Showcase Tag: Alzheimer's

Quantum Chemistry of Molecular Evolution

Ph.D. candidate Lázaro A. M. Castanedo and his adviser Chérif F. Matta are trying to answer one of the fundamental questions of molecular evolution: Why did nature pick the particular DNA structure humans have now as a carrier of our genetic information instead of other equally plausible choices? 

“In the absence of constraints, nature’s chemical reactions always choose the path that is the least costly in terms of Gibbs (free) energy,” explains Matta, who is a professor at the Department of Chemistry and Physics at Mount Saint Vincent University. Gibbs (free) energy balances the tendency of nature to increase randomness as time progresses and its tendency to seek the lowest possible energy. “Could nature have made less costly choices than those that led to today’s DNA? If so, why didn’t it?” 

Describing the project, Castanedo, who is pursuing his PhD at Saint Mary’s University in collaboration with Mount Saint Vincent, says he compares the energies of different combinations of building blocks of nucleic acids (that is to say DNA and RNA). In addition, chemistry textbooks provide ample descriptions of the structural and chemical differences between DNA and RNA, but Castanedo and Matta want to know why that is, and if the observed forms are somehow energetically advantageous. 

As an extension of this work, they are also investigating whether some of the nucleotides nature hasn’t chosen have applications in drug development. 

“One of the applications of discovering new nucleotides is that they can be used to produce similar molecular structures for drug discovery,” says Castanedo, who received the 2020 Abe Leventhal Research Bursary from the Alzheimer Society of Nova Scotia for his work in developing molecules to prevent and detect dementias. “They could be used to develop new treatments for Alzheimer’s, cancer, and other diseases.” 

Castanedo says he could do this research in a lab by synthesizing and comparing the components, which would take decades worth of human labour, or, he could approach it theoretically using computers, taking a fraction of the time. 

“We predict what happens in the ‘real world’ using the computational infrastructure of the Digital Research Alliance of Canada and ACENET, which has thousands of cores and a panoply of state-of-the-art computational quantum chemistry software,” he says. “So, you can actually create a model of this molecule on the computer and obtain, among many other properties, its Gibbs energy.”

Castanedo is one of Saint Mary’s heavier users — with 235,000 CPU hours in 2022 alone (about 27 years of CPU time) — because he’s been analyzing a total of 2,530 molecules for this project. 

“For each of these molecules, a CPU needs to run continuously anywhere from, say, 20 to 200 CPU hours. These calculations are computationally intense, as we use high levels of quantum chemical theory, such as density functional theory (DFT), to obtain accurate predictions,” he says.

“They generate a lot of data,” Matta says, “so you need to know what you are looking for — a needle in the haystack so to speak.” 

While preliminary data has already been made publicly available, much of this work is yet to be published. 

Building Better Brain Imaging Analysis Tools

Erin Mazerolle uses brain imaging to understand, for example, what’s happening in the brain of someone with Alzheimer’s disease.

“A lot of the work that I have done is to try to do a better job of understanding and diagnosing neurological diseases, and to take really advanced cutting-edge research imaging techniques — new ways to image the brain — and apply them for the first time in patient populations,” says Mazerolle, assistant professor of psychology and computer science at St. Francis Xavier University.

Over the course of this research, Mazerolle noticed that she and her peers don’t always do a good job at making sure their results are reproducible in other studies.

“If I do a study and publish it, and then another researcher reads it, can they get the same result?” she asks. “This is the part of my work where ACENET really comes in. I have a study going on right now in which we’re trying to figure out how the different ways that we analyze data are impacting the reproducibility of our results.”

She says that when analyzing brain imaging data, there are dozens of steps researchers take, and each one involves deciding which parameter to use at that juncture. And ultimately, all of those decisions impact the final result.

“There’s an analogy that we use called the ‘garden of forking paths.’ I make a decision and go down one garden path and I never know what my results would have looked like over on the other path,” she says.

“So we’re developing software that researchers can use to simultaneously analyze the imaging data using a whole bunch of different paths. That’s a really major computational challenge because these datasets are very large and it takes a lot of computational time and power to get to the end of each of these paths.”

She says the clusters at ACENET reduce the computational challenge to a reasonable time frame, but even with those high-performance computing resources, it can still take a month to do the analysis for a single study.

“It would be completely unfeasible for me to do it without ACENET,” Mazerolle says.

“We hope that by looking at all of the outcomes of the different paths, we get a better sense of whether our results are real, or if the ones that we like only occur down one or two paths,” she says. “Or, do the 200 paths actually look totally different and we never would have known that if we hadn’t tried this multiverse analysis?”

For her multiverse research, she’s using data from the Alzheimer’s Disease Neuroimaging Initiative, an international data-sharing database that looks at Alzheimer’s Disease, cognitive impairment and healthy normal aging. In particular, she’s comparing super-agers (who are in their 80s or 90s and have the memories of 20- and 30-year-olds) and comparing them to people who are aging normally. This is the study that will pilot the tool she hopes will help further brain imaging research across Canada.