The 2024 Nobel prize in chemistry has been awarded to David Baker, Demis Hassabis and John Jumper for their work on understanding the structure of proteins, which play vital roles in all living organisms. Hassabis and Jumper, of Google DeepMind, developed an artificial intelligence that predicts the structure of proteins. Baker, at the University of Washington in Seattle, has been recognised for his work on designing new proteins.
Proteins are the molecules that make life happen. All of the key machinery of life is made of proteins, from the muscles that power us and the molecules that read and copy DNA to the antibodies that protect us from infections.
“To understand life, you first need to understand the shape of proteins,” said Heiner Linke, chair of the Nobel committee for chemistry, at a press conference.
All proteins are made of chains of amino acids, and there are around 20 different kinds of these compounds. The shape of proteins is determined by the sequence of amino acids, but the way in which the chains fold up is so complex that predicting a protein’s structure from its sequence is extremely challenging.
“For several decades, this was considered impossible,” said Linke.
Several teams have developed various computational methods of predicting protein structures, but their accuracy was low. Then Hassabis and Jumper developed an AI called AlphaFold.
The first version of AlphaFold, unveiled in 2018, was an improvement on other methods. The second, released in 2020, was a massive leap forward, predicting two-thirds of protein structures with more than 90 per cent accuracy.
By 2022, AlphaFold had been used to predict the structure of almost all known proteins, with the results made freely available.
“It was an enormous breakthrough,” said Johan Åqvist, a member of the Nobel committee for chemistry. “This is a fantastic resource for chemical and biological research.”
Baker has long been working on the opposite problem, that of designing a protein with a desired structure. The possibilities here are endless – new proteins could be used to do pretty much anything, from treating diseases to creating complex nanomachines.
“David Baker opened up a completely new world of proteins that we had never seen before,” said Åqvist. “It’s a mind-blowing development.”
Baker has created software called Rosetta for doing this, which is also freely available. He and his team first demonstrated that Rosetta worked back in 2003, when they designed a protein, made it and then used a technique called X-ray crystallography to show it had the designed structure.
While Åqvist described this 2003 work as “the big breakthrough”, the protein created was small, simple and didn’t do anything.
Baker himself described the process as more gradual. “It really happened over many years,” he said. “Over the last 20 years, we’ve been able to design proteins with more and more complex and powerful functions.”
“As we got better and better at that, the scope of applications became more and more exciting,” said Baker. “It’s been this huge opening up of possibilities, because the proteins in nature do so many different things. They mediate all the processes in our bodies and in all living things.”
Baker also gave credit to his colleagues: “I stood on the shoulders of giants. I have had, throughout my career, absolutely wonderful colleagues to work with.”
The award came as a surprise, despite speculation that he might get it, he said. “It’s turning out to be a unique, special day.”
The chemistry prize is the third Nobel awarded so far this year. On 8 October, the 2024 Nobel prize in physics was awarded to John Hopfield and Geoffrey Hinton for their work on artificial neural networks. On 7 October, the 2024 Nobel prize in physiology or medicine went to Victor Ambros and Gary Ruvkun for their discovery that tiny pieces of RNA called microRNAs play a key role in controlling genes.
Last year’s Nobel prize in chemistry went to three of the developers of quantum dots – particles so small that their electrical and optical properties are influenced by quantum physics.
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