How AI Cracked the Protein Folding Code and Won a Nobel Prize

This is the inside story of how David Baker, Demis Hassabis and John Jumper won the 2024 Nobel Prize in Chemistry for 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.

Related Papers:

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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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[sabofx]

Thank you Deepmind/Google for open sourcing (instead of privatizing) the resulting science 🥰

[genomicmaths]

I am really sorry, but the Protein Folding Code (PFC) is not solved yet. To predict the 3D protein structure of a protein using machine learning algorithm does not uncover the PFC, which essentially is a biophysical/biochemical problem. To solve the PDF means to unveil the set of biophysical/biochemical rules that determine, given the aminoacid sequence, the 3D structure of the protein. That is, the title: "How AI Cracked the Protein Folding Code" is misleading. Nevertheless, the work predicting the 3D protein structure using machine learning is a meritorious work and worthy of a Nobel Prize, but please, do not mislead the public.

[rapmurali]

Thanks Quanta Magazine for producing such quality and informative videos.

[DeMLG]

I just can't get more surprised at the quality of these videos and the amount of quality information compressed here

[FriesOfTheDead]

1:06 Serotonin is not a protein, it's a neurotransmitter, probably 10000 times smaller than the protein you have representing serotonin, how do you get that so wrong?

[djp]

If you think about it, we are a bunch of proteins trying to learn how proteins work.

[oscarmvl]

What a great video! Great explanations, great questions to the experts, and great production overall!

[TheYgds]

After seeing and working with these tools since 2020, I remain flabbergasted. In undergrad, back in 2008, we were essentially told that until quantum computing was cracked the protein folding problem, much less the protein complex problem wouldn't be solved. While there are still outstanding questions and heights this technology could go on to tackle, it has gone far beyond what was even conceivable 10 years ago.

[AI-Life-]

The quality of your content is amazing! You’re a natural when it comes to explaining things, and your channel is truly a go-to for anyone looking to lear

[sebastjenschoenaers]

Alphafold was a major breakthrough but it didn't by far solve the problem entirely. Disordered and flexible region in the proteins secondary structure are very difficult to resolve computationally, but they are often very important for protein function. There's still no way around that.

[jeffmorrison]

It still amazes me that quality content like this is free. Thanks!!!

[andrewv]

1:08 what is serotonin doing among the examples of proteins?

[TommyLikeTom]

One of these guys, Demis Hassabis, worked on several games from my childhood, including Theme Park and Black and white.

[deyvismejia]

AI did NOT solve protein folding 😬. While the prize was well deserved this is an overstatement. Proteins can have many different shapes and undefined shape as well. Most proteins used are vertebrate proteins. There needs to be experimental validation of predicted shapes. Not all proteins are easily crystallized

[chillappe]

John Jumper is on another level. Used AI to do the interview.

[DanhNguyen-gyyk]

An absolutely wonderful video! Truly appreciated and respected!

[LinkAranGalacticHero]

I still have one question though. From my biology knowledge, protein folding is also affected by supporting molecules (thinking about chaperons) or is anyway heavily environment dependent, which can vary at different points of the cell. How is this factored into the algorithm? What about chaperons, specifically?

[NandkumarKamatGoa]

Outstanding video for someone who taught bioinformatics and chemo informatics at Goa University and made post graduate students play a lot with Fold it game

[invictusyou]

It was literally jaw-droppingly awesome
Hatts off to you guys
It's literally a sense of proud and accomplishment in me
Making me more excited about all the incredible things coming ahead and I'll get to learn and search more on

[abhishekadile]

So this Nobel prize is culmination of work starting from 1950 when that first database was developed.