AlphaFold

Seems like DeepMind just caused the ImageNet moment for protein folding.

Blog post isn't that deeply informative yet (paper is promised to appear soonish). Seems like the improvement over the first version of AlphaFold is mostly usage of transformer/attention mechanisms applied to residue space and combining it with the working ideas from the first version. Compute budget is surprisingly moderate given how crazy the results are. Exciting times for people working in the intersection of molecular sciences and ML :)

Tweet by Mohammed AlQuraishi (well-known domain expert)

DeepMind BlogPost

UPDATE:
Nature published a comment on it as well

Hi **,**

I'm curious about AlphaFold and its applications, especially in wet lab work. Has anyone in the community worked with AlphaFold, and what has your experience been? I'm particularly interested in understanding the delimitation of AlphaFold and exploring potential features that could be added for broader use. Any insights or suggestions would be greatly appreciated!

"Last year we presented #AlphaFold v2 which predicts 3D structures of proteins down to atomic accuracy. Today we’re proud to share the methods in @Nature w/open source code. Excited to see the research this enables. More very soon!"

I did not see this one coming, I got to admit it.

Paper here:

Figure 2 here:

In Figure 2, they show a series of charts but I don't understand what is the main point the authors want to make. There's a brief paragraph that mentions its:

we observe high side-chain accuracy when the backbone prediction is accurate (Fig. ) and we show that our confidence measure, the predicted local-distance difference test (pLDDT), reliably predicts the Cα local-distance difference test (lDDT-Cα) accuracy of the corresponding prediction (Fig. ). We also find that the global superposition metric template modelling score (TM-score) can be accurately estimated (Fig. ). Overall, these analyses validate that the high accuracy and reliability of AlphaFold on CASP14 proteins also transfers to an uncurated collection of recent PDB submissions, as would be expected

My interpretation of Figure 2 A is that it shows a histogram plot the fraction of protein chains that are a certain angstrom. This is straightforward.

Fig 2.b. is pretty straightforward. It's a plot moving to the top right showing the correlation between backbone accuracy and side-chain accuracy.

lDDT-Cα measures backbone accuracy using only Cα atoms, whereas, lDDT (local difference distance test) is a measure of structural agreement.

Fig 2.c. is where it gets confusing for me. The text mentions that:

lDDT-Cα = 0.997 × pLDDT − 1.17 (Pearson’s r = 0.76).

n = 10,795 protein chains

This should be the blue line in the plot right? And the dots are individual protein chains, ie. the n = 19,795

So, does the blue line represents the prediction and the blue points are the "ground truth" of protein chains? And is that why the blue line isn't drawn through the cloud of blue points but is slightly off to the right?

Also, is the shaded region of the linear fit in the figure caption referring to the blue points in the inset or something else?

The shaded region of the linear fit represents a 95% confidence interval estimated from 10,000 bootstrap samples.

Fig 2.d. I'm still confused about this. It should be similar to figure 2.c.

The structure of a protein determines its functionality. Researchers have used this data in the past to design new drugs, vaccines, and enzymes. You can access the database for free here -

This new database will allow researchers to gain a deeper understanding of protein families, how they interact and evolve, etc. Deepmind has written some use cases here -

How would you use it? What would you like to explore or predict with it?

And maybe I should add a bit more to the question if relevant. What part of the field and in what way?

Were Computational Biologists and Bioinformaticians heavily involved in the creation of Alpha Fold? Or is most of its success due to the Deep Learning and Reinforcement Learning researchers working on it?

Also, do you think future "big leaps" in Biological research will come out of domain related research from within Computational Biology and Bioinformatics or rather non domain specific research (like NLP models that are able to analyze multiple scientific papers at once or further deep learning research along the lines of Alpha Fold)?

This feedback from scientific practice should definitely be taken into account by the community:

Quote:

our results show that AlphaFold predictions are not better representations of the contents of a crystal than the models deposited in the PDB, as the deposited models agree much more closely with experimental data where the predicted and deposited models differ