I was surprised to see the "breakthrough" in this article. The idea to use correlated mutations to identify contacting residues has been around since the 90's [1](if not earlier), but it seems like it's just now getting implemented/benchmarked in reliable prediction workflows. As someone finishing a PhD in this field, my perspective is that there's a big lack of software engineering talent here. There are ongoing eff…
Coevolution was part of it, but the actual "breakthrough" in their paper was the use metagenome sequences for protein structure prediction [1]. I don't disagree that engineering talent is needed in this field, but in this case it's not really fair to boil it down to an engineering problem. I mean, this work relies on the development of the Rosetta energy function, which is a wild beast of a scientific problem [2]. [1…
Ah -- I hadn't read into the metagenomics aspect. That is quite substantial. Thanks for the link.
> I don't disagree that engineering talent is needed in this field, but in this case it's not really fair to boil it down to an engineering problem. I mean, this work relies on the development of the Rosetta energy function, which is a wild beast of a scientific problem
Interesting -- I guess my disagreement here stems from the definitions of "science" and "engineering". I agree that Rosetta scoring function development is extremely valuable. I also agree that figuring out how to efficiently balance coevolution-derived constraints vs. traditional Rosetta score is a substantial accomplishment. However, the profs on my committee consider this type of work (which I find really meaningful) to be more "engineering" (or "optimization") than "science". Their definitions had never sat well with me, and I'm glad you agree that this is valuable science.