This is a basic question, but how is the accuracy of the predicted biomolecular interactions measured? Are the predicted interactions compared to known interactions? How would the accuracy of predicting unknown interactions be assessed?
> Input mmCIFs are restricted to have resolution less than 9 Å. This is not a very restrictive filter and only removes around 0.2% of structures
NMR structures are more than 0.2% so that doesn't fit to the assumption that they implicitly remove NMR structures here. But if I filter by resolution on the PDB homepage it does remove essentially all NMR structures. I'm really not sure what to think here, the description seems too soft to know what they did exactly.
what do you trust more than NMR?
AF's dependence on MSAs also seems sub-optimal; curious to hear your thoughts?
that said, it's understandable why they used MSAs, even if it seems to hint at winning CASP more than developing a generalizable model.
arguably, MSA-dependence is the wise choice for early prediction models as demonstrated by widespread accolades and adoption, i.e., it's an MVP with known limitations as they build toward sophisticated approaches.
It wasn't until I started my postdocs, where I started learning about protein evolutionary relationships (and competing in CASP), that I changed my mind. I wouldn't say it so much as "multiple sequence alignments"; those are just tools to express protein relationships in a structured way.
If Alphafold now, or in the future, requires no evolutionary relationships based on sequence (uniprot) and can work entirely by training on just the proteins in PDB (many of which are evoutionarily related) and still be able to predict novel folds, it will be very interesting times. The one thing I have learned is that evolutionary knowledge makes many hard problems really easy, because you're taking advantage of billions of years of nature and an easy readout.