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A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets
Dalton T Ham1, Tyler S Browne1, Pooja N Banglorewala1
1Department of Biochemistry, Schulich School of Medicine and Dentistry, London, ON, N6A5C1, Canada.
Nature Communications
|September 7, 2023
Summary
We developed crisprHAL, a machine learning tool that accurately predicts CRISPR/Cas9 (SpCas9) single guide RNA (sgRNA) activity in bacteria. This advances antimicrobial development and genome engineering by improving sgRNA design and function prediction.
Area of Science:
- Microbiology
- Molecular Biology
- Bioinformatics
Background:
- The CRISPR/Cas9 system from Streptococcus pyogenes (SpCas9) is a versatile tool for antimicrobial applications and bacterial genome engineering.
- Existing models for predicting bacterial single guide RNA (sgRNA) activity lack accuracy and generalizability, partly due to limitations in training datasets that conflate SpCas9/sgRNA activity with cellular toxicity.
Purpose of the Study:
- To develop a high-quality dataset for training SpCas9/sgRNA activity prediction models.
- To create a machine learning architecture, crisprHAL, for accurate and generalizable prediction of sgRNA activity in bacteria.
- To improve the design of sgRNAs for antimicrobial and genome engineering applications.
Main Methods:
- Utilized a two-plasmid positive selection system to generate high-fidelity data distinguishing SpCas9/sgRNA cleavage activity from toxicity.
- Developed the crisprHAL machine learning model, incorporating transfer learning capabilities.
- Validated the model on existing datasets and tested its generalizability across different bacterial species.
Main Results:
- Generated a novel, high-quality dataset for SpCas9/sgRNA activity assessment.
- The crisprHAL model demonstrated significant improvements in sgRNA activity prediction accuracy, especially when fine-tuned with limited high-quality data.
- crisprHAL successfully recapitulated known SpCas9/sgRNA-target DNA interactions and showed generalizability to various bacterial species.
Conclusions:
- The crisprHAL model offers a robust solution for predicting bacterial SpCas9/sgRNA activity, overcoming limitations of previous models.
- This tool enhances the reliability of sgRNA design for both sequence-specific antimicrobials and precise bacterial genome engineering.
- crisprHAL represents a significant step towards a universal prediction tool for bacterial CRISPR-based applications.
