PRIDICT2.0 and ePRIDICT advance prime editing
Researchers from the University of Zurich, including the Krauthammer Lab at DQBM, have contributed to a new protocol for systematic pegRNA design published in Nature Protocols. The study presents PRIDICT2.0 and ePRIDICT, two machine learning tools that predict prime editing efficiency by considering both pegRNA design features and local chromatin context.
PRIDICT2.0 enables accurate predictions for insertions, deletions, and replacements up to 40 base pairs, while ePRIDICT incorporates chromatin states to further refine editing efficiency estimates. Together, these tools help researchers select optimal pegRNAs and improve genome editing outcomes in basic and translational research.
Read the full protocol here: https://doi.org/10.1038/s41596-025-01244-7