Scientists at Oak Ridge National Laboratory (ORNL) have made significant advancements in the CRISPR Cas9 gene-editing tool used to modify microbes for renewable fuels and chemical production. By combining quantum biology, artificial intelligence (AI), and bioengineering, the researchers have improved the accuracy of the CRISPR Cas9 tool, particularly when applied to microbes with different chromosomal structures and sizes.
CRISPR Cas9 is a powerful bioengineering tool that modifies genetic code to enhance an organism’s performance or correct mutations. However, the existing models for designing CRISPR Cas9 tools were primarily developed for mammalian cells and other model species. When applied to microbes, these models showed weak and inconsistent efficiency due to differences in chromosomal structures. The team at ORNL sought to address this limitation by developing a more accurate and reliable method.
To enhance the modeling and design of guide RNAs—the RNA molecules that guide the Cas9 enzyme to the targeted site in the genome—the ORNL scientists looked into quantum biology. This interdisciplinary field examines the impact of electronic structure on the chemical properties of nucleotides, the building blocks of DNA and RNA. By considering quantum chemical properties, the scientists aimed to improve the effectiveness of the Cas9 enzyme-guide RNA complex binding with the microbe’s DNA.
The researchers employed an explainable AI model called iterative random forest, trained on a dataset of approximately 50,000 guide RNAs. This model not only took into account the quantum properties of nucleotides but also provided insights into the molecular mechanisms that underlie the efficiency of guide RNAs. By using the Summit supercomputer at ORNL, the team validated the AI model through CRISPR Cas9 cutting experiments on E. coli, using guides selected by the model.
By incorporating quantum properties into guide RNA design, the improved CRISPR Cas9 tools have implications for a wide range of species, including humans. For instance, they can enhance the accuracy of targeted genetic modifications in drug development. Furthermore, these advancements contribute to functional genomics, enabling scientists to more effectively link genotype to phenotype, or genes to physical traits.
The ORNL synthetic biology team plans to collaborate with computational science colleagues to refine the microbial CRISPR Cas9 model further. By incorporating data from lab experiments and various microbial species, the researchers aim to continue improving the predictability and precision of CRISPR technology. Ultimately, these advancements have significant implications for various fields, including bioenergy production and the enhancement of bioenergy feedstock plants.
The research was supported by the Department of Energy’s Genomic Science Program and the Center for Bioenergy Innovation. By continually refining CRISPR Cas9 models and leveraging explainable AI modeling, scientists can better modify the DNA of organisms, avoiding costly errors and enabling more precise and efficient research in the future.
