Design of Highly Efficient pegRNAs with Minimized Off-Target Effects Using DeepPrime

Prime editors are newly developed genome editing tools capable of installing various types of mutations without introducing double-strand DNA breaks (DSBs) or requiring donor DNA templates. Prime editors consist of a Cas9 nickase fused to a reverse transcriptase (RT) domain and function as a ribonucleoprotein complex with prime editing guide RNA (pegRNA). Unlike the single guide RNA (sgRNA) used with Cas9 or base editors, the editing efficiency of pegRNAs is determined by the sequence composition and length of the guide sequence, primer binding site (PBS), and reverse transcription template (RTT). The high design flexibility of pegRNAs presents both opportunities and challenges. Therefore, designing pegRNAs with high efficacy has been a significant hurdle for the application of prime editing. Here, we present a step-by-step protocol for designing optimal pegRNAs to introduce desired edits using a deep learning-based algorithm, DeepPrime.