Baker (Seattle, Washington, United States, 1962) “has succeeded with the almost impossible feat of building entirely new kinds of proteins,” while Hassabis (London, United Kingdom, 1976) and Jumper (Little Rock, Arkansas, United States, 1985) “have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures,” said the Nobel Committee in its citation These discoveries, it added,  “hold enormous potential.”

In the words of Heiner Linke, Chair of the Nobel Committee for Chemistry: “One of the discoveries being recognized this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities.”

In 2023, Baker, Hassabis and Jumper received the BBVA Foundation Frontiers of Knowledge Award in Biology and Biomedicine “for their contributions to the use of artificial intelligence to accurately predict the three-dimensional structure of proteins,” said the committee at the time. “Scientists – it added – are now using these methods to predict protein conformations, understand protein complexes, design entirely new proteins, and identify novel drug targets.”

“De novo” proteins to create medicines, materials and sensors

The DNA of our cells contains all the instructions we need to develop, survive and reproduce. But proteins are the workhorses that keep all these functions going, and proteins, in turn, are composed of the “biological building blocks” known as amino acids. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other. Since then, his research group has produced a multitude of novel proteins, some of which can be used as pharmaceuticals, vaccines, nanomaterials and sensors.

“New proteins can be improved medicines, so there are many new and exciting medical applications, for example, creating new vaccines or new cancer treating medications,” explained Baker in an interview shortly after hearing of the Frontiers of Knowledge Award.

The key to understanding how a protein will act lies in the arrangement in space it adopts through folding, but deciphering this in the lab is a slow, scattergun process. And predicting its function from its chemical composition is likewise a complex and uncertain task. In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2 that enabled them to predict the structure of virtually all known proteins, some 200 million of them. Among its myriad applications, researchers can now better understand antibiotic resistance and design enzymes that can decompose plastic.

“I believe AlphaFold represents really the first powerful example of how deep learning is able to capture the complexity of biological systems and really develop mathematical understandings of extraordinarily complex things,” declared Jumper in an interview granted after learning of the Frontiers of Knowledge Award. For Hassabis, “AlphaFold has already made a huge impact on biological research in quite a short space of time. We know that over a million researchers have used the structures predicted by AlphaFold in their research, and pretty much every pharma company in the world has been using AlphaFold in their drug discovery programs.”

Parallel tools for deciphering and designing proteins

David Baker is the Henrietta and Aubrey Davis Endowed Professor in Biochemistry, Director of the Institute for Protein Design, and a Howard Hughes Medical Institute investigator at the University of Washington, where is also an adjunct professor of genome sciences, bioengineering, chemical engineering, computer science, and physics. In the late 1990s, he began exploring ways to deduce the structure of proteins guided by the principles of physics, and wrote his findings into an algorithm known by the name Rosetta. This new tool performed fairly well with small proteins, but demanded large computational resources and expert knowledge to get it working properly.

In parallel, Demis Hassabis and John Jumper (co-founder and CEO and Senior Staff Research Scientist respectively at the company DeepMind) decided to use artificial intelligence to solve the problem in a quicker, more accessible way. Jumper led a team using available deep-learning tools and vast quantities of data on the sequences and structures of known proteins, and set to work training the neural network. A few years later, they launched AlphaFold2.

When announcing AlphaFold2, Jumper had outlined some of its underlying concepts, and Baker was quick to take note. He and his team put their existing ideas together with the new insights from DeepMind to come up with another tool they called RoseTTAFold. Its level of accuracy was comparable to that of AlphaFold2, plus it came with an added functionality. Not only could it reliably predict a protein’s structure from its amino acid sequence, it could also run the process in reverse, determining the corresponding amino acid sequence from a protein of a given shape.

30 Frontiers awardees have gone on to win the Nobel Prize

The award of the Nobel in Chemistry to David Baker, Demis Hassabis and John Jumper makes a total of 30 Frontiers of Knowledge laureates that have later won the Nobel Prize.

Eleven Frontiers awardees have gone on to receive the Nobel Prize in Economics: Lars Peter Hansen (2013), Jean Tirole (2014), Angus Deaton (2015),  William Nordhaus (2018), Abhijit Banerjee and Esther Duflo (2019), Paul Milgrom and Robert Wilson (2020),  David Card (2021), Ben Bernanke (2022) and Claudia Goldin (2023).

In the case of the Nobel Prize in Medicine, six Frontiers laureates were subsequently distinguished by the Swedish Academy:  Shinya Yamanaka (2011), James P. Allison (2018), David Julius and Ardem Patapoutian (2021) and Katalin Karikó and Drew Weissman (2023).

The Nobel Prize in Physics has found its way to seven previous Frontiers awardees: Didier Queloz and Michel G. E. Mayor (2019), Klaus Hasselman and Syukuru Manabe (2021) Ferenc Krausz and Anne L’Huillier (2023), and Geoffrey Hinton (2024).

Finally, in the case of the Chemistry Nobel, the Swedish Academy has recognized the work of six Frontiers awardees: Robert J. Lefkowitz in 2012, Emmanuelle Charpentier and Jennifer Doudna in 2020, and David Baker, Demis Hassabis and John Jumper in 2024.