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A team of scientists has produced first open source all-atom models of full-length COVID-19 Spike protein that facilitates viral entry into host cells – a discovery that can facilitate a faster vaccine and antiviral drug development.

The group from Seoul National University in South Korea, University of Cambridge in the UK and Lehigh University in the US produced the first open-source all-atom models of a full-length S protein.

The researchers say this is of particular importance because the S protein plays a central role in viral entry into cells, making it a main target for vaccine and antiviral drug development.

"Our models are the first full-length SARS-CoV-2 spike (S) protein models that are available to other scientists," said Wonpil Im, a professor in Lehigh University.

"Our team spent days and nights to build these models very carefully from the known cryo-EM structure portions. Modeling was very challenging because there were many regions where simple modeling failed to provide high-quality models," he wrote in a paper published in The Journal of Physical Chemistry B.

Scientists can use the models to conduct innovative and novel simulation research for the prevention and treatment of Covid-19.

Though the coronavirus uses many different proteins to replicate and invade cells, the Spike protein is the major surface protein that it uses to bind to a receptor.

The total number of global COVID-19 cases was nearing 9 million, while the deaths have increased to over 467,000, according to the Johns Hopkins University.

With 2,279,306 cases and 119,967 deaths, the US continues with the world's highest number of COVID-19 infections and fatalities, according to the CSSE.

Brazil comes in the second place with 1,083,341 infections and 50,591 deaths.

Taking multiple courses of antibiotics within a short span of time may do people more harm than good, suggests new research which discovered an association between the number of prescriptions for antibiotics and a higher risk of hospital admissions.

Patients who have had 9 or more antibiotic prescriptions for common infections in the previous three years are 2.26 times more likely to go to hospital with another infection in three or more months, said the researchers.

Patients who had two antibiotic prescriptions were 1.23 times more likely, patients who had three to four prescriptions 1.33 times more likely and patients who had five to eight 1.77 times more likely to go to hospital with another infection.

"We don't know why this is, but overuse of antibiotics might kill the good bacteria in the gut (microbiota) and make us more susceptible to infections, for example," said Professor Tjeerd van Staa from the University of Manchester in Britain.

The study, published in the journal BMC Medicine, is based on the data of two million patients in England and Wales.

The patient records, from 2000 to 2016, covered common infections such as upper respiratory tract, urinary tract, ear and chest infections and excluded long term conditions such as cystic fibrosis and chronic lung disease.

The risks of going to hospital with another infection were related to the number of the antibiotic prescriptions in the previous three years.

A course is defined by the team as being given over a period of one or two weeks.

"GPs (general physicians) care about their patients, and over recent years have worked hard to reduce the prescribing of antibiotics,""Staa said.

"But it is clear GPs do not have the tools to prescribe antibiotics effectively for common infections, especially when patients already have previously used antibiotics.

"They may prescribe numerous courses of antibiotics over several years, which according to our study increases the risk of a more serious infection. That in turn, we show, is linked to hospital admissions," Staa added.

It not clear why hospital admissions are linked to higher prescriptions and research is needed to show what or if any biological factors exist, said the research team.

"Our hope is that, however, a tool we are working for GPs, based on patient history, will be able to calculate the risks associated with taking multiple courses of antibiotics," said Francine Jury from the University of Manchester.