DTMP-prime: A deep transformer-based model for predicting prime editing efficiency and PegRNA activity

Abstract

Prime editors are CRISPR-based genome engineering tools with significant potential for rectifying patient mutations. However, their usage requires experimental optimization of the prime editing guide RNA (PegRNA) to achieve high editing efficiency. This paper introduces the deep transformer-based model for predicting prime editing efficiency (DTMP-Prime), a tool specifically designed to predict PegRNA activity and prime editing (PE) efficiency. DTMP-Prime facilitates the design of appropriate PegRNA and ngRNA. A transformer-based model was constructed to scrutinize a wide-ranging set of PE data, enabling the extraction of effective features of PegRNAs and target DNA sequences. The integration of these features with the proposed encoding strategy and DNABERT-based embedding has notably improved the predictive capabilities of DTMP-Prime for off-target sites. Moreover, DTMP-Prime is a promising tool for precisely predicting off-target sites in CRISPR experiments. The integration of a multi-head attention framework has additionally improved the precision and generalizability of DTMP-Prime across various PE models and cell lines. Evaluation results based on the Pearson and Spearman correlation coefficient demonstrate that DTMP-Prime outperforms other state-of-the-art models in predicting the efficiency and outcomes of PE experiments.

Keywords

• MT: Bioinformatics
• CRISPR
• prime editing
• PegRNA
• off-target
• deep learning