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Proteins are the building blocks of life, so being able to predict their shape and design them with a specific shape and function has huge ramifications for our understanding of medicine and disease. Yesterday’s Nobel Prize for Chemistry recognised Demis Hassabis and John Jumper for their work on AlphaFold, Google DeepMind’s breakthrough AI system that stunned the scientific community in 2020 by accurately predicting protein structures. Google DeepMind worked with European Bioinformatics Institute | EMBL-EBI to provide free and open access to predicted structures for the full human proteome - the set of all proteins in the human body - and 20 model organisms in the AlphaFold Protein Structure Database (AFDB). AFDB now includes over 200 million predictions for nearly every protein known to science and is used by over 1.6 million researchers across 190 countries. It is an essential tool for biological research, accelerating our understanding of disease, drug development, tackling antimicrobial resistance, designing climate change resilient crops, and plastic-eating enzymes to tackle pollution. AlphaFold wouldn’t exist without the work of scientists - underpinned by long-term research funding, infrastructure investment and data-sharing policies - who spent decades uncovering hundreds of thousands of protein structures and sharing them in public archives, like those at EMBL-EBI. Charlotte Deane, executive chair of EPSRC, said: "These AI algorithms are already fundamentally changing the way we discover and design new medicines. “It is an exciting time to be working in science, particularly in these interdisciplinary areas, as AI not only starts solving really hard problems but is also changing the way we do science.” Our congratulations to Demis Hassabis, John Jumper and David Baker on their Nobel Prize.
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 Nobel Prize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.” The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential. The diversity of life testifies to proteins’ amazing capacity as chemical tools. They control and drive all the chemical reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues. Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors. The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough. In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind. Learn more Press release: https://bit.ly/3TM8oVs Popular information: https://bit.ly/3XYHZGp Advanced information: https://bit.ly/4ewMBta