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Protein designers and modelers share 2024 chemistry Nobel

Protein designers and modelers share 2024 chemistry Nobel

9 October 2024

The prizewinning research enhances understanding of nature’s molecular toolkit and enables the construction of novel proteins.

David Baker, Demis Hassabis, and John Jumper.
From left: David Baker, Demis Hassabis, and John Jumper. Credits: Ian C. Haydon/UW Medicine (Baker); Google DeepMind (Hassabis and Jumper)

Updated at 4:00pm EDT

David Baker, Demis Hassabis, and John Jumper are the recipients of the 2024 Nobel Prize in Chemistry for their work on protein design and structure, the Royal Swedish Academy of Sciences announced on Wednesday. Baker, of the University of Washington in Seattle, receives half the prize “for computational protein design.” Hassabis and Jumper, at Google DeepMind in London, share the other half for predicting protein structures with neural network–based AI. This year’s Nobel Prize in Physics also honored research in neural networks.

In the 1990s, Baker and his collaborators developed the Rosetta computer program to design amino acid sequences that would fold into desired structures. The program takes structural-fragment data from various existing proteins and uses an energy optimization procedure to assemble them together into a new form.

When Rosetta was developed, computational resources were meager enough that Baker founded a project called Rosetta@home so that people could donate computational power from their personal computers to help complete Rosetta’s calculations. Before the work by Baker’s team, protein engineering had relied mostly on the modification of naturally occurring proteins—the topic of the 2018 chemistry Nobel. The de novo design capabilities ushered in by Rosetta have allowed biochemists to build proteins from scratch to perform new functions, such as logic operations inside and outside cells.

Structure of the vitellogenin protein in the honeybee.
AlphaFold’s predicted structure of the vitellogenin protein in the honeybee. The colors represent regions of the protein predicted with high (blue) through low (yellow and orange) confidence. Credit: Courtesy of the AlphaFold Protein Structure Database/CC BY 4.0

If a specific sequence of amino acids is known, biochemists reasoned that it could be possible to predict the complicated 3D structure that the sequence folds into. Since 1994, protein researchers have held a biennial challenge—the Critical Assessment of Structure Prediction (CASP) experiment—to test theoretical structure predictions against experimental observations. AI models are adept at pattern recognition and have been steadily improving the prediction accuracy for years. As recounted in Physics Today’s 2021 report on DeepMind’s transformative work, designed by Hassabis, Jumper, and colleagues:

Structure predictions are graded on a scale from 0 to 100: A random guess might score below 20; an atomically precise structure, above 90.

From the early days of CASP, models have been scoring above 80 for the easiest template-based predictions, while scores for the most difficult targets have been stuck around 40. So DeepMind’s first CASP entry, the original AlphaFold model in 2018, shook things up by scoring above 70 for more than half of the most difficult targets. …

For the 2020 CASP assessment, the DeepMind team had revamped its model into AlphaFold2, whose predictions scored near 90 even for the most difficult targets—scores so high that they were probably limited by the imprecision of the experimental structures the predictions were graded against.

Earlier this year, Hassabis, Jumper, and colleagues reported the development of AlphaFold3, an upgraded model that also predicts the structures of nucleic acids, small molecules, and other biochemical complexes. The accurate design of proteins and the prediction of their structure have far-reaching implications in medicine and many other fields.

At the Nobel press conference, Baker offered an example: an inexpensive nasal spray that he and colleagues are working on to protect against multiple coronavirus variants. “I’m really excited,” he said, “about all the ways in which protein design can make the world a better place.”

Selected articles in Physics Today

PT’s Nobel Prize coverage

  • John Hopfield and Geoffrey Hinton shared the 2024 physics prize “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
  • Moungi Bawendi, Louis Brus, and Aleksey Yekimov shared the 2023 chemistry prize for the development of quantum dots.
  • And more in PT’s Nobel Prize collection
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