Scientists work out how to predict future COVID variants which could evade vaccines

·Contributor
·3 min read
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Could a new variant evade all current vaccines? (Getty)

Scientists may be able to use artificial intelligence (AI) to predict new COVID variants – and work out whether vaccines will be effective against them.

The researchers say that the new AI technique could answer questions such as how protected vaccinated people will be against a new variant and whether antibody therapies will still work.

The answers could be delivered in near real-time, the researchers, led by Professor Sai Reddy from the Department of Biosystems Science and Engineering at ETH Zurich in Basel.

The researchers also believe that being able to identify new variants ‘in advance’ could lead to new ways to develop new drugs or vaccines.

The technology could also be used to develop new treatments for viruses such as flu, the researchers believe.

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"Of course, no-one knows which variant of SARS-CoV-2 will emerge next," Prof Reddy says. "But what we can do is identify key mutations that may be present in future variants, and then work to develop vaccines in advance that provide a broader range of protection against these potential future variants."

"Machine learning could support antibody drug development by enabling researchers to identify which antibodies have the potential to be most effective against current and future variants.”

Since viruses mutate randomly, no-one can know exactly how SARS-CoV-2 will evolve in the coming months and years and which variants will dominate in the future.

The new method takes a comprehensive approach: for each potential variant, it predicts whether or not it is capable of infecting human cells and if it will be neutralised by antibodies produced by the immune system found in vaccinated and recovered persons.

The researchers believe that hidden among all these potential variants is the one that will dominate the next stage of the COVID-19 pandemic.

Prof Reddy and his team used laboratory experiments to generate a large collection of mutated variants of the SARS-CoV-2 spike protein.

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The scientists did not produce or work with live virus, rather they produced only a part of the spike protein, and therefore there was no danger of a laboratory leak.

The spike protein interacts with the ACE2 protein on human cells for infection, and antibodies from vaccination, infection or antibody therapy work by blocking this mechanism.

Many of the mutations in SARS-CoV-2 variants occur in this region, which allows the virus to evade the immune system and continue to spread.

The collection of mutated variants the researchers have analysed comprises only a small fraction of the several billion theoretically possible variants—which would be impossible to test in a laboratory setting—it does contain a million such variants.

By performing high-throughput experiments and sequencing the DNA from these million variants, the researchers determined how successfully these variants interact with the ACE2 protein and with existing antibody therapies.

This indicates how well the individual potential variants could infect human cells and how well they could escape from antibodies.

The researchers used the collected data to train machine learning models, which are able to identify complex patterns and when given only the DNA sequence of a new variant could accurately predict whether it can bind to ACE2 for infection and escape from neutralising antibodies.

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The final machine learning models can now be used to make these predictions for tens of billions of theoretically possible variants with single and combinatorial mutations and going far beyond the million that were tested in the laboratory.

The new method will help develop the next generation of antibody therapies. Several of such antibody drugs were developed to treat the original SARS-CoV-2 virus and approved for use in the United States and Europe.

The researchers are already working with biotechnology companies that are developing next generation COVID-19 antibody therapies.

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