You may be quick to think it’s the biologists and chemists who are running the show in terms of finding treatments and vaccines for the COVID-19 virus but this is simply not the case. Physics based methods such as x-ray crystallography and computer modelling are just a couple examples of how physics is spurring us closer to effective treatments and potential vaccines [1].
X-ray crystallography has been instrumental for researchers at the University of Alberta who have discovered a biological inhibitor which has the potential to be used to treat COVID-19 [1]. This biological inhibitor works by blocking the enzyme in the COVID-19 virus that enables it to replicate [3]. It has been used to treat other types of coronavirus infections in cats and it has been shown to prevent the replication of the COVID-19 virus in monkey lung tissue [2]. While human trials for the antiviral drug await approval by the US Food and Drug Administration, crystallography experiments have been planned at the Stanford Synchrotron Radiation Lightsource (SSRL) to further investigate whether the drug can be optimised for humans [2].
Figure 1: Photograph of the Stanford Synchrotron Radiation Lightsource facility. Taken from: Lightsources.org. 2020. Stanford Synchrotron Radiation Lightsource (SSRL). [online] Available at: <https://lightsources.org/lightsources-of-the-world/americas/stanford-synchrotron-radiation-lightsource-ssrl/> [Accessed 20 October 2020].
The SSRL has recently opened a state-of-the-art crystallography beamline [2]. “We’ll be able to use smaller crystals, collect higher-quality data, get a better signal-to-noise ratio, and collect more data sets per hour than ever before” says Ian Wilson, a representative from Scripps Research (one of the funding bodies for the beamline) [2]. It is clear that physics-based techniques and technologies are at the forefront of our fight against COVID.
Computational physics has also been pivotal in finding ways to fight COVID. The Lawrence Livermore National Laboratory spearheads the use of computational methods for understanding how the COVID-19 virus may interact with potential treatments and antibodies [2]. They have been using physics-based models to understand the dynamics between molecules and proteins with the end goal of discovering potential drug-candidates and antibodies for treating the COVID-19 virus [2]. Jim Brase, who leads the lab at Lawrence explains that they are investigating how complex molecules bind together and the energy of their binding strength [2]. Brase explains that these interactions “are typically million-atom calculations, and you have to let [the atoms] jiggle around and settle into their lowest energy states” [2]. They then repeat these calculations hundreds of times to obtain the average binding energy before moving onto the next potential configuration [2]. So as Brase accurately puts it “you are doing a lot of calculations” [2]. Artificial intelligence has also been crucial for sorting through what is almost an infinite number of amino acid combinations to find molecules which have the potential to combat the COVID-19 virus [2]. The implications for computational physics are far reaching and have never been so important. If this doesn’t make you want to pay more attention in your MATLAB computer labs, then I don’t know what will!
- Sarah
Figure 2: Artist's impression of understanding COVID-19. Taken from: Drug Target Review. 2020. Can A Computer Help Us Find A Treatment For COVID-19?. [online] Available at: <https://www.drugtargetreview.com/article/61581/can-a-computer-help-us-find-a-treatment-for-covid-19/> [Accessed 20 October 2020].
References
1. Cartwright, J., 2020. [online] Available at: <https://physicsworld.com/a/covid-19-how-physics-is-helping-the-fight-against-the-pandemic/> [Accessed 20 October 2020].
2. Kramer, D., 2020. Cats and llamas could offer a path to coronavirus therapies. Physics Today, 73(9), pp.22-25.
3. Vuong, W., Khan, M.B., Fischer, C. et al. 2020. Feline coronavirus drug inhibits the main protease of SARS-CoV-2 and blocks virus replication. Nat Commun 11, 4282, https://doi.org/10.1038/s41467-020-18096-2
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Irving, T., 2020. Understanding The Spread Of COVID-19 Through Physics-Based Modeling Used For Jet Engines. [online] SciTechDaily. Available at: <https://scitechdaily.com/understanding-the-spread-of-covid-19-through-physics-based-modeling-used-for-jet-engines/> [Accessed 20 October 2020].