A team of scientists at the Oak Ridge National Laboratory (ORNL), have embarked on a groundbreaking venture, leveraging quantum biology, artificial intelligence (AI), and bioengineering to revolutionize the effectiveness of CRISPR Cas9 genome editing tools. Their focus is on microbes, particularly those earmarked for modifications to produce renewable fuels and chemicals, presenting a unique challenge due to their distinct chromosomal structures and sizes.
Traditionally, CRISPR tools have been tailored for mammalian cells and model species, resulting in weak and inconsistent efficiency when applied to microbes. Recognizing this limitation, Carrie Eckert, leader of the Synthetic Biology group at ORNL, remarked, “Few have been geared towards microbes where the chromosomal structures and sizes are very different.” This realization prompted the ORNL scientists to explore a new frontier in the quest to enhance the precision of CRISPR tools.
The team’s journey took an unconventional turn as they delved into quantum biology, a field at the intersection of molecular biology and quantum chemistry. Quantum biology explores the influence of electronic structure on the chemical properties and interactions of nucleotides, the fundamental building blocks of DNA and RNA, within cell nuclei where genetic material resides.
To improve the modeling and design of guide RNA for CRISPR Cas9, the scientists developed an explainable AI model named the iterative random forest. Trained on a dataset of approximately 50,000 guide RNAs targeting the genome of E. coli bacteria, the model took into account quantum chemical properties. The objective was to understand, at a fundamental level, the electronic distribution in nucleotides, which influences the reactivity and stability of the Cas9 enzyme-guide RNA complex.
“The model helped us identify clues about the molecular mechanisms that underpin the efficiency of our guide RNAs,” explained Erica Prates, a computational systems biologist at ORNL. The iterative random forest, with its thousands of features and iterative nature, was trained using the high-performance Summit supercomputer at ORNL’s Oak Ridge Leadership Computer Facility.
What sets this approach apart is its commitment to explainable AI. Rather than relying on a “black box” algorithm that lacks interpretability, the ORNL team aimed to understand the biological mechanisms driving results. Jaclyn Noshay, a former ORNL computational systems biologist and first author on the paper, emphasized, “We wanted to improve our understanding of guide design rules for optimal cutting efficiency with a microbial species focus.”
Validation of the explainable AI model involved CRISPR Cas9 cutting experiments on E. coli, using a large group of guides selected by the model. The results were promising, confirming the efficacy of the model in guiding genome modifications for microbes.
The implications of this research extend far beyond microbial genome editing. “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,” highlighted Carrie Eckert. The study not only advances the field of synthetic biology but also has broader applications in drug development and bioenergy research.
The ORNL researchers envision collaborative efforts with computational science colleagues to further enhance the microbial CRISPR Cas9 model using additional data from lab experiments and diverse microbial species. The ultimate goal is to refine CRISPR Cas9 models for a wide range of species, facilitating predictive DNA modifications with unprecedented precision.
The study, supported by the DOE Office of Science Biological and Environmental Research Program, ORNL’s Lab-Directed Research and Development program, and high-performance computing resources, signifies a significant leap forward in the quest to improve CRISPR technology. As Paul Abraham, a bioanalytical chemist at ORNL, remarked, “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.” The findings hold promise for applications in fields ranging from bioenergy feedstock enhancement to drug development, marking a pivotal moment in the evolution of CRISPR technology.
Sources
https://doi.org/10.1093/nar/gkad736
