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3D models reveal why some animals don't get coronavirus

This technology could help prevent future coronavirus outbreaks.

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Early on in the pandemic, it became clear Covid-19 had made the leap from animals to humans.

The exact chain of transmission isn't known, but the science so far suggests bats played a starring role. After a tiger contracted the virus, scientists started to ask: What other animals can get Covid-19?

A new study published Thursday in PLOS Computational Biology offers molecular clues to which of the animals we come in closest contact with are most susceptible to coronavirus. And, perhaps, more importantly, the study shows which animals are least susceptible to infection.

Pangolins, which were blamed in the past for spreading Covid-19 to humans, score most highly on the susceptible list. Mice are the least susceptible. Cats fall somewhere in the middle; and despite reports of dogs with Covid-19, the study's results were inconclusive when it comes to man's best friend.

The key, this study suggests, may lie in a single molecule carried by some animals and not others.

Protein Power — Covid-19 infection occurs when the spike protein of SARS-CoV-2 binds to specific receptors on cells, allowing the virus to enter animal (and human) cells and start replicating.

The receptor in question is known as the ACE2 receptor protein, and it lies on the surface of the cell. It is this protein that forms the basis of the new study. The researchers used a unique form of computer modeling to generate 3D protein models.

"Our hypothesis was that there must be similarities in the amino acid sequence of the ACE2 receptor of susceptible species and that's exactly what we found," João Rodrigues, lead author on the study and a postdoctoral research fellow in structural biology at Stanford University, tells Inverse.

The 3D models enabled the researchers to test how the virus' spike protein interacts with receptor proteins from the cells of 28 different animals as diverse as guinea pigs and ducks.

To see whether the proteins on the animal's cells interacted with the virus' spike protein, the researchers used a scientific measurement known as a HADDOCK score, named for the gruff Captain Archibald Haddock from the Tintin comics.

This figure from the study shows how each animal's HADDOCK score compares:

Species are ordered in increasing order of HADDOCK score. Species for which coronavirus susceptibility is unknown are shown in gray.

"The HADDOCK score is an indicator of how well two proteins fit together, sort of like a key in a lock," Rodrigues says.

Some proteins fit better than others — much like Cinderella's slipper, if the proteins don't gel together, then the virus can't enter the cell. As a result, the HADDOCK score can reveal any given animal's likelihood of becoming infected. Surprisingly given their reputation as plague-carriers, rats' scores suggest they are less likely to get coronavirus than humans, cats, or even cows.

"Good fits will have lower scores. In our study, non-susceptible species have higher scores than susceptible species," Rodrigues says.

The higher the HADDOCK score, the less susceptible the species is to the coronavirus. But to fully understand the implications of each animal's score, it has to be weighed in relation to other animals' — a mouse scores -93.2 in the model, for example, which may not seem great, but it's considerably higher than humans' score of -116.2.

"This difference in score is because the mouse ACE2 has certain mutations compared to the human variant that we predict make it bind less well to the viral spike protein," Rodrigues says.

Most of the non-susceptible species in their model also have this same mutation inhibiting protein binding, Rodrigues explains. The mutation is the key to understanding why some animals are susceptible to the coronavirus, while others are not.

Future Coronaviruses — The researchers hope the protein modeling technology they use in this study could help prevent future novel coronavirus outbreaks in humans.

"Armed with this knowledge, we should be able to build models that predict — emphasis on predict — which species are susceptible to this and other coronaviruses and that could be potential animal reservoirs," Rodrigues says.

Essentially, if you understand which animals are capable of becoming infected with coronaviruses at all, you can potentially stop the chain of transmission to humans.

"Our protocol is readily applicable to other coronaviruses, as long as we know the structures of the viral spike protein and of the receptor to which it binds to," Rodrigues says.

Limitations — The researchers are upfront about two key limitations to their study. The research uses 3D protein models, and does not look at real-time Covid-19 cases.

"First, while our models do agree with experimental data, for the most part, there is always a degree of uncertainty due to the computational nature of our work," Rodrigues says.

"This means we can make educated guesses about how the virus spike binds to the hosts' ACE2 receptors and about which amino acids of the receptor play an important role in this process," Rodrigues says.

"It does not mean, however, that our results can be used to say, enact policies affecting animal health or that the general public should look at our results as a 'ruler' for risk for Covid for pets," he adds.

3D structure model of the receptor-binding domain of SARS-CoV-2 (top) bound to the human ACE2 receptor (bottom). The two highlighted amino acids are part of a set of interactions that is conserved in animal species susceptible to infection by the virus but are absent from immune species.Credit: João Rodrigues

And while the binding of spike protein to the ACE2 receptor is important, it's "only one early step of the entire viral infection process," Rodrigues says.

"So, even if our models correctly predict strong binding of the spike protein to ACE2 there is a chance that other subsequent steps fail and therefore there is no productive infection," he says.

Therapy Time — Recent developments in artificial intelligence have helped overcome what's known as the "protein folding problem," which occurs when researchers are unsure of the shapes that folded proteins form.

DeepMind's AI technology AlphaFold enables scientists to predict protein structure using its models. It opens a new pathway for biological research.

The timing is good, too, as Rodrigues' team hopes other scientists will use their findings to create therapeutic drugs to tackle the coronavirus' harmful effects on the human body.

"Since we made our protocols completely open-source, interested researchers can build on our results and refine them to their liking, for example, to test which variants of human ACE2 would bind the spike protein best," Rodrigues says.

The proposed therapy works through mutations that "enhance binding" of ACE2 to the spike protein, according to Rodrigues' model.

"One form of therapy being developed is to create artificial versions of human ACE2 that have these and other mutations and use them as 'traps' for the virus," Rodrigues says.

By tricking the virus into binding to the traps rather than our own ACE2 receptors, it would allow the body to "buy time for our immune system to mount a counter-attack," Rodrigues says.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results.
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