Expert Impressions of Deep Mind Alphafold Protein Folding Advance

Expert Impressions of Deep Mind Alphafold Protein Folding Advance

Expert Impressions of Deep Mind Alphafold Protein Folding Advance

Brian Wang |
December 5, 2020 |

Lior Pachter said “Deep Mind AlphaFold (protein folding) results are just markedly different from what a lot of other methods are producing. This is not an incremental improvement.”

But protein folding is not solved. Not only is it not even a well-defined statement to say something like that (others have pointed out that there is a lot of subtlety in what one even means by “protein folding”) but it’s not even the winner for all the CASP14 proteins.

I don’t mind that Google hyped this. It’s impressive work they did and there are super exciting prospects for the field. I do mind that many (computational) biologists who ought to know better are going around screaming “protein folding is solved!” Have some self respect.

— Lior Pachter (@lpachter

Stephen Curry provides this analysis –

Firstly, there is no doubt that DeepMind have made a big step forward. Of all the teams competing against one another they are so far ahead of the pack that the other computational modellers may be thinking about giving up. But we are not yet at the point where we can say that protein folding is ‘solved’. For one thing, only two-thirds of DeepMind’s solutions were comparable to the experimentally determined structure of the protein. This is impressive but you have to bear in mind that they didn’t know which two-thirds of their predictions were correct until the comparison with experimental solutions was made.

Alphafold 2 will certainly help to advance biology. For example, as already reported, it can generate folded structure predictions that can then be used to solve experimental structures by crystallography (and probably other techniques). So this will help the science of structure determination go a bit faster in some cases.

However, despite some of the claims being made, we are not at the point where this AI tool can be used for drug discovery.

Alphafold 2 is predicting structures to an average accuracy of 0.16 nanometers but to get t0 reliable insights into protein chemistry or drug design we will need an average structure prediction accuracy of 0.03 nanometers.

A friend (who does not work in science) asked me today whether it is true that “protein folding has been solved”. My short answer:

The AlphaFold method produced very impressive results on CASP14. Protein folding is not a solved problem.

— Lior Pachter (@lpachter) December 1, 2020

The AlphaFold results are impressive not just because they are (on average) much better than other methods, but because the improvement is so great in just the last 2 years that it suggests much more is still possible.

— Lior Pachter (@lpachter) December 1, 2020

CASP is both the gold standard for assessing predictive techniques and a unique global community built on shared endeavour. Accuracy is measured on a range of 0-100 “GDT”. #AlphaFold has a median score of 92.4 GDT across all targets – its average error about the width of an atom.

— DeepMind (@DeepMind) November 30, 2020

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