Machine learning-based prediction of base editor sgRNA fitness score

Pooled CRISPR base-editing screens enable the functional assessment of large numbers of genetic variants at scale. Predicting whether a selected sgRNA-Base Editor combination will produce a robust fitness effect could support sgRNA prioritisation and reveal features that are transferrable across screening experiments. However, this remains challenging due to the number of molecular and biological events taking place in a screening experiment. Here we investigated whether sgRNA fitness effect can be predicted by Machine Learning from a set of biological and sequence-specific features using a published Base Editing screening dataset. Decision Tree based models showed AUC-ROC ranging from 87 to 94% and AU-PR ranging from 53 to 75% using a gene-held out validation. Feature interpretability analysis revealed that sgRNA sequence, in particular PAM-proximal nucleotides, were the most predictive features. Gene essentiality and Predicted Variant Effect also carried predictive weight albeit to a more marginal extent. Analysis of matched Cas9 and base-editing data further identified gene essentiality as the feature most strongly associated with the different fitness score among base editing and Cas9. Our results confirm the feasibility of predicting fitness effect of a selected base editor-sgRNA combination using sequence as the primary feature, opening the way for future studies to incorporate additional biological features to enhance model interpretability and transferability.