Scientists use quantum biology, AI to sharpen genome editing tool
Scientists at Oak Ridge National Laboratory used their expertise in quantum biology, artificial intelligence and bioengineering to improve how CRISPR Cas9 genome editing tools work on organisms like microbes that can be modified to produce renewable fuels and chemicals.
CRISPR is a powerful tool for bioengineering, used to modify genetic code to improve an organism’s performance or to correct mutations. The CRISPR Cas9 tool relies on a single, unique guide RNA that directs the Cas9 enzyme to bind with and cleave the corresponding targeted site in the genome. Existing models to computationally predict effective guide RNAs for CRISPR tools were built on data from only a few model species, with weak, inconsistent efficiency when applied to microbes.
“A lot of the CRISPR tools have been developed for mammalian cells, fruit flies or other model species. Few have been geared towards microbes where the chromosomal structures and sizes are very different,” said Carrie Eckert, leader of the Synthetic Biology group at ORNL. “We had observed that models for designing the CRISPR Cas9 machinery behave differently when working with microbes, and this research validates what we’d known anecdotally.”
To improve the modeling and design of guide RNA, the ORNL scientists sought a better understanding of what’s going on at the most basic level in cell nuclei, where genetic material is stored. They turned to quantum biology, a field bridging molecular biology and quantum chemistry that investigates the effects that electronic structure can have on the chemical properties and interactions of nucleotides, the molecules that form the building blocks of DNA and RNA.
The way electrons are distributed in the molecule influences reactivity and conformational stability, including the likelihood that the Cas9 enzyme-guide RNA complex will effectively bind with the microbe’s DNA, said Erica Prates, computational systems biologist at ORNL.
The best guide through a forest of decisions
The scientists built an explainable artificial intelligence model called iterative random forest. They trained the model on a dataset of around 50,000 guide RNAs targeting the genome of E. coli bacteria while also taking into account quantum chemical properties, in an approach described in the journal Nucleic Acids Research. More