Revolutionizing CRISPR technology with artificial intelligence

Abstract

Genome engineering has made remarkable strides, evolving from DNA-binding proteins such as zinc fingers and transcription activator-like effectors to CRISPR-Cas systems. CRISPR technology has revolutionized the field through its simplicity and ability to target specific genome regions via guide RNA and Cas proteins. Progress in CRISPR tools-CRISPR nucleases, base editors and prime editors-has expanded the toolkit to induce targeted insertions or deletions, nucleotide conversions and a wider array of genetic alterations. Nevertheless, variations in editing outcomes across cell types and unintended off-target effects still present substantial hurdles. Artificial intelligence (AI), which has seen rapid advances, provides high-level solutions to these problems. By leveraging large datasets from diverse experiments, AI enhances guide RNA design, predicts off-target activities and improves editing efficiency. In addition, AI aids in discovering and designing novel CRISPR systems beyond natural limitations. These developments provide new modalities essential for the innovation of personalized therapies and help to ensure efficiency, precision and safety. Here we discuss the transformative role of AI in advancing CRISPR technology. We highlight how AI contributes to refining nuclease-based editing, base editing and prime editing. Integrating AI with CRISPR technology enhances existing tools and opens doors to next-generation medicine for gene therapy.

Fig. 1. CRISPR-based genome editing tools.

CRISPR-based genome editing tools include CRISPR nucleases, base editors and prime editors. Cas nucleases are composed of the Cas9 protein and single guide RNA (sgRNA), which induce DSBs. CRISPR nucleases can be used for insertions, deletions and point mutations, as well as enabling chromosomal translocations. Base editors are composed of a catalytically impaired Cas9, sgRNA and a deaminase. Base editors primarily mediate C-to-T or A-to-G conversions without generating DSBs. Prime editors consist of a Cas9 nickase, an engineered reverse transcriptase and pegRNA, which contains a PBS and reverse transcription (RT) template encoding the desired edit. Prime editors are capable of small insertions and deletions and can facilitate all types of point mutation. The editing efficiency of each tool can be enhanced through AI-driven approaches.

Fig. 2. AI-driven prediction and engineering in CRISPR systems.

The timeline illustrates AI-assisted advancements in CRISPR-based genome editing, categorizing developments in gRNA design, off-target prediction, editing outcome prediction and protein engineering. ML and DL models are applied to predict the on-target and off-target activities of gRNA, along with editing efficiency and patterns. These models also facilitate the design of optimized gRNAs. Furthermore, AI-driven protein design can be utilized for protein engineering. Advanced tools such as AlphaFold3 enable structure-based discovery, amino acid homology-based exploration and Cas miniaturization. The color-coded classification differentiates models targeting Cas nucleases (in green), base editors (BE, in orange) and prime editors (PE, in blue), highlighting their specific contributions to genome engineering. Through these advancements, AI continues to refine CRISPR technologies, enhancing precision and broadening their applications in genome engineering.