g-f(2)23 DeepMind demonstrated that AI help us understand the world: AlphaFold can predict, in record time, the shape of proteins to within the width of an atom

Extra-condensed knowledge

DeepMind has already notched up a streak of wins. 

• Demis Hassabis, DeepMind’s public face and co-founder, has always stressed that these successes were just stepping stones towards a larger goal: AI that actually helps us understand the world.
• AlphaFold predicted the structure of dozens of proteins with a margin of error of just 1.6 angstroms—that’s 0.16 nanometers, or atom-sized. 
• Figuring out what proteins do is key to understanding the basic mechanisms of life, when it works and when it doesn’t.
• The breakthrough could help researchers design new drugs and understand diseases. 

Genioux knowledge fact condensed as an image.

Condensed knowledge 

• MIT Technology Review. 
• DeepMind has already notched up a streak of wins, showcasing AIs that have learned to play a variety of complex games with superhuman skill, from Go and StarCraft to Atari’s entire back catalogue. Demis Hassabis, DeepMind’s public face and co-founder, has always stressed that these successes were just stepping stones towards a larger goal: AI that actually helps us understand the world.
• A protein is made from a ribbon of amino acids that folds itself up with many complex twists and turns and tangles. This structure determines what it does. And figuring out what proteins do is key to understanding the basic mechanisms of life, when it works and when it doesn’t. 
• In this year’s CASP, AlphaFold predicted the structure of dozens of proteins with a margin of error of just 1.6 angstroms—that’s 0.16 nanometers, or atom-sized. 
• This far outstrips all other computational methods and for the first time matches the accuracy of experimental techniques to map out the structure of proteins in the lab, such as cryo-electron microscopy, nuclear magnetic resonance and x-ray crystallography.  
• These techniques are expensive and slow: it can take hundreds of thousands of dollars and years of trial and error for each protein. AlphaFold can find a protein’s shape in a few days.
• The breakthrough could help researchers design new drugs and understand diseases. 
• In the longer term, predicting protein structure will also help design synthetic proteins, such as enzymes that digest waste or produce biofuels. 
• Researchers are also exploring ways to introduce synthetic proteins that will increase crop yields and make plants more nutritious.
• Astronomical numbers.
• Identifying a protein’s structure is very hard. For most proteins, researchers have the sequence of amino acids in the ribbon but not the contorted shape they fold into. And there are typically an astronomical number of possible shapes for each sequence. Researchers have been wrestling with the problem at least since the 1970s, when Christian Anfinsen won the Nobel prize for showing that sequences determined structure.
• Puzzle pieces. 
• AlphaFold builds on the work of hundreds of researchers around the world. DeepMind also drew on a wide range of expertise, putting together a team of biologists, physicists and computer scientists. 
• DeepMind trained AlphaFold on around 170,000 proteins taken from the protein data bank, a public repository of sequences and structures.
• Researchers are now waiting to find out exactly how AlphaFold works. “Once they describe to the world how they do it then a thousand flowers will bloom,” says Baker. “People will be using it for all kinds of different things, things that we can't imagine now.”

Category 2: The Big Picture of The Digital Age

[genioux fact extracted from MIT Technology Review]

Authors of the genioux fact

Fernando Machuca

References

DeepMind’s protein-folding AI has solved a 50-year-old grand challenge of biology,  Will Douglas Heaven, November 30, 2020, MIT Technology Review.

ABOUT THE AUTHORS

Extracted from MIT Technology Review

Will Douglas Heaven

I am the senior editor for AI at MIT Technology Review, where I cover new research, emerging trends and the people behind them. Previously, I was founding editor at the BBC tech-meets-geopolitics website Future Now and chief technology editor at New Scientist magazine. I have a PhD in computer science from Imperial College London and know what it’s like to work with robots.