DeepPrime7: Predicting PE7 Prime Editing Efficiency Across PAM Variants

Prime editing efficiency depends critically on pegRNA design and PAM compatibility, but the emergence of Prime Editor 7 (PE7) systems with engineered PAM variants has introduced an additional layer of complexity. Selecting the appropriate editor configuration for each target now requires more than pegRNA ranking. Existing predictors, largely trained on PE2/NGG data, do not explicitly model PE7-or PAM-dependent activity differences. Here, we present DeepPrime7, a PE7-optimized predictor trained on PAM-diverse pooled screening data that jointly models pegRNA sequence features and PE7 system identity. Using a multi-task formulation and strict guide-disjoint evaluation, DeepPrime7 accurately predicts editing efficiency across NGG-, NRCH-, and NRTH-compatible PE7 systems and enables both PE-system selection and pegRNA prioritization.