3-D protein modeling suggests why COVID-19 infects some animals, but not others

3D protein modeling suggests why COVID-19 infects some animals, but not others
3D structure model of the receptor-binding domain of SARS-CoV-2 (in blue) interacting with the human ACE2 receptor (in gray). Amino acids important to the interaction, which are present only in COVID-susceptible animal species are highlighted in yellow. Sugars bound to the proteins are shown in pink. Credit: Rodrigues et al. 2020 (CC-BY 2.0)

Some animals are more susceptible to COVID-19 infection than others, and new research suggests this may be due to distinctive structural features of a protein found on the surface of animal cells. João Rodrigues of Stanford University, California, and colleagues present these findings in the open-access journal PLOS Computational Biology.


Previous research suggests that the current pandemic began when the virus that causes COVID-19, SARS-CoV-2, jumped from bats or pangolins to humans. Certain 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 are immune and others are not.

To address this question, Rodrigues and colleagues 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. They used computers to simulate the proteins’ 3-D structures and investigate how the spike protein interacts with different animals’ ACE2 receptors—similar to checking which locks fit a certain key.

The researchers found that certain animals’ ACE2 “locks” fit the viral “key” better, and that these animals, including humans, are susceptible to infection. Despite being approximations, the simulations pinpointed certain structural features unique to the ACE2 receptors of these susceptible species. The analysis suggest that other species are immune because their ACE2 receptors lack these features, leading to weaker interactions with spike proteins.

These findings could aid development of antiviral strategies that use artificial “locks” to trap the virus and prevent it from interacting with human receptors. They could also help improve models to monitor animal hosts from which a virus could potentially jump to humans, ultimately preventing future outbreaks.

“Thanks to open-access data, preprints, and freely available academic software, we went from wondering if tigers could catch COVID-19 to having 3-D models of protein structures offering a possible explanation as to why that is the case in just a few weeks,” Rodrigues says.

His team plans to continue refining the computational tools used in this study.


Dozens of mammals could be susceptible to SARS-CoV-2


More information:
Rodrigues JPGLM, Barrera-Vilarmau S, M. C. Teixeira J, Sorokina M, Seckel E, Kastritis PL, et al. (2020) Insights on cross-species transmission of SARS-CoV-2 from structural modeling. PLoS Comput Biol 16(12): e1008449. doi.org/10.1371/journal.pcbi.1008449
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Alpha animals must bow to the majority when they abuse their power

Alpha animals must bow to the majority when they abuse their power
Vulturine guineafowl occur in the savannahs of Kenya. The birds live in groups, with a strict dominance hierarchy. Credit: © Danai Papageorgiou

Many animal groups decide where to go by a process similar to voting, allowing not only alphas to decide where the group goes next but giving equal say to all group members. But, for many species that live in stable groups—such as in primates and birds—the dominant, or alpha, group members often monopolize resources, such as the richest food patches and access to mates. Scientists at the Max Planck Institute of Animal Behavior and the Cluster of Excellence Centre for the Advanced Study of Collective Behavior at the University of Konstanz have studied the links between dominance and group decision-making in wild vulturine guineafowl. They report that democratic decision-making plays an essential role in mitigating the power of alphas by deciding where to move next if those alphas are monopolizing resources.


Vulturine guineafowl are large birds native to savannahs of East Africa. They are the first bird species to have been reported to live in a multilevel society where social groups comprising from 15 to more than 60 individuals interact preferentially with other social groups. Within these large groups, there is a clear dominance hierarchy. Like in wolves and primates, the dominant, or alpha, group members can outcompete other group members and exclude them from food.

While it had long been thought that alphas lead the way and decide where the group moves next, studies over the past decade have suggested that all group members can have equal say by ‘voting’ for where the group goes next. However, it has remained to be determined whether this form of democratic decision-making exists in order to keep the power of dominants in check. “Working together as a group is critical for these birds, as their bright plumage makes isolated individuals easy targets for predators such as leopards and martial eagles,” says Damien Farine, the senior author of the study and lead research on the vulturine guineafowl project.

Despotic leadership versus democratic decision-making

The scientists found that who initiated, and therefore decided where the group moved to next, was dependent on the recent actions of the dominant group members. When groups were feeding in large spacious areas, where distributed food was equally accessible to everyone, then all group members contributed equally. However, when dominant individuals monopolized a particularly rich food patch—chasing other group members out—then the excluded subordinates combined their votes to move the group away from the patch, ultimately forcing the dominants to abandon their rich resources. These findings suggest democratic decision-making, as opposed to despotic leadership, has evolved so that all group members can obtain the resources (e.g. food and water) that they need to survive. This would not be possible if dominant individuals always decided what was best for themselve.

The researchers combined observations on foot, video tracking, and high-resolution GPS tracking across multiple groups of vulturine guineafowl, spanning several years. They first recorded all disputes between individuals

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Fossils purported to be world’s earliest animals revealed as algae

Nov. 23 (UPI) — Fossils previously heralded as the earliest evidence of animal life have been revealed to be algae. The reinterpretation, announced Monday in the journal Nature Ecology and Evolution, will force scientists to reconsider early animal evolution.

“It brings the oldest evidence for animals nearly 100 million years closer to the present day,” study co-author Lennart van Maldegem said in a news release.

“We were able to demonstrate that certain molecules from common algae can be altered by geological processes — leading to molecules which are indistinguishable from those produced by sponge-like animals,” said van Maldegem, a postdoctoral research fellow at the Australian National University.

The new research reverses the trend of fresh discoveries pushing the emergence of animal life further and further back on the evolutionary timeline.

For decades, scientists have struggled to pinpoint the origins of animal life, but recently, a series of discoveries suggested sponge-like animals began proliferating in Earth’s oceans during the Ediacaran Period, as many 635 million years ago.

“Ten years ago, scientists discovered the molecular fossils of an animal steroid in rocks that were once at the bottom of an ancient sea in the Middle East,” said study co-author Jochen Brocks.

“The big question was, how could these sponges have been so abundant, covering much of the seafloor across the world, but leave no body fossils?” said Brocks, an ANU professor.

It turns out, sponges weren’t abundant — they didn’t exist yet.

Though it’s true that sponges remain the only organisms that produce the steroids of note, the latest research suggests ocean chemistry can convert algae sterols into ‘animal’ sterols.

“These molecules can be generated in the lab when simulating geological time and temperatures, but we also showed such processes did happen in ancient rocks,” said ANU researcher Ilya Bobrovskiy, who first discovered the steroid fossils 10 years ago.

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Researchers suggest AI can learn common sense from animals

AI researchers developing reinforcement learning agents could learn a lot from animals. That’s according to recent analysis by Google’s DeepMind, Imperial College London, and University of Cambridge researchers assessing AI and non-human animals.

In a decades-long venture to advance machine intelligence, the AI research community has often looked to neuroscience and behavioral science for inspiration and to better understand how intelligence is formed. But this effort has focused primarily on human intelligence, specifically that of babies and children.

“This is especially true in a reinforcement learning context, where, thanks to progress in deep learning, it is now possible to bring the methods of comparative cognition directly to bear,” the researchers’ paper reads. “Animal cognition supplies a compendium of well-understood, nonlinguistic, intelligent behavior; it suggests experimental methods for evaluation and benchmarking; and it can guide environment and task design.”

DeepMind introduced some of the first forms of AI that combine deep learning and reinforcement learning, like the deep Q-network (DQN) algorithm, a system that played numerous Atari games at superhuman levels. AlphaGo and AlphaZero also used deep learning and reinforcement learning to train AI to beat a human Go champion and achieve other feats. More recently, DeepMind produced AI that automatically generates reinforcement learning algorithms.

On the human cognition side, at a Stanford HAI conference earlier this month DeepMind neuroscience research director Matthew Botvinick urged machine learning practitioners to engage in more interdisciplinary work with neuroscientists and psychologists.

Unlike other methods of training AI, deep reinforcement learning gives an agent an objective and reward, an approach similar to training animals using food rewards. Previous animal cognition studies have looked at a number of species, including dogs and bears. Cognitive behavioral scientists have discovered higher levels of intelligence in animals than previously assumed, including dolphins’ self-awareness, and crows’ capability for revenge.

Studies on animals’ cognitive abilities may also inspire AI researchers to look at problems in a different way, especially in deep reinforcement learning. As researchers draw parallels between animals in testing scenarios and reinforcement learning agents, the idea of testing AI systems’ cognitive abilities has evolved. Other forms of AI, like assistants Alexa or Siri, for example, cannot search a maze for a box containing a reward or food.

Published in CellPress Reviews, the team’s paper — “Artificial Intelligence and the Common Sense of Animals” — cites cognition experiments with birds and primates.

“Ideally, we would like to build AI technology that can grasp these interrelated principles and concepts as a systematic whole and that manifests this grasp in a human-level ability to generalize and innovate,” the paper reads. “How to build such AI technology remains an open question. But we advocate an approach wherein RL agents, perhaps with as-yet-undeveloped architectures, acquire what is needed through extended interaction with rich virtual environments.”

When it comes to building systems like those mentioned in the paper, challenges include helping agents sense that they exist within an independent world. Training agents to grasp the concept of common sense is another hurdle, along with identifying

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