How AI Solved Protein Folding and Won a Nobel Prize
This is the inside story of how AI cracked the protein folding code. In 2024, David Baker, Demis Hassabis and John Jumper won the Nobel Prize in Chemistry for these advances in computer-assisted protein design and structure prediction.
Proteins are biological nano-machines that perform a vast array of vital functions inside of every living organism. For more than half a century, scientists have looked to solve the central mystery of protein science: How does a one-dimensional string of molecules fold innately and near-instantaneously into a complex three-dimensional shape? In 2020, Google DeepMind entered a deep-learning algorithm called AlphaFold2 into the Olympics of protein folding — and to everyone’s shock and surprise, ended up solving a key part of the puzzle. This breakthrough kick-started an AI revolution in biology research, clearing the path to revolutionary new techniques in protein design, which is the process of creating new and novel proteins that could solve some of the world's biggest problems.
Read the full article at: https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/
Related Papers:
- "Highly accurate protein structure prediction with AlphaFold" https://www.nature.com/articles/s41586-021-03819-2
- "De novo design of protein structure and function with RFdiffusion" https://www.nature.com/articles/s41586-023-06415-8
- "Generalized biomolecular modeling and design with RoseTTAFold All-Atom" https://www.science.org/doi/10.1126/science.adl2528
- "Accurate structure prediction of biomolecular interactions with AlphaFold 3" https://www.nature.com/articles/s41586-024-07487-w
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Chapters:
00:00 - Introduction
01:03 - What is a protein?
02:31 - Levinthal Paradox
02:53 - The Protein Folding Problem - how proteins fold to function
03:48 - John Kendrew / using X-ray crystallography to determine structure
05:02 - The Protein Data Bank (PDB)
05:45 - Christian Anfinsen's Nobel winning research
06:28 - Chemical structure of amino acids
07:17 - Secondary and tertiary folding structures
07:59 - Quaternary folding structure
08:16 - The beginnings of computational biology
09:09 - Critical Assessment of protein Structure Prediction (CASP) challenge
10:26 - Baker lab develops RoseTTA
11:31 - Google DeepMind introduces deep learning with AlphaGo
12:00 - DeepMind develops AlphaFold 1 to enter CASP 13
13:32 - AlphaFold 2 explained
15:28 - DeepMind wins CASP 14 and solves the protein folding problem
17:10 - An AI revolution in biological research
17:45 - How the Baker lab designs new proteins
19:53 - New AI tools predict cellular interactions, AlphaFold 3 and RoseTTAFold All-Atom
21:23 - David Baker, John Jumper, and Demis Hassabis win the Nobel Prize
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Quanta Magazine is an editorially independent publication supported by the Simons Foundation: https://www.simonsfoundation.org
Proteins are biological nano-machines that perform a vast array of vital functions inside of every living organism. For more than half a century, scientists have looked to solve the central mystery of protein science: How does a one-dimensional string of molecules fold innately and near-instantaneously into a complex three-dimensional shape? In 2020, Google DeepMind entered a deep-learning algorithm called AlphaFold2 into the Olympics of protein folding — and to everyone’s shock and surprise, ended up solving a key part of the puzzle. This breakthrough kick-started an AI revolution in biology research, clearing the path to revolutionary new techniques in protein design, which is the process of creating new and novel proteins that could solve some of the world's biggest problems.
Read the full article at: https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/
Related Papers:
- "Highly accurate protein structure prediction with AlphaFold" https://www.nature.com/articles/s41586-021-03819-2
- "De novo design of protein structure and function with RFdiffusion" https://www.nature.com/articles/s41586-023-06415-8
- "Generalized biomolecular modeling and design with RoseTTAFold All-Atom" https://www.science.org/doi/10.1126/science.adl2528
- "Accurate structure prediction of biomolecular interactions with AlphaFold 3" https://www.nature.com/articles/s41586-024-07487-w
---------
Chapters:
00:00 - Introduction
01:03 - What is a protein?
02:31 - Levinthal Paradox
02:53 - The Protein Folding Problem - how proteins fold to function
03:48 - John Kendrew / using X-ray crystallography to determine structure
05:02 - The Protein Data Bank (PDB)
05:45 - Christian Anfinsen's Nobel winning research
06:28 - Chemical structure of amino acids
07:17 - Secondary and tertiary folding structures
07:59 - Quaternary folding structure
08:16 - The beginnings of computational biology
09:09 - Critical Assessment of protein Structure Prediction (CASP) challenge
10:26 - Baker lab develops RoseTTA
11:31 - Google DeepMind introduces deep learning with AlphaGo
12:00 - DeepMind develops AlphaFold 1 to enter CASP 13
13:32 - AlphaFold 2 explained
15:28 - DeepMind wins CASP 14 and solves the protein folding problem
17:10 - An AI revolution in biological research
17:45 - How the Baker lab designs new proteins
19:53 - New AI tools predict cellular interactions, AlphaFold 3 and RoseTTAFold All-Atom
21:23 - David Baker, John Jumper, and Demis Hassabis win the Nobel Prize
---------
VISIT our website: https://www.quantamagazine.org
LIKE us on Facebook: / quantanews
FOLLOW us Twitter: / quantamagazine
Quanta Magazine is an editorially independent publication supported by the Simons Foundation: https://www.simonsfoundation.org
Quanta Magazine
Explore mind-bending developments in basic science and math research. Quanta Magazine is an award-winning, editorially independent magazine published by the Simons Foundation. http://www.quantamagazine.org/
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