Molecular Evolution

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.

Simulating Solutions: The Power of Computational Chemistry

Stijn De Baerdemacker says his research in theoretical chemistry is “fairly fundamental” and yet it doesn’t find itself very far away from real-world applications.

“Typically, when we think about chemistry, it’s about tubes and beakers and people in lab coats, and that’s still a big portion of what of what we do,” De Baerdemacker says. “However, it’s not only running the experiments, it’s also exploring what molecules you can make and how they can solve problems.”

In other words, he’s always looking to develop something new — a better material for a device, or a better drug for a disease, for example. And if you’re searching for the “holy grail” that will solve your problem, it’s very time-consuming, he says, because you have to assess all of the options.

“This is where computations come in, because on a computer, we are not bound by safety requirements,” he says. “We can just go in and try to simulate what will happen. That speeds up the discovery process by many orders of magnitude.”

De Baerdemacker applies various mathematical models that describe the structure of molecules to determine which ones would provide the most accurate results.

“We make sure the methods are grounded in proper theory,” he says. “Once we have the theory down, we run tests and we use ACENET’s resources to put everything into code. We test it on our own local systems and then try them on a bigger system like ACENET,” he says. They then send the code to the chemist, who runs computer simulations using various molecules. This enables the chemist to narrow the options to the most promising candidates before running experiments.

De Baerdemacker says that when he talks to people about high-performance computing and asks them who they think might be HPC’s biggest clients, they’re surprised to learn that chemists are among them. And, he adds, his colleagues in computational chemistry are definitely more frequent users, but even fundamental chemists make good use of services at ACENET.

“We also run a lot of simulations,” he says.

Using a concrete example, he says a lot of diseases are associated with how the proteins in our bodies work and the way they are folded in the cell is also are important for their functionality. Alzheimer’s is one example where a protein starts to curl up and then pierces through a cell membrane,” he says.

“We’re actively looking for a drug that will inhibit that behaviour, but in order to do that, we need to come up with candidates and do a lot of simulations,” he says. “When we’re doing those simulations, we use ACENET.”

But where he has used ACENET to the fullest extent is with machine learning, which he says chemists have been using for 20 years.

“When we’re working on computations, it’s not one variable, it’s millions,” he says. “On machine learning, we’ve discovered that the machine has figured out the kind of molecule we were looking at all by itself. It looked at the data and it found patterns. At this point, ACENET is really crucial.”

Designing New Molecules

Jason Masuda has something in common with the alchemists of old. But instead of trying to turn lead into gold, he’s building substances that have never been seen on Earth before. Masuda is a chemistry professor at Saint Mary’s University who is building new molecules from atoms. He says the idea is to use new combinations of atoms to make an actual physical compound with unique chemical properties. “In organic chemistry there are a number of functional groups – specific groups of atoms within molecules that have very characteristic properties, such as aldehydes, ketones, and alcohols – but what we’re doing is making functional groups that have not been made before. Organic chemists understand how the normal functional groups react. We make a new arrangement of atoms and then we study them and how they react with other molecules. That gives us insights into their reactivity. That’s what chemistry is about. Reactions.” The majority of Masuda’s work is done in the lab using glove boxes in an inert environment because many of the molecules he creates are reactive to water and air. “The compounds that we make are not transient things,” he says. “They are actually something you can physically store in a lab.” Once a new molecule has been created, Masuda studies it and catalogs its properties in a library of molecules. “We purify these molecules, we crystallize them and then we analyze them using a variety of techniques. One of the key methods is by using an Xray defractometer on single crystals. That lets us see the places of the atoms in three dimensions.” Masuda also uses ACENET to conduct electronic structure calculations using software called Gaussian09 – a program that places groups of atoms into a structure that is the most stable configuration.“We use that alongside our experimental work to predict the reactivity of these molecules,” he says. Masuda will often use Gaussian to see ahead of time if a molecule will be stable enough to exist in nature. “Gaussian09 is a great tool to save us from wasting time. Because we’re pushing the boundaries of what nature allows, quite often I will generate a molecule and get it to optimize on ACENET and then, if it looks promising, we’ll make it in the lab. There’s a saying that two hours in the library saves you two months in the lab. It’s the same with ACENET. Sometimes a couple of hours with Gaussian09 will save you weeks or months in the lab.” While Masuda creates molecules in the name of pure science, he says giving scientists such as cancer researchers a palette of molecules to work with will ultimately have many practical applications as well. “Basic science leads to new discoveries,” he says. “Often we have no idea what those discoveries will be ahead of time.”

Developing New Products at the Molecular Level

Erika Merschrod is a professor of chemistry at Memorial University and an expert in the field of materials science. She designs, optimizes and tests new materials that respond to the environment in particular ways. Merschrod and her colleagues design, manufacture and study large molecules that have complex structures contained within them, particularly materials that form films. They look for unique properties that can be used to develop useful products, such as colour changes that happen when the films come in contact with other substances. The group then uses those properties to design coatings that can be used in applications like antifouling coatings, sensors and medical tissue development. Modelling the properties of a film, as opposed to an individual molecule, is a complicated process. “Once molecules touch each other, they behave differently than they would if they were just single molecules,” says Merschrod. While Merschrod ultimately tests the materials she develops in a wet lab, she says computer modelling is also essential to the process. “Modelling is crucial. It might take us 10 or 12 steps to develop a material. That can take a lot of time in a lab. If you can use computer modelling to predict ahead of time the kinds of materials you’re going to produce, that can save a lot of time.” “We focus on sensing capabilities, things like colour changes that the molecules help to amplify. One of the questions we try to answer is how do you transform a chemical event into something a human can see. If we can answer that question we can develop materials that detect trace elements that a layperson can use.” “We also develop hierarchical materials computationally. Constructing a multi-scale system is not trivial because of the computational cost, but this forces us to consider and test what the important features of a given system are.” Merschrod says that the ACENET system is useful even to scientists who don’t have a background in computing. “ACENET has a real service oriented approach. That’s one reason it works so well. It’s designed to help real high performance users but it also has a core group of users who are not theorists. It works because the focus is on ease of use; because of the excellent support staff. That support means that my students don’t have to be experts to use it.”

Pushing the Envelope (or Molecule) with Quantum Chemistry

Dr. Jason Pearson’s research team of seven at the University of Prince Edward Island strives to understand the properties of molecules at the electronic level; why they behave the way they do, why some interactions are strong while others are not, and ultimately predict how molecules will behave when interacting with other molecules. It’s a broad scope. They do this by developing and applying computational algorithms. For example, an experimental researcher might have samples from which they are searching for new compounds. Along the way, they might find something with interesting properties, but need to identify the chemical substance. This is where Dr. Pearson’s group can take the experimental data, build every possibility into models and then run simulations with the data that ultimately allows them to understand the molecule. Researchers then can examine the molecule through a new lens so to speak. But it doesn’t stop there. Along the way, Pearson is developing new computational tools and new simulation techniques useful for other researchers. Already a resource to colleagues at the University, Pearson’s long-term focus is to utilize and develop computational technology for the purpose of designing new molecules and materials. His group employs a two-pronged approach whereby state-of-the-art simulation techniques are applied to probe the molecular level of detail in chemical systems, allowing for accurate quantifications of structure, interactions, and mechanisms of action. Simultaneously, they design the next generation of computational algorithms and methodologies for the investigation of electronic structure, specifically focused on the concept of the electron pair. The methods developed in Pearson’s lab can be applied to almost all matter, and therefore can be used in many fields, such as designing new materials, new industrially relevant catalysts and nanotechnologies to name a few. Pearson hopes to further push the bounds of what’s possible in computational chemistry by gathering large data sets in the chemical sciences, creating a database for others to access and utilizing the data to develop new simulation techniques that are faster and more accurate. For example, this approach could enable very accurate simulations on large systems like biomolecules and other polymers. His goal is to develop algorithms that behave like a robot, travelling through the data sets, collecting data and solving problems. The chemistry research that is enabled by Dr. Pearson’s group is addressing a broad scope of the world’s most important technological and societal challenges, such as climate change, energy, security, food supply and health.

Understanding Polymers

James Polson spends much of his time trying to understand the mechanisms that make life possible. Polson is an associate professor of physics with the University of Prince Edward Island. He studies polymers – a group of long chainlike molecules composed of many repeated chemical subunits. Proteins and DNA are natural biopolymers that are essential to biological structure and function. Polson uses computer simulations and analytical theoretical methods to study the physical properties of polymers in confined and crowded environments. “We’re carrying out numerically intensive calculations to study model systems under conditions that are relevant to recent experiments using DNA,” he says. Polson’s research falls into three categories. The first is called polymer translocation – the study of what happens when polymer molecules are driven through a narrow hole in a barrier; a process similar to threading a needle. “There’s a lot of interest in this,” he says. “Understanding the basic physics of this process will provide insight useful for developing new translocation-based technologies to sequence DNA.” The second category of research involves studying the effects of squeezing polymers into narrow channels. In such environments, the molecules sometimes sample folded states, and Polson uses simulations to quantify the tendency for polymers to exist in these states. The results will be useful for the development of the technique of genomic mapping, which involves confining DNA in nanochannels. Polson is also studying the propensity of confined polymers to self-segregate – a process that is relevant to chromosome separation in bacteria. “Before a bacterium or a cell divides, it first replicates its chromosomes,” he says. “But there is a lot of uncertainty into the mechanisms involved in pulling those chromosomes apart. One possibility is that entropy provides the main driving force, and our simulations will provide insight into the importance of this effect.” As part of his research, Polson uses the ACENET computer network to create virtual models of polymer molecules to simulate their behaviour under various conditions. While the models are highly simplistic and designed to capture only the most basic features of real molecular systems, the simulations are nevertheless very time-consuming. “What I need from ACENET is computer power. For each calculation I need 100 to 200 processors for one to two days, and dozens of such calculations are needed for any given project.” Polson says the ongoing support he receives from the ACENET staff is crucial to his work. “Every summer I have students working with me. There are a lot of computer skills that they have to learn, but ACENET is really good at giving tutorials for newbies.”

Studying the Duplication of Genes

The duplication of DNA through the process of mitosis is essential to life. But that process doesn’t always go as planned. Sometimes the copies are flawed and the genes contained within them are mutated – the process that drives evolution. Sometimes extra copies of genes are produced. Those extra copies, or gene duplications, work like other gene mutations: sometimes the effect is good; other times it is bad or has no effect at all. Denise Clark is a geneticist and a professor of biology at the University of New Brunswick in Fredericton.She studies those gene duplications as part of her research and she’s using the ACENET computer network to do it. “When two copies of a gene are both functioning within a genome they can take on different roles,” says Clark. “We’re interested in how the the duplicated genes function and also in the mechanism that causes the duplications to arise.” Fruit flies are the primary organism Clark studies. The tiny insects are the gold standard for this kind of genetic research because they reproduce quickly and also because they have already been studied extensively by geneticists for many years. “There has been a determination of the entire DNA sequence, or “genome”, for hundreds individual fruit flies by several fruit fly research groups to look at genetic variation in populations,” she says. “That next-generation DNA sequencing data is available for anyone to look at.” “A couple of years ago I started using ACENET to look for duplicates. If those duplicates are localized – if they’re not present around the world – we can assume they are new.” ACENET provides a crucial piece of the puzzle. The file for one fruit fly genome is gigabytes in size, says Clark. The sheer size of the data that needs to be analyzed would quickly overtax a standard computer system. “I’ve looked at 500 genomes using ACENET. That’s something I couldn’t do on my laptop.” The job is made easier because ACENET has installed a number of genome tools that Clark can use off the shelf. It allows her to look at hundreds of genomes in parallel or string together the tools she wants to use in a pipeline.”I’m not building any new tools, except for the specific pipeline that lists what I need. I start with a genome file that’s gigabytes in size and at the end I’m left with a file that’s hundreds of kilobytes,” she says. Clark says that now that technology to sequence genes has gotten much cheaper and faster, there has been an explosion of new genetic knowledge and ideas recently. “A lot of geneticists’ ideas about genomes and genetic variation have changed since the development of next-generation sequencing technology.”