Scientists Use Quantum Biology, AI to Sharpen Genome Editing Tool
Published:23 Jan.2024 Source:DOE/Oak Ridge National Laboratory
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. 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 scientists built an explainable artificial intelligence model called iterative random forest. The model revealed key features about nucleotides that can enable the selection of better guide RNAs. "The model helped us identify clues about the molecular mechanisms that underpin the efficiency of our guide RNAs," Prates said, "giving us a rich library of molecular information that can help us improve CRISPR technology."
Using explainable AI gave scientists an understanding of the biological mechanisms that drove results, rather than a deep learning model rooted in a "black box" algorithm that lacks interpretability, said Jaclyn Noshay, a former ORNL computational systems biologist who is first author on the paper. "We're greatly improving our predictions of guide RNA with this research," Eckert said. "A major goal of our research is to improve the ability to predictively modify the DNA of more organisms using CRISPR tools. This study represents an exciting advancement toward, understanding how we can avoid making costly 'typos' in an organism's genetic code," said ORNL's Paul Abraham.