Researchers at ETH Zurich have developed a method to explore the possibilities of how the pandemic virus could evolve. Thanks to their work, it may be possible to develop antibody therapies and vaccines that are more likely to be effective also against future viral variants.
The authors argue that AI may help us identify the ways the Covid virtus may evolve. While this may well be true, the pro-AI arguments about AI benefits may well obscure darker narratives. This is reminiscent of the early argument that the internet would be entirely beneficial and set us free.Michael Lerner
SARS-CoV-2 is constantly mutating and each new variant often catches the world by surprise. Take for example the highly mutated Omicron variant that emerged last November and required health authorities to develop a rapid response strategy even though, initially there were no answers to important questions: How protected are vaccinated and previously infected people against the new variant? And are antibody therapies still effective against this new version of the virus?
Researchers led by Professor Sai Reddy from the Department of Biosystems Science and Engineering at ETH Zurich in Basel have now developed a way of using artificial intelligence to answer such questions, potentially even in real-time immediately after a new variant emerges.
Exploring the multitude of potential 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. In theory, there is virtually no limit to the ways in which a virus could mutate. And this is the case even when considering a small region of the virus: the SARS-CoV-2 spike protein, which is important for infection and detection by the immune system. In this region alone there are tens of billions of theoretical possible mutations.
That’s why the new method takes a comprehensive approach: for each variant in this multitude of potential viral variants, it predicts whether or not it is capable of infecting human cells and if it will be neutralized by antibodies produced by the immune system found in vaccinated and recovered persons. It is highly likely that hidden among all these potential variants is the one that will dominate the next stage of the COVID-19 pandemic.
Synthetic evolution and machine learning
To establish their method, Reddy and his team used laboratory experiments to generate a large collection of mutated variants of the SARS-CoV-2 spike protein. 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.
Although 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. These carry different mutations or combinations of mutations.
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.