As the COVID-19 pandemic scattered and isolated people, researchers across Virginia Tech connected for a data-driven collaboration seeking improved drugs to fight the disease and potentially many other illnesses.

A multidisciplinary collaboration spanning several colleges at Virginia Tech resulted in a newly published study, “Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios,” in the Journal of Physical Chemistry Letters.

The study focuses on using computer algorithms to generate adaptations to molecules in compounds for existing and potential medications that can improve those molecules’ ability to bind to the main protease, a protein-based enzyme that breaks down complex proteins, in SARS-CoV-2, the virus that causes COVID-19.

This process allows exponentially more molecular adaptations to be considered than traditional trial-and-error methods of testing drugs one by one could allow. Candidate molecule adaptations can be identified among myriad possibilities, then narrowed to a few or one that can be created in a laboratory and tested for effectiveness.

“We present a novel transferable data-driven framework that can be used to accelerate the design of new small molecules and materials, with desired properties, by changing the combination of building blocks as well as decorating them with functional groups,” said Sanket A. Deshmukh, associate professor of chemical engineering in the College of Engineering. A “functional group” is a cluster of atoms that generally retains its characteristic properties, regardless of the other atoms in the molecule.

“Interestingly, the newly designed functionalized drug not only had a better half maximal effective concentration value than its parent drug, but also several of the proposed and used antivirals including Remdesivir,” Deshmukh said, referring to a measure of compound potency.

Moving through all the phases of the study would not have been possible without extensive cross-departmental collaboration. 

Four Virginia Tech faculty members – Deshmukh;  Anne M. Brown, associate professor with University Libraries and the Department of Biochemistry in the College of Agriculture and Life Sciences;  Andrew Lowell, assistant professor in the Department of Chemistry in the College of Science; and James Weger-Lucarelli, assistant professor in the Department of Biomedical Sciences and Pathobiology at the Virginia-Maryland College of Veterinary Medicine  — are among 13 co-authors of the published study. 

Deshmukh group’s expertise in developing transferable computational models and frameworks for accelerated design of drug-like small molecules and materials, and Brown’s extensive computational expertise in protein structure-function relationships meshed seamlessly as a baseline for the study.

“Sanket’s group had a molecular repurposing framework, and I have experience with exploiting protein targets,” said Brown. “Combined with Andrew who does the synthesis, which is to make the compound, and then James doing the testing and the viral assays, we formed a fantastic collaboration.”

But the faculty members stress that it was the graduate students and postdoctoral students in the laboratories who made the study possible. Nine of them are co-authors: Samrendra K. Singh, chemical engineering, Kelsie King, genetics, bioinformatics, and computational biology; Cole Gannett, chemistry; Christina Chuong, biomedical and veterinary sciences; Soumil Y. Joshi, chemical engineering; Charles Plate, chemical engineering; Parisa Farzeen, chemical engineering; Emily M. Webb, entomology; and Lakshmi Kumar Kunche, chemical engineering.

The professors said the students communicated well with one another without any prompting from their mentors.  “I think one of the great things to see is the students really talking with one another and collaborating with one another as well without us having to say ‘Do this,’” Deshmukh said. 

Finally, the functionalized molecules were tested against live SARS-CoV-2 in a veterinary college laboratory by Weger-Lucarelli and his team.

“Initial virtual screening of the existing database identified a parent compound that was expected to inhibit the protease of SARS-CoV-2,” Weger-Lucarelli said. “Then the data-driven framework altered the structure of that molecule to enhance that activity. We compared those side by side to show that this new compound that was expected to be more potent against SARS-CoV-2 than the parent compound was, in fact, more potent against SARS-CoV-2.”

The process to develop and test a functionalized molecule against COVID-19 has many potential applications even beyond mitigation of COVID-19. Studies are ongoing among the team to employ the same type of research to find functionalized molecules that may be able to treat hepatitis E, dengue fever and chikungunya, the latter two being mosquito-borne illnesses.

“Another direction we’re going in is that we’re targeting proteases and enzymes from other viruses and trying to design other new molecules,” Lowell said.

The algorithm process also has potential in non-biological uses, Sankit said. The “approach is very versatile and is being applied to functionalize and design other materials such as metal organic frameworks (MOFs), glycomaterials, polymers, etc.,” the paper states.

The assembled interdisciplinary team is planning to continue its collaborations.

“None of us could do this work without the other people in this collaboration,” Weger-Lucarelli said.

“This is a great example of  the synergy between going from computational prediction to chemical synthesis to testing in viruses,” Brown said, “and how we at Virginia Tech are really emphasizing that interplay between these three areas and taking that to the next level to develop strong collaborative teams.”

Ever since Antonie van Leeuwenhoek discovered the world of bacteria through a microscope in the late seventeenth century, humans have tried to look deeper into the world of the infinitesimally small.

There are, however, physical limits to how closely we can examine an object using traditional optical methods. This is known as the ‘diffraction limit’ and is determined by the fact that light manifests as a wave. It means a focused image can never be smaller than half the wavelength of light used to observe an object.

Attempts to break this limit with “super lenses” have all hit the hurdle of extreme visual losses, making the lenses opaque. Now physicists at the University of Sydney have shown a new pathway to achieve superlensing with minimal losses, breaking through the diffraction limit by a factor of nearly four times. The key to their success was to remove the super lens altogether.

The research is published today in Nature Communications.

The work should allow scientists to further improve super-resolution microscopy, the researchers say. It could advance imaging in fields as varied as cancer diagnostics, medical imaging, or archaeology and forensics.

Lead author of the research, Dr Alessandro Tuniz from the School of Physics and University of Sydney Nano Institute, said: “We have now developed a practical way to implement superlensing, without a super lens.

“To do this, we placed our light probe far away from the object and collected both high- and low-resolution information. By measuring further away, the probe doesn’t interfere with the high-resolution data, a feature of previous methods.”

Previous attempts have tried to make super lenses using novel materials. However, most materials absorb too much light to make the super lens useful.

Dr Tuniz said: “We overcome this by performing the superlens operation as a post-processing step on a computer, after the measurement itself. This produces a ‘truthful’ image of the object through the selective amplification of evanescent, or vanishing, light waves.

Co-author, Associate Professor Boris Kuhlmey, also from the School of Physics and Sydney Nano, said: “Our method could be applied to determine moisture content in leaves with greater resolution, or be useful in advanced microfabrication techniques, such as non-destructive assessment of microchip integrity.

“And the method could even be used to reveal hidden layers in artwork, perhaps proving useful in uncovering art forgery or hidden works.”

Typically, superlensing attempts have tried to home in closely on the high-resolution information. That is because this useful data decays exponentially with distance and is quickly overwhelmed by low-resolution data, which doesn’t decay so quickly. However, moving the probe so close to an object distorts the image.

“By moving our probe further away we can maintain the integrity of the high-resolution information and use a post-observation technique to filter out the low-resolution data,” Associate Professor Kuhlmey said.

The research was done using light at terahertz frequency at millimetre wavelength, in the region of the spectrum between visible and microwave.

Associate Professor Kuhlmey said: “This is a very difficult frequency range to work with, but a very interesting one, because at this range we could obtain important information about biological samples, such as protein structure, hydration dynamics, or for use in cancer imaging.”

Dr Tuniz said: “This technique is a first step in allowing high-resolution images while staying at a safe distance from the object without distorting what you see.

“Our technique could be used at other frequency ranges. We expect anyone performing high-resolution optical microscopy will find this technique of interest.”

DOWNLOAD images at this link.

INTERVIEWS

Dr Alessandro Tuniz                                    Associate Professor Boris Kuhlmey
School of Physics                                        School of Physics
The University of Sydney                            The University of Sydney
alessandro.tuniz@sydney.edu.au               boris.kuhlmey@sydne.edu.au

MEDIA ENQUIRIES

Marcus Strom | marcus.strom@sydney.edu.au | +61 474 269 459

Research paper: A Tuniz & B Kuhlmey, ‘Subwavelength terahertz imaging via virtual superlensing in the radiating near field’, Nature Communications (2023)
DOI: 10.1038/s41467-023-41949-5 (Available on request)

DECLARATION

The authors declare no competing financial interests. Research was in part funded by the Australian Research Council.