English
Swedish

Presentation speech by Professor Johan Åqvist, Member of the Royal Swedish Academy of Sciences, Member of the Nobel Committee for Chemistry, 10 December 2024.

Your Majesties, Your Royal Highnesses, Esteemed Nobel Prize Laureates, Ladies and Gentlemen,

Proteins are the macromolecules that enable the chemistry of life. They catalyse all the needed chemical reactions. They also function as signalling molecules, receptors, pumps, antibodies and building blocks of different tissues. Their diversity is truly remarkable, and to understand how proteins work you need to know what they look like.

Proteins are polymers made up from an alphabet of 20 amino acids that can be combined in endless ways.

A typical protein contains maybe a few hundred amino acids and thousands of atoms. Their magic derives from the fact that these strings of amino acids fold themselves up into well-defined and complex three- dimensional shapes.

Even more magically, the 3D structures are somehow encoded in the sequence of amino acids. A complex 3D pattern is thus encoded in a linear sequence! Hence, it should be possible to predict the 3D structure of a given protein directly from its amino acid sequence. This 50-year-old problem has been called the Grand Challenge of Biochemistry. However, progress in protein structure prediction was slow for many years.

But one can also turn the problem around and ask – given a certain 3D structure, what amino acid sequences would fold up into this structure? This would enable the design of completely new proteins, and David Baker showed in 2003 that it was possible. He could design new structures that had never been seen in Nature before and compute what sequences would give these structures. He could then experimentally verify that the computational predictions were correct.

This is what we now call computational protein design and, following the first breakthrough 20 years ago, David Baker has designed a multitude of new proteins with many exciting applications: virus inhibitors, nanomaterials, protein switches and sensors, just to mention a few.

The reverse problem of predicting the 3D structure from amino acid sequence had to wait until 2020 for the real breakthrough. This was when Demis Hassabis and John Jumper and their co-workers had developed the now famous AlphaFold2 computer programme. It basically cracked the code.

AlphaFold is an ingenious piece of neural network engineering that showed unsurpassed performance in protein structure prediction. Utilising the vast amounts of experimental data stored in sequence and structure databases, the network is trained to discover correlations and patterns among amino acid sequences. This enables it to produce astonishingly accurate structural models directly from the sequences.

It is no exaggeration to say that AlphaFold has caused a revolution in structural biochemistry.

David Baker, Demis Hassabis and John Jumper,
Your ground-breaking work in computational protein design and protein structure prediction has revolutionised these fields. It has opened up completely new possibilities to design proteins that have never been seen before, and we now have access to predicted structures of all 200 million known proteins. These are truly great achievements.

On behalf of the Royal Swedish Academy of Sciences, I wish to convey to you our warmest congratulations. May I now ask you to step forward and receive your Nobel Prizes from the hands of His Majesty the King.

Copyright © The Nobel Foundation 2024