igRNA Prediction and Selection AI Models (igRNA-PS) for Bystander-less ABE Base Editing.

ABSTRACT

CRISPR derived base editing techniques tend to edit multiple bases in the targeted region, which impedes precise reversion of disease-associated single nucleotide variations (SNVs). We designed an imperfect gRNA (igRNA) editing strategy to achieve bystander-less single-base editing. To predict the performance and provide ready-to-use igRNAs, we employed a high-throughput method to edit 5000 loci, each with approximate 19 systematically designed ABE igRNAs. Through deep learning of the relationship of editing efficiency, original gRNA sequence and igRNA sequence, AI models were constructed and tested, designated igRNA Prediction and Selection AI models (igRNA-PS). The models have three functions, First, they can identify the major editing site from the bystanders on a gRNA protospacer with a near 90% accuracy. second, a modified single-base editing efficiency (SBE), considering both single-base editing efficiency and product purity, can be predicted for any given igRNAs. Third, for an editing locus, a set of 64 igRNAs derived from a gRNA can be generated, evaluated through igRNA-PS to select for the best performer, and provided to the user. In this work, we overcome one of the most significant obstacles of base editors, and provide a convenient and efficient approach for single-base bystander-less ABE base editing.