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 mi “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. 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.”
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 using the
Summit supercomputer at ORNL’s Oak Ridge Leadership Computer Facility, or OLCF,
a DOE Office of Science user facility. Eckert said her synthetic biology team
plans to work with computational science colleagues at ORNL to take what they’ve
learned with the new microbial CRISPR Cas9 model and improve it further 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 gives scientists a higher-throughput
pipeline to link genotype to phenotype, or genes to physical traits, a field
known as functional genomics. The research has implications for the work of the
ORNL-led Center for Bioenergy Innovation (CBI), for example, to improve
bioenergy feedstock plants and bacterial fermentation of biomass. “We’re greatly
improving our predictions of guide RNA with this research,” Eckert said. “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 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, a bioanalytical chemist who leads the DOE
Genomic Science Program’s Secure Ecosystem Engineering and Design Science Focus
Area, or SEED SFA, that supported 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.” Co-authors on the publication
included ORNL’s William Alexander, Dawn Klingeman, Erica Prates, Carrie Eckert,
Stephan Irle and Daniel Jacobson; Tyler Walker, Jonathan Romero and Angelica
Walker of the Bredesen Center for Interdisciplinary Research and Graduate
Education at the University of Tennessee, Knoxville; and Jaclyn Noshay and David
Kainer, who were formerly with ORNL and now with Bayer and the University of
Queensland, respectively. Funding for the project was provided by the SEED SFA
and CBI, both part of the DOE Office of Science Biological and Environmental
Research Program, by ORNL’s Lab-Directed Research and Development program, and
by the high-performance computing resources of the OLCF and Compute and Data
Environment for Science, both also supported by the Office of Science.
UT-Battelle manages ORNL for DOE’s Office of Science, the single largest
supporter of basic research in the physical sciences in the United States. The
Office of Science is working to address some of the most pressing challenges of
our time. For more information, please visit energy.gov/science. — Stephanie
Seay
