisely targeting RNA in bacterial, plant, or human cells. To optimize these biotechnologies, there is a crucial need to spatiotemporally modulate Cas13's endonuclease activity. While metagenomics has traditionally been crucial for the discovery of naturally occurring CRISR regulators, it is a time-consuming and somewhat blind process, unable to focus on specific functions. The advent of AI-driven protein scaffolding approaches has significantly transformed structural biology, accelerating the annotation, discovery, and design of novel proteins.
To create new-to-nature Cas13 inhibitors, we employed in silico design methods using RF Diffusion, a noise-based software for generating protein scaffolds, and proteinMPNN to ensure the resulting proteins exhibit the desired biochemical properties. We then developed a high-throughput screening workflow that integrates computational design with wet lab validation, enabling the efficient and rapid design, purification, screening, and testing of small soluble binders targeting specific proteins. The inhibitors were further validated through binding and activity assays, as well as structural biology analysis.
This approach successfully provided Cas13 inhibitors, representing a step forward toward better control of RNA-targeting CRISPR systems to enhance the safety and efficacy of RNA-related CRISPR applications.