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How AI nearly predicted Omicron structure

How AI nearly predicted Omicron structure



AI | JAN 29, 2022 ©GETBES | 0 Views



THE WORLD HEALTH ORGANIZATION identified the coronavirus strain sweeping South Africa as a "variant of concern" on November 26 and dubbed it Omicron. Sriram Subramaniam of the University of British Columbia obtained an online genome sequencing the next day and had samples of Omicron DNA mailed to his lab.


To better understand how proteins operate, Subramaniam's team employs electron microscopes to expose their 3D structure. For some early coronavirus strains, it has previously identified the spike proteins that coronaviruses utilize to bind and penetrate human cells. Because Omicron's spike protein's DNA varied in ways that may explain the variant's fast dissemination, describing it felt crucial. But, like other internet shoppers that weekend, Subramaniam had to be patient: he couldn't look at Omicron proteins until the DNA arrived in the mail. .

Colby Ford, a computational genomics researcher at the University of North Carolina in Charlotte, has been thinking about Omicron's spike protein as well. Relatives had been asking him a question that many specialists were concerned about: would Omicron be able to resist existing vaccines? These vaccinations instruct the body how to react to spike proteins from a previous strain. Ford used a freshly devised shortcut instead of ordering lab materials. Because the DNA of Omicron's spike protein differed in ways that may explain the variant's rapid spread, characterising it felt critical. Subramaniam, like other internet customers that weekend, had to wait for the DNA to come in the mail before looking at Omicron proteins.

Ford received his initial results in about an hour and instantly put them online. He and two colleagues published a longer study in early December that included predictions that some antibodies to prior strains would be less effective against Omicron, which has already been approved for publication.



Subramaniam's lab got their Omicron gene samples shortly after, and on December 21, it published its microscope observations of the structure, as well as results from genuine antibody testing. One of Ford's two projected structures came close to being correct: He determined that the center atoms' locations varied by roughly half an angstrom, or around a hydrogen atom's radius. "These technologies allow you to rapidly make an educated guess—which is critical in a case like Covid," Ford explains. "Any new virus that emerges will be used to mimic what I accomplished here." .

The way predictions on Omicron's spike protein outpaced trials illustrates a recent sea change in molecular biology brought forth by AI. Thanks to rival research teams at Alphabet's UK-based AI lab DeepMind and the University of Washington, the first software capable of reliably predicting protein structures became publicly available only months before Omicron.

Ford employed both programmes, but his results were more suggestive than definite because none was developed or verified for forecasting tiny changes induced by mutations like those of Omicron. Some researchers were suspicious about them. However, the fact that he was able to experiment with powerful protein prediction AI so easily demonstrates how recent breakthroughs are already altering how biologists work and think.

While working on his lab's data, Subramaniam claims he received four or five emails from people giving projected Omicron spike topologies. "A lot of people did it just for fun," he says. Direct measurements of protein structure will remain the gold standard, but AI predictions will become increasingly important in research, especially on future disease outbreaks, according to Subramaniam. He describes it as "transformative."

Knowing a protein's structure may aid various types of biology study, from evolutionary studies to illness research, because a protein's form dictates how it acts. In drug development, determining the structure of a protein can lead to the discovery of novel therapy targets.

It's not easy to figure out what a protein's structure is. They are complex molecules made up of instructions encoded in an organism's DNA that act as enzymes, antibodies, and most of the rest of life's machinery. Proteins are made up of amino acid chains that may fold into complicated forms and function in a variety of ways.

Traditionally, deciphering the structure of a protein required arduous laboratory effort. The majority of the approximately 200,000 known structures were mapped using a difficult method in which proteins are crystallized and blasted with x-rays. Although newer methods, such as the electron microscope utilized by Subramaniam, can make the process go faster, it is still far from simple.

After decades of gradual development, the long-held ambition that computers could predict protein structure from an amino acid sequence became a reality in late 2020. In a competition for protein prediction, DeepMind software dubbed AlphaFold was so precise that the challenge's originator, John Moult, a professor at the University of Maryland, proclaimed the problem solved. DeepMind's breakthrough was "a really memorable moment" for Moult, who had worked on the problem for so long.

For several scientists, the situation was especially frustrating since DeepMind would not immediately provide specifics about how AlphaFold operated. Last year, David Baker, whose group at the University of Washington focuses on protein structure prediction, told WIRED, "You're in this bizarre scenario where there's been this tremendous advance in your area, but you can't build on it." His research group utilized DeepMind's hints to build RoseTTAFold, an open-source program that was similar to but not as strong as AlphaFold and was published in June. Both are based on machine learning algorithms that have been trained on a database of over 100,000 known protein structures to predict protein shapes. The following month, DeepMind revealed the specifics of its own research and made AlphaFold freely available to anybody. Suddenly, there were two approaches to predict protein configurations in the world.

Minkyung Baek, a postdoctoral researcher in Baker's lab who spearheaded the RoseTTAFold project, says she's been shocked by how rapidly protein structure predictions have become commonplace in biology research. According to Google Scholar, UW and DeepMind's papers on their software have been referenced in over 1,200 scholarly articles in the short period after they were published.

Although forecasts haven't proved to be vital in her work on Covid-19, she feels they will become more relevant in the future in terms of illness response. Algorithms won't provide fully formed pandemic-prevention solutions, but projected structures can aid scientists in strategizing. According to Baek, "a projected structure might help you focus your experimental effort on the most essential challenges." She's currently working on improving RoseTTAFold so that it can reliably anticipate the structure of antibodies and invading proteins while they're bonded together, making it more valuable for infectious disease research.

Protein predictors, despite their outstanding accuracy, do not tell everything about a molecule. They only provide a single static structure for a protein, ignoring the bends and wiggles that occur when it interacts with other molecules. The algorithms were trained on databases of known structures, which are more representative of the structures that are simplest to map experimentally than the whole diversity of nature. The algorithms, according to Kresten Lindorff-Larsen, a professor at the University of Copenhagen, will be used more frequently and will be valuable, but "we as a discipline also need to learn better when these approaches fail."

Subramaniam's Omicron publication includes results of a type of AI that has yet to be conquered: a combination structure for a spike attached to the human protein it targets. The findings imply that the variant's structural alterations let it to engage host cells more forcefully while also being less resistant to antibodies from prior strains, which might explain how Omicron can overrun even highly vaccinated populations.

"Direct measurement will always be the gold standard," says Subramaniam. "When you're putting up a multibillion-dollar medication program, people want to know what's true." Simultaneously, he claims that AI forecasts are now often informing his experimental efforts. Subramaniam says, "It's transformed the way we think."

Important observations

The Omicron variant's receptor-binding motif (RBM) comprises nine distinct alterations. The Omicron variation is distantly linked to the initial Wuhan strain of SARS-CoV-2 and its Gamma variant, according to variant-specific sequencing analyses (first identified in Brazil). The Omicron RBD has 15 single amino acid substitution mutations that resulted in residue type changes, according to the mutational study. Amino acid polarity and side-chain length had changed as a result of these alterations. By modifying the surface charges or producing steric hindrance, these alterations are likely to influence the binding affinity between RBD and antibody.


Antigen-antibody interactions

The omicron RBD exhibits a lower binding affinity for each tested antibody than the wild-type RBD, according to the antibody binding analyses. Electrostatic contact was reduced by 4 percent to 52 percent. Furthermore, the data demonstrated a decrease in buried surface area, suggesting that the omicron RBD interacts with antibodies from a greater distance. Importantly, statistical analysis demonstrated that omicron RBD's binding affinity for tested antibodies is not substantially different from wild-type RBD's.

The investigation of omicron RBD/wild-type RBD – antibody complexes revealed alterations in several residues at the binding interface, including 448N, 484A, and 494S. In the Omicron RBD – antibody binding interface, however, several residues remained unaltered.

Study significance

The structure of omicron RBD and its interaction kinetics with anti-SARS-CoV-2 neutralising antibodies are determined using in silico prediction methods. Despite inheriting 60 mutations in the RBD, the omicron version was not able to entirely avoid antibody-mediated neutralisation, according to the findings. The modifications to amino acids with bigger side chains may have resulted in a decrease in binding affinity between omicron RBD and tested antibodies. When compared to wild-type RBD, these changes in the omicron RBD resulted in a significantly more distant interaction with antibodies, resulting in a drop in binding affinity.

Minkyung Baek, says she's been shocked by how rapidly protein structure predictions have become commonplace in biology research. According to Google Scholar, UW and DeepMind's papers on their software have been referenced in over 1,200 scholarly articles in the short period after they were published.

Although forecasts haven't proved to be vital in her work on Covid-19, she feels they will become more relevant in the future in terms of illness response. Algorithms won't provide fully formed pandemic-prevention solutions, but projected structures can aid scientists in strategizing. According to Baek, "a projected structure might help you focus your experimental effort on the most essential challenges." She's currently working on improving RoseTTAFold so that it can reliably anticipate the structure of antibodies and invading proteins while they're bonded together, making it more valuable for infectious disease research.

Protein predictors, despite their outstanding accuracy, do not tell everything about a molecule. They only provide a single static structure for a protein, ignoring the bends and wiggles that occur when it interacts with other molecules. The algorithms were trained on databases of known structures, which are more representative of the structures that are simplest to map experimentally than the whole diversity of nature. The algorithms, according to Kresten Lindorff-Larsen, a professor at the University of Copenhagen, will be used more frequently and will be valuable, but "we as a discipline also need to learn better when these approaches fail."





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