(Nanowerk News) Viruses kill millions of people around the world every year. “In addition to the new coronavirus, the main viral killers include hepatitis, HIV and HPV,” said Lela Vukovic, assistant professor of chemistry at the University of Texas at El Paso.
Researchers are constantly trying to find new therapies that will help prevent infection or work therapeutically to reduce symptoms of one virus at a time. “Another strategy,” said Vukovic, “would be to find therapies that are broad-spectrum and act simultaneously on a number of different viruses. ”
Many viral infections start with the virus binding to heparan sulfate molecules on the surface of the host cell. Together with experimenters led by Francesco Stellacci from the Swiss Federal Institute of Technology in Lausanne (EPFL) and in collaboration with Petr Král from the University of Illinois at Chicago, Vukovic has helped to study nanoparticles with solid nuclei and attached ligands that mimic heparan sulfate molecules. and their microscopic action on several viruses.
They found that nanoparticles containing certain ligands can attach themselves to viruses, which soon after can disintegrate.
“Such virus-destroying materials can be prepared,” Vukovic said at a recent seminar at the Texas Advanced Computing Center (TACC). “The question is: are there any clues we can get from computer modeling to design new and better materials and understand the mechanism that causes the virus capsid to rupture?
Since nanoparticles are tiny, they cannot be visualized clearly at the atomic level and at microsecond time scales at which reactions occur. Vukovic therefore created models of the atomic structure of the virus, as well as nanoparticles with ligands of different lengths attached.
Using TACC supercomputers, she simulated how viral proteins and nanoparticles interact with each other. She found that the virus binds and makes many contacts with longer ligands.
Not only that. Nanoparticles attach themselves to the junction of two proteins and, like a wedge, increase the distance between viral proteins, breaking contacts and disintegrating the virus. The first results of the research were published in Natural materials in 2018 (“Broad-spectrum, non-toxic antiviral nanoparticles with a virucidal inhibition mechanism”), and new results, obtained by student Parth Chaturvedi, have been published on bioRxiv (“Computer modeling of the virucidal inhibition mechanism for broad spectrum antiviral nanoparticles and HPV16 capsid segments”).
Nuanced designs of nanosensors
Vukovic’s interest in modeling nanoparticles for medicine led her to her next project, helping to design nanosensors that are small, fast, and sensitive enough to detect microscopic amounts of neurotransmitters in the brain.
The basis of the technology are carbon nanotubes – cylinders 10,000 times narrower than the average human hair – which have found applications in a variety of fields including electronics, optics and, more recently, medicine. .
Carbon nanotubes, or CNTs, the researchers found, have an unusual property. They can spontaneously illuminate under certain circumstances with detectable light outside the body. However, they cannot function in the body without modification.
One proven approach is to wrap the CNT in DNA. The Landry lab at the University of California at Berkeley was experimenting with DNA strands of different lengths and constitutions to see if CNT gave off strong light emission when exposed to dopamine, and got mixed results.
“The screening approach works, but it doesn’t provide a clear understanding of why it works or how to design it better in the future. Can we do something more systematic? Vukovic asked.
She undertook a series of computer experiments on Stampede2, TACC’s main supercomputer at the time, exploring the 3D structure, energy landscape, and binding patterns of DNA-wrapped CNTs.
She and her student Ali Alizadehmojarad discovered that DNA of certain lengths wraps around the nanotube like a ring, while others wrap it in a helix or irregularly. These different binding patterns lead to different luminescence in the presence of neurotransmitters. The CNT coiled into a ring of a type of DNA, according to her and the Landry lab, was much more effective at detecting and signaling the presence of neurotransmitters. The research has been published in a series of articles in Nano letters in 2018 (“Ultralarge Modulation of Fluorescence by Neuromodulators in Carbon Nanotubes Functionalized with Self-Assembled Oligonucleotide Rings”) and Advanced material interfaces in 2020 (“Binding Affinity and Conformational Preferences Influence Kinetic Stability of Short Oligonucleotides on Carbon Nanotubes”).
The challenges and achievements of the sensor project inspired a revelation in Vukovic.
She had successfully explored the experimental mysteries of CNTs at the atomic level using molecular dynamics simulations and provided critical information. “But I’m only doing one molecule at a time,” Vukovic said. “As a theorist, what can I contribute? If I test 10 molecules, I don’t even scratch the surface.”
Her awareness led her to integrate AI and data-driven methods into her approach. “We’ve completely changed our research; learned new methods. Over the past two years we’ve been working on this.”
This period of growth and learning led Vukovic and his team, Payam Kelich and Huanhuan Zhao, to their most recent project: working with the Landry laboratory on the discovery of new optical sensors made up of DNA-CNT conjugates to detect the DNA molecule. serotonin. As a key molecule that has an impact on our mood and happiness, there is great interest in detecting the presence and amounts of serotonin in different body tissues.
Recently, Vukovic’s lab developed new AI-based computational tools that train models to learn from Landry’s experimental data and predict new serotonin sensors.
The collaboration is bearing fruit. A first article, which has just been published on bioRxiv (“Machine learning enables discovery of carbon-DNA nanotube sensors for serotonin”), describes efforts to computer predict new serotonin sensors and experimentally validate the predictions. So far, the approach has led to the discovery of five new serotonin DNA-CNT sensors with a higher response than that observed in previous sensors. (This research is funded by a new grant from the National Science Foundation.)
Vukovic is able to meet these massive and ambitious computing challenges in part thanks to his access to some of the most advanced scientific instruments on the planet through the University of Texas Research Cyberinfrastructure (UTRC) program. Launched in 2010, the initiative provides powerful computing and data resources free of charge to Texan scientists, engineers, students, and academics at UT’s 13 institutions.
“None of these projects would have been possible without TACC,” said Vukovic. “When we were ready to run we were given the time we needed and we were able to move quickly and get things done.”
As a computational chemist, Vukovic says she tries to use her knowledge to contribute to practical applications in medicine and beyond. “We think deeply about how to contribute and work on projects where IT can make a real difference.”