The 2024 Nobel Prize in Chemistry is dedicated to proteins. The first winner, David Baker, achieved the almost impossible feat of creating entirely new types of proteins. The two other winners, Demis Hassabis and John Jumper, were rewarded for having developed an AI model to solve a 50-year-old problem: predicting the complex structures of proteins. These discoveries hold enormous potential.
New winners
After medicine and physics, it’s time for chemistry. Three years ago, the committee awarded the Nobel Prize to two researchers for their work which enabled the development of a new precise tool for molecular construction: organocatalysis. Two years ago, the Nobel Prize was awarded jointly to Carolyn Bertozzi, K. Barry Sharpless and Morten Meldal for the development of “click chemistry”. Finally last year, the precious sesame rewarded the discovery and development of quantum dots.
For this year 2024, the Nobel Prize in Chemistry was jointly awarded to David Baker for computational protein design, as well as to David Hassabis and John M. Jumper for protein structure prediction. Here are the different points to remember.
De novo design and the work of David Baker
In 2003, David Baker took a revolutionary step in molecular biology by achieving create entirely new proteins. This breakthrough is part of a discipline called conception again where proteins are no longer limited to existing sequences observed in nature. Instead, researchers can design proteins from scratch by imagining and generating novel structures with specific functions.
The key to this breakthrough lies in software, called Rosetta, which was developed by Baker’s team. This program uses algorithms to predict how amino acids fold into a three-dimensional structure, which largely determines a protein’s function. By combining knowledge from biophysics and computational advances, Rosetta makes it possible not only to predict protein structures, but also to design proteins with precise and novel functions.
This ability to generate tailor-made proteins has huge implications in various scientific and industrial fields. In medicine, de novo design can, for example, be used to create enzymes capable of degrading toxic molecules, developing new drugs or improving immune systems. Another example in the field of nanotechnologywhere these personalized proteins can serve as materials to make complex nanostructures.
AlphaFold2: A revolution through artificial intelligence
In 2020, Demis Hassabis et John Jumperresearchers at Google DeepMind, have for their part succeeded in solving one of the biggest challenges in molecular biology with artificial intelligence: predicting the structure of proteins from their amino acid sequences. For decades, scientists have tried to decipher this enigma, known as protein folding problemwhich had remained unsolved for over 50 years.
The model developed by DeepMind, called AlphaFold2has enabled unprecedented progress. Unlike traditional laborious methods of X-ray crystallography or cryogenic electron microscopy, AlphaFold2 uses advanced machine learning to predict with exceptional precision the three-dimensional structure of a protein, simply from the sequence of its amino acids.
The impact of this model has been phenomenal. In just a few years, AlphaFold2 has made it possible to predict the structure of almost any protein identified by scientists, i.e. approximately 200 million structures. This colossal database has transformed research into molecular biology and in biochemistrygreatly facilitating the understanding of the underlying mechanisms of many diseases and accelerating the search for new treatments.
In short, this work has opened up immense perspectives for science and technology by not only making it possible to visualize complex protein structures in a few minutes, but also by offering the opportunity to create new ones to solve medical and environmental. The combination of Baker, Hassabis and Jumper’s approaches illustrates the incredible potential of proteins as chemical tools of life, which can be controlled, designed and predicted on an unprecedented scale.
