by Researchers at Oak Ridge National Laboratory (ORNL) have utilized their expertise in quantum biology, artificial intelligence (AI), and bioengineering to enhance the efficiency of CRISPR Cas9 genome editing tools for organisms like microbes. These tools can be used to modify genetic codes and improve the performance of organisms or correct mutations. The challenge lies in designing effective guide RNAs for CRISPR tools that work well with microbes, as existing models are based on data from a few model species and have inconsistent efficiency with microbes.
To tackle this issue, ORNL scientists delved into quantum biology, a field that examines the impact of electronic structure on the chemical properties of nucleotides, the building blocks of DNA and RNA. By understanding the electronic distribution in molecules, researchers can assess the reactivity and stability of the Cas9 enzyme-guide RNA complex when binding with the microbe’s DNA.
The scientists developed an explainable AI model called iterative random forest, which was trained on a dataset of approximately 50,000 guide RNAs targeting the E. coli bacteria genome, while also considering quantum chemical properties. This model provided insights into nucleotide features that enable the selection of better guide RNAs, resulting in a rich library of molecular information to improve CRISPR technology.
The AI model was validated through CRISPR Cas9 cutting experiments on E. coli bacteria, using a large group of guides selected by the model. Using explainable AI, scientists gained a deeper understanding of the biological mechanisms underlying the results, instead of relying on black box algorithms lacking interpretability.
The ORNL research team plans to collaborate with computational science colleagues to further improve the microbial CRISPR Cas9 model by incorporating data from lab experiments and various microbial species. This advancement in CRISPR Cas9 models has broad implications, not only for bioengineering microbes but also for drug development and improving bioenergy feedstock plants.
The ultimate goal is to enhance the ability to predictively modify the DNA of various organisms using CRISPR tools, reducing errors and improving the precision and speed of research in diverse fields such as functional genomics and bioenergy innovation.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
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