New insights into protein structures could help inform drug development and predict future outbreaks — ScienceDaily

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

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New tool to predict breast cancer recurrences

A new tool combining traditional pathology with machine learning could predict which breast cancer patients actually need surgery. The technology, reported in the November issue of American Journal of Physiology — Cell Physiology (vol. 319: C910-C921; https://doi.org/10.1152/ajpcell.00280.2020), could spare women from unnecessary treatments, reduce medical expenses, and lead to a new generation of drugs to stop breast cancer recurrences.

Ductal carcinoma in situ (DCIS) of the breast, an early form of disease also known as stage 0 breast cancer, is a diagnosis that only sometimes leads to invasive breast cancer. But only some patients need surgery, chemotherapy and/or radiotherapy, and the rest could be sent home. Predicting the outcomes of patients with early forms of cancer has been a major scientific problem for decades.

Professor Howard Petty and Ms. Alexandra Kraft, his research assistant, both of the University of Michigan, have just

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DeepMind’s latest AI breakthrough can accurately predict the way proteins fold

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind’s AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be “competitive” with actual, experimental data.



diagram: AlphaFold 2


AlphaFold 2

“We have been stuck on this one problem – how do proteins fold up – for nearly 50 years,” said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. “To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.”

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Researchers and enthusiasts across

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Scientists design model to predict cellular drug targets against COVID-19 — ScienceDaily

A computational model of a human lung cell has been used to understand how SARS-CoV-2 draws on human host cell metabolism to reproduce by researchers at the University of Warwick. This study helps understand how the virus uses the host to survive, and enable drug predictions for treating the virus to be made.

Viruses rely on their host to survive, a crucial step of lifecycle is the synthesis of the virus particles within the host cell, therefore understanding this process is key to finding ways to prevent the virus from surviving.

Using a computer model of a human lung cell metabolism, scientists from the School of Life Sciences at the University of Warwick have captured the stoichiometric amino and nucleic acid requirements of SARS-CoV-2, the virus that causes Covid-19. Publishing their results in the paper, ‘Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network’, in the

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Flow physics could help forecasters predict extreme events

Flow physics could help forecasters predict extreme events
Brian Elbing (left) holds a microphone with storm chaser Val Castor (right) in front of his storm chasing truck, in which the researchers mounted an infrasound sensor for monitoring tornadoes. Credit: Brian Elbing

About 1,000 tornadoes strike the United States each year, causing billions of dollars in damage and killing about 60 people on average. Tracking data show that they’re becoming increasingly common in the southeast, and less frequent in “Tornado Alley,” which stretches across the Great Plains. Scientists lack a clear understanding of how tornadoes form, but a more urgent challenge is to develop more accurate prediction and warning systems. It requires a fine balance: Without warnings, people can’t shelter, but if they experience too many false alarms, they’ll become inured.


One way to improve tornado prediction tools might be to listen better, according to mechanical engineer Brian Elbing at Oklahoma State University in Stillwater, in the heart of

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Study suggests mechanical properties of spike proteins can predict infectivity and lethality of different coronaviruses — ScienceDaily

When someone struggles to open a lock with a key that doesn’t quite seem to work, sometimes jiggling the key a bit will help. Now, new research from MIT suggests that coronaviruses, including the one that causes Covid-19, may use a similar method to trick cells into letting the viruses inside. The findings could be useful for determining how dangerous different strains or mutations of coronaviruses may be, and might point to a new approach for developing treatments.

Studies of how spike proteins, which give coronaviruses their distinct crown-like appearance, interact with human cells typically involve biochemical mechanisms, but for this study the researchers took a different approach. Using atomistic simulations, they looked at the mechanical aspects of how the spike proteins move, change shape, and vibrate. The results indicate that these vibrational motions could account for a strategy that coronaviruses use, which can trick a locking mechanism on the

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New study could help predict which individuals are more susceptible to cancer-causing agent — ScienceDaily

New insights into the mechanisms behind how cancer-causing agents in the environment activate genetic recombination in DNA could help to explain some of the effects of exposure as well as predicting which individuals may be more susceptible to developing the disease, a new UK study has suggested.

Everyone is exposed to low levels of carcinogens (substances or radiation that promote the formation of cancer) in the environment. One of the most widely found is benzopyrene — a general chemical pollutant found in smoke from stoves such as wood burners, exhaust fumes and barbequed meat and fish. One active ingredient of benzopyrene, BPDE, directly damages the DNA sequence forming what is known as adducts which in turn promote cancer-causing mutations.

While models exist showing how BPDE causes these mutations, some of the pathways are still not understood. It is currently believed that a BPDE adducts cause mutations during DNA synthesis because

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Study reconstructs ancient storms to help predict changes in tropical cyclone hotspot — ScienceDaily

Intense tropical cyclones are expected to become more frequent as climate change increases temperatures in the Pacific Ocean. But not every area will experience storms of the same magnitude. New research from the Woods Hole Oceanographic Institution (WHOI) published in Nature Geoscience reveals that tropical cyclones were actually more frequent in the southern Marshall Islands during the Little Ice Age, when temperatures in the Northern Hemisphere were cooler than they are today.

This means that changes in atmospheric circulation, driven by differential ocean warming, heavily influence the location and intensity of tropical cyclones.

In the first study of its kind so close to the equator, lead author James Bramante reconstructed 3,000 years of storm history on Jaluit Atoll in the southern Marshall Islands. This region is the birthplace of tropical cyclones in the western North Pacific — the world’s most active tropical cyclone zone. Using differences in sediment size as

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Ancient storms could help predict future shifts in tropical cyclone hotspots

Nov. 16 (UPI) — To get a better sense of how climate change might alter the patterns of major ocean storms, shifting the parameters of tropical cyclone hotspots, scientists reconstructed 3,000-years of storm history in the Marshall Islands.

The analysis showed that during the Little Ice Age, storms more frequently struck Jaluit Atoll in the southern Marshall Islands.

The findings, published Monday in the journal Nature Geoscience, suggest differences in ocean warming strongly influence Pacific storm patterns.

By analyzing differences in sediment size, researchers were able to pinpoint the timing of extreme weather events. The data showed that prior to the Little Ice Age, storms hit Jaluit Atoll roughly once per century, but between 1350 and 1700 AD, the islands were struck by four cyclones per century — a significant increase.

By studying the affects of ancient climate change on storms patterns in the Northern Pacific, researchers were able to

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As COVID-19 surges, science writers at the Festival of Books predict a long road ahead

Science & Medicine: Looking at the Coronavirus and Pandemics. Part of the Los Angeles Times Festival of Books, Stories and Ideas 2020. (L-R) L.A. Times health-care reporter Soumya Karlamangla, sociologist, physician, and author Nicholas Christakis, science journalist Debora MacKenzie, andprize-winning author/journalist Sonia Shah.
(Los Angeles Times)

“It will end with a whimper and not a bang,” said Sonia Shah, journalist and author of “Pandemic: Tracking Contagions, From Cholera to Ebola and Beyond.”

Shah was speaking at Friday’s science and medicine panel at the Los Angeles Times Festival of Books — the theme of which, naturally, was COVID-19. The panelists had been asked to make predictions on when we can be rid of a pandemic that has just this week surged to previously unimaginable levels, and Shah’s was the most optimistic scenario they could muster: a series of deescalating surges, mitigated by a slowly disseminated vaccine and perhaps some herd immunity.

Joining Shah on the virtual panel were Debora MacKenzie, author of “COVID-19: The Pandemic That Never Should Have Happened and How to Stop the Next One,” and physician and sociologist Nicholas Christakis, author of “Apollo’s Arrow: The Profound and Enduring Impact of Coronavirus

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