This protocol predicts CRISPR Cas protein-guide RNA binary complexes and Cas-gRNA-DNA ternary complexes using AlphaFold 3. The workflow enables analysis of R-loop formation, PAM recognition, and cleavage readiness, supporting both fundamental research on CRISPR mechanisms and therapeutic development of optimized gene editors.
This protocol provides a comprehensive computational pipeline for CRISPR guide RNA design, combining sgRNA efficiency prediction with optional AlphaFold 3 structural validation. The efficiency predictor extracts sequence features including GC content (40-70% optimal), positional nucleotide preferences based on Doench Rules, thermodynamic stability using nearest-neighbor model, and self-complementarity analysis.
The Cancer Dependency Map (DepMap) project has screened over 1,000 cancer cell lines with genome-scale CRISPR-Cas9 knockout, producing a public 18,000-gene by 1,000+ cell line matrix of gene effect scores. Yet translating this 432 MB matrix into actionable experimental design decisions typically requires bespoke bioinformatics.