The way predictions took place before experiments on Omicron’s spike proteins reflect a recent sea change in molecular biology brought about by AI. The first software capable of accurately predicting protein structures became widely available just months before Omicron appeared, thanks to competing research teams at Alphabet’s UK-based AI lab DeepMind and the University of Washington.
Ford used both packages, but because neither was designed or validated for predicting small changes caused by mutations such as Omicron, his results were more suggestive than definitive. Some researchers looked at him with suspicion. But the fact that he can easily experiment with powerful protein prediction AI shows that recent breakthroughs are already changing the way biologists work and think.
Subramaniam says he received four or five emails from people predicting omicron spike structures while working toward his lab’s results. “Quite a few did it just for fun,” he says. Direct measurement of protein structure will remain the final criterion, says Subramaniam, but he expects AI predictions to become increasingly central to research—including future disease outbreaks. “It’s transformative,” he says.
Because the shape of a protein determines how it behaves, knowing its structure can help with all kinds of biology research, from the study of evolution to work on disease. In drug research, protein structure detection can help reveal potential targets for new therapies.
Determining the structure of proteins is far from simple. They are complex molecules that are assembled from instructions encoded in an organism’s genome to serve as enzymes, antibodies, and other machinery of life. Proteins are made up of strings of molecules called amino acids that can fold into complex shapes that behave in different ways.
Understanding the structure of proteins has traditionally involved painstaking laboratory work. Most of the nearly 200,000 known structures were mapped using a tricky process in which proteins form in a crystal and are bombarded with X-rays. Newer techniques, such as electron microscopy, used by Subramaniam may be faster, but the process is still not easy.
In late 2020, the long-held hope that computers could predict protein structure from amino acid sequence suddenly became real after decades of slow progress. DeepMind software called AlphaFold proved so accurate in a competition for protein prediction that the challenge’s cofounder, John Moult, a professor at the University of Maryland, announced a solution to the problem. “After working personally on this problem for so long,” Moult said, DeepMind’s achievement was “a very special moment.”
The moment was also disappointing for some scientists: DeepMind did not immediately release details of how AlphaFold works. David Baker, whose lab at the University of Washington works on protein structure prediction, told Nerdshala last year, “You’re in this strange situation, where you’ve got this great advance in the field, but you can’t build on it.” ” His research group used clues left by DeepMind to guide the design of open source software called RoseTyFold, released in June, which was similar but not as powerful as AlphaFold. Both are based on machine learning algorithms, which are honed to predict protein structures by training them on a collection of over 100,000 known structures. Next month, DeepMind published details Released AlphaFold for my own work and for anyone to use. Suddenly, the world had two ways of predicting protein structures.
Minkyung Bak, a postdoctoral researcher in Baker’s lab who led the work on the Rose TTAfold, says he is surprised at how quickly protein structure prediction has become the standard in biology research. Google Scholar reports that UW and DeepMind’s papers on their software together have been cited by more than 1,200 academic articles in the short time since they were published.
Although the predictions have not proven to be important for working on COVID-19, he believes they will become increasingly important for future disease response. Anti-epidemic answers aren’t entirely made up of algorithms, but inferred structures can help scientists strategize. “A predictable structure can help you channel your experimental effort into the most important problems,” Beck says. She is now trying to get RoseTTafold to accurately predict the structure of antibodies and invading proteins when bound together, which will make the software more useful for infectious disease projects.
Despite their impressive performance, protein predictors do not reveal everything about a molecule. They sputter a stable structure to a protein, and do not hold up to flakes and wiggles when it interacts with other molecules. The algorithms were trained on a database of known structures, which are the easiest to map experimentally, rather than the full variety of nature. Cresten Lindorf-Larsen, a professor at the University of Copenhagen, predicts that the algorithm will be used more frequently and will be useful, but says, “We as a field also need to learn better when these methods fail. “
In addition to the spike protein structure, Subramaniam’s Omicron paper also includes results of a type that has yet to be conquered – a composite structure for a spike targeting human proteins. The results suggested that the structural changes of the variant allow it to bind more strongly to host cells, while being less sensitive to antibodies from previous strains, a combination that may explain why Omicron outnumbers even highly vaccinated communities. could.
Subramaniam says, “The gold standard will always be a direct measure. “If you’re building a billion dollar drug program, people want to know what the real thing is.” At the same time, he says that his experimental work is now often informed by AI predictions. “It has changed the way we think,” says Subramaniam.
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