A study from Terry Fox-funded researchers at UHN’s Princess Margaret Cancer Centre introduces a new AI model that can help scientists design more efficient gene editing tools.
Gene editing enables scientists to make targeted changes to DNA in living cells. These approaches are widely used in biomedical research and have potential applications in treating disease, including correcting disease-causing mutations, identifying potential drug targets, and investigating gene function.
The CRISPR-Cas 9 system is one of the most widely used gene editing tools and uses a short piece of RNA, known as single-guide RNA (sgRNA), to direct the Cas9 protein to a specific location in the genome, where it can cut DNA. However, not all sgRNAs perform equally well, and predicting which guides will be most effective remains a challenge.
As the sequence of the sgRNA affects its gene editing performance, careful guide design is essential. Several AI models have been developed to help predict which RNA sequences will work best. However, these programs often function as "black boxes”, providing little information about how the models work. They also often overlook how the position of patterns within sgRNA sequences influences their effects on Cas9.
To address this problem, the team, co-led by 2024 Terry Fox New Investigator Dr. Sushant Kumar, and collaborators developed DeepCC9, a new machine learning framework designed to be both accurate and interpretable. The model identifies position-based sequence patterns for sgRNAs that affect gene editing efficiency. DeepCC9 enables users to learn what sequence features drive this predictive performance.
When tested using datasets for different Cas9 variants, DeepCC9 outperformed existing approaches in predicting how well gene editing would perform. In addition, the researchers identified 74 informative features of sgRNA sequences associated with the prediction of Cas9 genome-editing efficiency. They also found that the position of these features within an sgRNA can significantly affect how well Cas9 binds to and cuts DNA.
These findings provide a clearer picture of the sequence features that contribute to successful gene editing and could help scientists design more effective sgRNAs for research and biotechnology applications. DeepCC9 may help accelerate discoveries in genome engineering and other areas of molecular biology.
This research was funded by a Terry Fox New Investigator Award: Integrative Computational Framework For Precision Cancer Prevention and Early Detection
This study was also supported by the Princess Margaret Cancer Foundation, the Drug Applied Research Center, and Tabriz University of Medical Sciences.
A version of this article originally appeared on the Research at UHN website.