interesting observation and experience. must have made thesis development complex, assuming the realization dawned on you during the phd.
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.
My realizations happened after my PhD. When I was writing my PhD I still believed we would solve the protein folding and structure prediction problems using classical empirical force fields.
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.
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.