How AlphaFold solves protein folding

*Helpful videos and blogposts about attention and AlphaFold*

[WanJae]

The media would have you believe that researchers merely asked AI to solve the folding problem, and it magically did. This video is an excellent illustration that the problem needed to be really well understood by the researchers, and a method had to be found in which to frame the problem in such a way that it was suitable for machine learning to solve. This is not unlike traditional software development. For now, there's still a lot of human intelligence required in getting AI to do useful things.

[lbgstzockt]

The triangle inequality is the kind of mathematical construct that, when learning about, seems kind of useless or obvious but then shows up everywhere.

[LookingGlassUniverse]

I know this is a bit different from my usual videos! I've been very interested in how AI in science recently, and AlphaFold is a great case study.

I was shocked when I heard that protein folding, one of the most notorious and important questions in computational biology, had suddenly been solved by AI. In this video I explain what the AlphaFold algorithm actually does. Next week I want to see if I can replicate it.

[adityakhanna]

Gosh, I remember at the beginning of my undergrad talking to my friend about protein folding and how hard is it. Five years later, we were talking about how AlphaFold just solved the problem. It's crazy!

Would love to see more diverse stuff from you!

[aren]

You did a very good job explaining this. I watched Nazim Bouatta's whole presentation after seeing your video, and then came back to re-watch this. The way you fill in some of my gaps in understanding that I missed to pick up in his presentation, and the way he goes into detail on the things you summarized is great! Really helped me understand this incredible model even as a beginner in ml. I applaud you for your work.

[jaredokada]

In undergrad, I used to work in a structural biochemistry lab. I worked a lot with predicting protein structures using homology modeling to comparing two proteins with similar primary structures and other characteristics and using that to predict the tertiary structure of our protein of interest. I graduated before alphafold came out but I remember the excitement of those working in my former lab when it came out. Back then with our ab initio protein modeling techniques, I think the best we could do was proteins with like 100-200 residues with limited accuracy but I think alphafold can due over a thousand with decent accuracy. I no longer do any of that stuff but it is pretty amazing to see such a massive breakthrough in my former field of study.

[gelly]

thanks! there have been a few explanations of alphafold in various videos, but now I can say that finally there is a good explanation video on youtube

[dibenp]

Come for the animation. Stay for the content. ❤

[tomholroyd]

And Go. We thought Go was impossible for a computer. We learned that AI can see patterns that we overlook

[MelindaGreen]

The triangle inequality went a long way towards understanding for me, thanks

[zachatomata]

It's interesting to see some AI topics on your channel. I find quantum physics interesting, but a lot of it goes over my head admittedly. AI/ML is more in my wheelhouse, so I'm excited for the follow up video on this. I think a lot of the more interesting AI algorithms are those like AlphaFold which combine some expert/domain knowledge to provide structure to more numerical machine learning algorithms. If you're starting to explore more AI literature, do you think you would try to create a video combining your own domain knowledge of physics plus AI to solve something new?

[mikegb]

And Deep Mind isn't even a biotech-focused research lab. They are just a bunch of computer scientists doing cool things with neural nets: playing video games, playing Go, protein folding, controlling plasma in a tokamak, etc...

[hansisbrucker]

I remember playing Fold-it years ago to help scientists figure out proteine folding as computers couldn't do it. Glad to see that computers *can* do it now 😊

[kasinadhsarma]

thank you indepth analysis for the alpha phold

[carla]

Very well explained! Thank you very much for this. I have an upcoming presentation about AF and this helps a lot to understand the main idea.

[nubconnor]

It doesn't solve protein folding. It solves protein structure prediction to a very good accuracy based on amino acid sequence, it potentially covers the conformational landscape a protein can have. It not solving protein folding, it doesn't fold the protein nor give any insights (as of yet) on the stages. In addition, the structures predicted of heavily biased based on the current state of the PDB, dominated by X-ray crystallography structures. Blurry interaction (or fuzzy), ensemble interactions are coming more prevalent, something that is only now being touched upon. It is incredibly useful for pairing with experimental structure determination, and generating hypothesis, for example for molecular replacement (X-ray), model building (Cryo-EM) and informing biochemical perturbation of a protein-protein interaction.

[checkm]

Awesome animations. Really nicely done :)

[yan-amar]

Look at AlphaGo go, how AlphaFold folds, and Alphabet... Bets?

[philochristos]

Does it just predict how known proteins fold, or can it predict how a completely novel protein would fold? Like if I just invented an amino acid sequence that doesn't actually exist in nature, could it predict how that protein would fold if it were actually made?

[philochristos]

I don't know much biology, but isn't it the case that not all amino acid sequences are capable of folding into a stable shape? I wonder what AlphaFold would say about those. Would it say, "I mean, like, we can fold parts of it, but we can't get the whole thing to fold into a stable shape"?