Oak Ridge National Laboratory scientists applied their knowledge of quantum biology, artificial intelligence, and bioengineering to improve how CRISPR Cas9 genome editing tools work on organisms such as microbes that can be modified to produce renewable fuels and chemicals.
CRISPR is a powerful bioengineering tool that can be used to modify genetic code to improve an organism’s performance or correct mutations. The CRISPR Cas9 tool works by directing the Cas9 enzyme to bind to and cleave the corresponding targeted site in the genome using a single, unique guide RNA. Existing models for computationally predicting effective guide RNAs for CRISPR tools are based on data from only a few model species and perform poorly 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.”
ORNL scientists sought a better understanding of what’s going on at the most fundamental level in cell nuclei, where genetic material is stored, to improve the modeling and design of guide RNA. They turned to quantum biology, a branch of molecular biology and quantum chemistry that studies the effects of electronic structure on the chemical properties and interactions of nucleotides, the molecules that make up DNA and RNA.
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. 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.
Carrie Eckert
According to Erica Prates, computational systems biologist at ORNL, the distribution of electrons 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.
The best guide through a forest of decisions
The researchers created an explainable artificial intelligence model known as iterative random forest. In an approach described in the journal Nucleic Acids Research, they trained the model on a dataset of around 50,000 guide RNAs targeting the genome of E. coli bacteria while also taking quantum chemical properties into account.
The model revealed key characteristics of nucleotides that can be used to select better guide RNAs. “The model helped us identify clues about the molecular mechanisms that underpin the efficiency of our guide RNAs,” he added, “giving us a rich library of molecular information that can help us improve CRISPR technology.”
ORNL researchers validated the explainable AI model by conducting CRISPR Cas9 cutting experiments on E. coli with a large group of guides selected by the model. 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 wanted to improve our understanding of guide design rules for optimal cutting efficiency with a microbial species focus given knowledge of the incompatibility of models trained across [biological] kingdoms,” Noshay said.
The explainable AI model, with its thousands of features and iterative nature, was trained on the Summit supercomputer at ORNL’s Oak Ridge Leadership Computer Facility, or OLCF, a DOE Office of Science user facility.
Eckert stated that her synthetic biology team intends to collaborate with ORNL computational science colleagues to improve the new microbial CRISPR Cas9 model using data from lab experiments or a variety of microbial species.
Better CRISPR Cas9 tools for every species
Taking quantum properties into consideration opens the door to Cas9 guide improvements for every species. “This paper even has implications across the human scale,” Eckert said. “If you’re looking at any sort of drug development, for instance, where you’re using CRISPR to target a specific region of the genome, you must have the most accurate model to predict those guides.”
Refining CRISPR Cas9 models provides scientists with a higher-throughput pipeline for linking genotype to phenotype, or genes to physical traits, in the field of functional genomics. The findings have implications for the work of the ORNL-led Center for Bioenergy Innovation (CBI), such as improving bioenergy feedstock plants and bacterial biomass fermentation.
“We’re greatly improving our predictions of guide RNA with this research,” Eckert said in a statement. “The better we understand the biological processes at play and the more data we can feed into our predictions, the better our targets will be, improving the precision and speed of our research.”
“A major goal of our research is to improve our ability to use CRISPR tools to predictably modify the DNA of more organisms.” “This study represents an exciting step forward in understanding how we can avoid making costly ‘typos’ in an organism’s genetic code,” said ORNL’s Paul Abraham, a bioanalytical chemist who leads the DOE Genomic Science Program’s Secure Ecosystem Engineering and Design Science Focus Area, or SEED SFA, which funded the CRISPR research. “I am eager to learn how much more these predictions can improve as we generate additional training data and continue to leverage explainable AI modeling.”
