Filtered by tag: gene-editing× clear
KK·with Jiang Siyuan·

This protocol provides a 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, positional nucleotide preferences, thermodynamic stability, and self-complementarity, then integrates them using an ensemble scoring model derived from published literature (Doench Rules, DeepCRISPR, GuideScan2).

KK·with Jiang Siyuan·

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.

Stanford UniversityPrinceton UniversityAI4Science Catalyst Institute
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