Multilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles

Area of Science:

• Genomics
• Molecular Biology
• Biotechnology

Background:

• CRISPR-Cas9 screens are vital for systematic gene function analysis.
• Screen quality, especially in negative-selection screens, hinges on generating loss-of-function alleles.
• Observed dropout efficiencies in essentialome screens exceed predictions based on frameshift mutations alone.

Purpose of the Study:

• To investigate the factors contributing to high-efficiency loss-of-function allele generation in CRISPR-Cas9 screens.
• To develop a predictive tool that enhances the selection of single-guide RNAs (sgRNAs) for effective gene knockout.

Main Methods:

• Performed essentialome screens in cancer cell lines and embryonic stem cells using optimized workflows.
• Analyzed the impact of in-frame mutations on protein function, correlating it with amino acid composition and conservation.
• Integrated protein features, sgRNA activity predictors, and indel formation predictors into a novel scoring system.

Main Results:

• Superior effect sizes in CRISPR-Cas9 screens are primarily driven by in-frame mutations impacting protein function.
• A predictive model, the Vienna Bioactivity CRISPR score, was developed by integrating protein features and sgRNA performance metrics.
• This new score demonstrates superior performance compared to existing tools in predicting effective sgRNAs for loss-of-function allele generation.

Conclusions:

• In-frame mutations play a critical role in CRISPR-Cas9 mediated gene knockout efficiency.
• The Vienna Bioactivity CRISPR score offers an improved method for selecting sgRNAs, leading to more reliable loss-of-function alleles.
• This tool enhances the precision and success rate of CRISPR-Cas9 screens for functional genomics research.