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Business News/ Opinion / Columns/  Google’s AlphaFold has a new tool to track genetic mutations
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Google’s AlphaFold has a new tool to track genetic mutations

Last year’s AI breakthrough is showing what it can do for health

AlphaMissense is meant to be an additional filter when a researcher is confronted with a long list of possible mutations. Premium
AlphaMissense is meant to be an additional filter when a researcher is confronted with a long list of possible mutations.

It’s becoming clear that scientists are only scratching the surface of what artificial intelligence (AI) can teach us about human biology and disease. Case in point: new work by Alphabet’s AI subsidiary, Google DeepMind, that promises to help winnow down the genetic causes of human afflictions.

Recall that it has only been a year since DeepMind researchers rocked the scientific world by unleashing a database with the predicted structure of more than 200 million proteins—like a family album of nearly every protein found in every organism on Earth. Now, it has built on it to develop a tool that can pluck harmful genome mutations from thousands of tiny benign ones. Called AlphaMissense, the tool is not a quantum leap, but it’s a harbinger for other advances that could spill out of AlphaFold and other such efforts.

Last year, Google DeepMind showed that its AlphaFold could predict the structure of any protein from its genetic sequence. It was hailed as one of the most important scientific advances of the year, albeit with some caveats. Researchers have plumbed it to inform drug-discovery efforts, improve vaccine and drug design, and fill in some of the many blanks about the protein universe.

The magnitude of the advance might not have registered with people, DeepMind’s CEO Demis Hassabis told The Verge, but the ability to translate a string of letters into a complex 3D form has had “the most unequivocally biggest beneficial effects so far in AI on the world." He noted that, “Every Big Pharma company is using it to advance their drug discovery programmes." The tool is free.

Now, DeepMind has tweaked AlphaFold to analyse those sequences for an entirely different task: identifying which single-letter changes in the genome might cause harm, and which are likely to be benign. The result is AlphaMissense, the details of which it revealed in a paper this week in the prestigious journal Science.

It’s not the first tool designed to identify relevant mutations from the long list of minor errors in our genomes, and certainly others will come after it. But it’s a welcome improvement over what’s out there—and one that might help solve genetic mysteries and even clear a path to developing therapies for rare diseases.

AlphaMissense tries to overcome another longstanding problem: Although genome sequencing has become far more accessible, researchers often encounter mutations that defy interpretation—amino acids are swapped in a ways never seen before, or a mutation hasn’t been sufficiently studied to determine if it’s pathogenic or not.

Families looking for answers to an abnormality are told, “we did your genetic testing and found a ‘variant of uncertain significance’," says Timothy Yu, a neurologist at Boston Children’s Hospital. While Yu is sceptical that AlphaMissense could get rid of all uncertainty, “it should help," he says.

DeepMind scientists stressed on a call with media that AlphaMissense isn’t offering an alternative to a genetic diagnosis. Rather, the tool is meant to be an additional filter when a researcher is confronted with a long list of possible mutations, allowing doctors like Yu to get an answer faster. Early intervention is critical to making a difference in patients’ lives, particularly in an era where other technologies make it increasingly possible to quickly design a bespoke drug to address a problematic mutation.

AlphaMissense is not the only recent AlphaFold advance worth watching. Another notable, less splashy development came earlier this month.

Two separate papers, published simultaneously in Nature, offered a way to organize AlphaFold’s 200 million protein structures into something akin to a family tree. By connecting even the most distant protein relatives, that effort that could help scientists glean broader insights about human biology.

For example, by studying the structure of similar proteins, Martin Steinegger, a computational biologist at Seoul National University, was able to draw links between bacteria and proteins related to human immunity, while another team separately identified never-before-seen protein shapes. The hope is their efforts could help scientists understand the vast percentage of proteins whose function remains a mystery.

There’s been plenty of worrying about the damage that AI could do, but modern discoveries like these show how AI can accelerate progress on the most vexing questions about human biology and health—and motivate scientists to keep pushing to see what these machines can help us do better next.

Lisa Jarvis is a Bloomberg Opinion columnist covering biotech, health care and the pharmaceutical industry.

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Updated: 22 Sep 2023, 12:58 AM IST
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