PREDICTIVE MODELING REVEALS NOVEL SEQUENCE FEATURES OF PRIME EDITING EFFICIENCY AND PRECISION

Abstract

Prime editing (PE) is an exciting genome editing technology that enables precise geneticmodifications without requiring donor DNA or inducing double-strand breaks. However, its relatively low efficiency limits broader applications. PE efficiency depends on the PE guide RNA (pegRNA) design, which consists of a spacer, a scaffold, a primer binding site, and a reverse transcriptase template containing the intended edit. Various computational models have been developed to predict pegRNA efficiencies across different target sites and edit types. Most existing models, however, rely on deep learning, making it difficult to interpret the contributions of individual features and their combinatorial effects on editing efficiency. Moreover, existing datasets provide limited coverage in terms of the sequence space for each editing length at a single target DNA locus, offering few insights into how the intended edit sequence influences PE efficiency. Recently, in collaboration with Britt Adamson’s lab at Princeton, we acquired new PE screen datasets including insertions, substitutions, and deletions in wild type (WT) condition and mismatch repair (MMR)-deficient condition. This thesis aims to dissect the influence of sequence attributes of the desired edits and cellular repair pathways on PE outcomes. We introduced linear models with LASSO feature selection to identify important positional sequence features predictive of editing efficiencies. Separate models were trained on insertion and substitution datasets. Our results demonstrated that linear models achieved excellent performance, suggesting that sequence features alone are determinants of editing efficiency. Beyond predictive accuracy, our linear models offered greater interpretability. Notably, we observed a new factor in both our datasets and previously published datasets: for insertion edits, incorporating cytosine (C) at the first editing position increases editing efficiency, providing a practical guideline for optimizing pegRNA design. Additionally, our model recaptured the C-C mismatch preference in WT cells versus in MMR-deficient cells. In summary, this work offers novel insights into the sequence composition and cellular repair mechanisms in PE efficiency, which can guide researchers in designing PE experiments to achieve higher efficiency.