California: New research on protein structures by Stanford University, California claims to help drug development and predict future outbreaks of COVID-19.
According to the study in the open-access journal PLOS Computational Biology, some animals are more susceptive of the COVID-19 infection than others, and this may be due to particular protein structures on the surface of animal cells.
Joao Rodrigues of Stanford University, California, and colleagues presented the research suggesting that the current pandemic began when the virus causing COVID-19, SARS-CoV-2, jumped from bats or pangolins to humans. Alike other animals, such as cattle and cats, appear to be susceptible to COVID-19, while others, such as pigs and chickens, are not.
One zoo even reported infections in tigers. However, it was unclear why some animals were immune and others were not.
The researchers looked for clues in the first step of infection when SARS-CoV-2’s ‘spike’ protein binds to an ‘ACE2′ receptor protein on the surface of an animal cell. Computers were used to simulate the 3D structures of the protein and investigate how the spike protein interacts with different animals’ ACE2 receptors.
The researchers found that the animals, including humans who are susceptible to infection, were those whose ACE2 ‘locks’ fit the viral ‘key’ better. Despite being approximations, the simulations pinpointed certain structural features unique to the ACE2 receptors of these susceptible species. The analysis suggests that other species are immune because their ACE2 receptors lack these features, leading to weaker interactions with spike proteins.
The study examined that these findings were able to aid the development of antiviral strategies that use artificial ‘locks’ to trap the virus and prevent it from interacting with human receptors. They were also capable to improve models to monitor animal hosts from which a virus could potentially jump to humans, ultimately preventing future outbreaks.
Rodrigues told that his team would continue refining the computational tools used in this study. “Thanks to open-access data, preprints, and freely available academic software, we went from wondering if tigers could catch COVID-19 to having 3D models of protein structures offering a possible explanation as to why that is the case in just a few weeks,” he said.