Been out of the field for a while, could someone currently in it qualify these results? Hyperbolic title notwithstanding, they approach 90% median free modeling accuracy. The "other 90%" still remains to be solved...
I'd love to hear more. Specifically, what do you think that computational biology can do that you think Art doesn't understand or credit?
[1]: https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf...
This model requires a collection of wild-type proteins in an accurate MSA. Producing an accurate MSA is hard even if you have many homologs.
They require protein homologs which means they can "only" do this for wild-type proteins. This work is useless with mutant and synthetic proteins. This is a big advancement that will assist crystallographers and NMR structural biologists with difficult wild-type proteins, but it doesn't "solve protein folding" by any stretch of the imagination.
To assess co-evolutionary couplings the amount of homologs in the MSA is not as important as the number of effective sequences (i.e. sequence depth and diversity) in it.
> They require protein homologs which means they can "only" do this for wild-type proteins.
Even remote homologs work, as shown by the widespread use of HHM-based methods in the prediction pipelines.
> This work is useless with mutant and synthetic proteins.
Unless you generate a flurry of data with them using deep mutational scanning for example. As long as correlated mutations are present in the MSA the technique should work as expected no matter where the protein sequences originated.
And another one about its application in structure prediction: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295002/