[Linkpost] AlphaFold: a solution to a 50-year-old grand challenge in biology

Does knowing the structure of a protein help with simulating how it responds to any arbitrary/unknown protein/molecule/agonist/antagonist/superagonist? [it seems that even with all the protein structures that we do know well, that finding appropriate agonists of the protein with the desired action is still a huge unsolved problem]. Is simulation a much more difficult problem than "folding"?

This allows us to design "efficient" proteins (proteins designed "intelligently" often do tend to be smaller, less "messy" and "bulky" than naturally-evolved proteins [which also cross over at the most pedagogically unhelpful sites ever], and with protein folding solved, it may be easier for us to design proteins that are less complicated/more amenable to simulation than the natural set of proteins that exist => not to mention that it may be possible to find a specific transferase protein that is able to precisely add a methyl or carboxyl group to any molecule at any location, or a ligase that is able to split a molecule at any arbitrary location). We may also be able to design them based on properties like how easy it is to introduce them into the cell via mRNA (the genes for many natural proteins are not easy to introduce into the cell via CRISPR or AAV, but as protein design-space is so large, you can probably design another protein that carries out the same function that can be delivered into cells via mRNA or CMV-based vectors, without needing to force the corresponding gene at the right location at the cell's nucleus). 

Anyhow, designing proteins for industrial chemistry (eg properly degrade polyethylene plastics in the ocean) [and also those with a specific physical property rather than those that perform a very specific function] is a much easier problem than, say, figuring out how to make an extremely particular histone acetyltransferase or DNA methyltransferase or chaperone enzyme [often those at the center of hub networks and whose evolved messiness naturally evolves due to the necessity of needing to have other extremely precise interactions with other proteins that have also evolved to become messy bloated behemoths] localize/diffuse at the locations where it can precisely do the right things at {X} sites and not do the wrong things at the {Y} other sites. 

Also, this helps us develop a "periodic table of protein function" where you can design proteins that can carry out X function if you change certain motifs to it, and it will turn out as much cleaner/more organizeable/more predictable than the natural super-messy [and hard to organize] set of protein motifs we find in the wild. I think this is especially relevant for manufacturing and industrial chemistry - proteins that broadly carry out functions sort of similar to zymogen. 

The whole field of structural biology was 95% useless anyway.

As long as it produces machine-interpretable output, it's useful for training new algorithms, even if the vast majority of humans are unable to properly interpret protein structure.

^Anyhow, this post was replying to the idealized version. Protein folding is still far from solved, as https://twitter.com/mctucsf/status/1333447404910112768 explains. It's an exciting advance to be sure. I think this allows us to better figure out what a stable system of ultrastructural scaffolds is first before figuring out what precise things can be built USING those ultrastructural scaffolds.

I disagree with your assessment that structural biology is useless.  Knowing the shape of a protein can be pretty important if you want to perturb the protein's function by, say, finding or creating a small molecule that binds to it.  Crystal structures or cryo-EM structures can shed a lot of light on how a molecule binds to its target, which in turn can suggest further modifications to try and make a tighter binder.  It's not clear to me yet how easy or hard it will be to simulate ligand-protein binding using AlphaFold.  I'd lean toward 'hard' but maybe molecular dynamics simulations would dovetail well with a structure determined by AlphaFold.  

If you have a protein, and you know it's designed to bind to something, but you don't know to what, then maybe running a lot of imprecise simulations (using it's folded structure) will allow you to narrow down the list of candidates, and thereby significantly save the time and cost of experiments?

(Not an expert, just guessing)