Engineering DNA-Targeting CRISPR and CRISPR-Like Effectors: Advances from Rational Design and High-Throughput Screening to AI-Driven Development

Abstract

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute adaptive immune systems in prokaryotes and have transformed life sciences, precision medicine, and synthetic biology as programmable genome-editing tools. Despite their broad utility, naturally occurring DNA-targeting Cas effectors remain constrained by several intrinsic limitations, including large protein size that complicates delivery, stringent protospacer adjacent motif (PAM) requirements that restrict targetable genomic space, and mismatch tolerance that can lead to off-target activity and potential genotoxicity. These challenges have made Cas protein engineering and the discovery of novel CRISPR and CRISPR-like systems from metagenomic resources central to the development of next-generation genome-editing platforms. This Review places recent advances within an integrated synthetic biology engineering continuum that links natural effector discovery, structure-guided hypothesis generation, high-throughput functional screening, machine learning-enabled model construction, and iterative redesign. This Review summarizes progress in the screening, optimization, and functional engineering of DNA-targeting CRISPR and CRISPR-like effectors, with emphasis on structure-guided rational design, directed evolution coupled with high-throughput screening, bioinformatics- and evolution-guided mining of novel systems from large-scale sequence databases, and artificial intelligence-assisted development. By integrating these strategies, we highlight how CRISPR effector engineering is moving toward design-build-test-learn (DBTL)-inspired workflows that expand the functional landscape of genome-editing technologies and advance genome editing toward improved efficiency, safety, and programmability.