> Could you give an overview of how people can leverage this (or how you might?).
Short answer: nobody knows. Traditionally, protein folding is a solution in search of a problem, but that's largely because the predictions were...unusably bad. This was always more of a super-difficult validation problem for the force fields and simulation methods, which could then be used for other problems of greater value (such as rational protein design, or simulation of the motion of proteins with known structures).
These predictions are better, but still pretty far from the level of precision that you'd want for any kind of rational drug design, where the exact locations of protein side-chains (for example) matter a lot. You'll note that AlphaFold returns structures that are "relaxed" using one of the oldest simulation systems for proteins: AMBER. So it's not exactly a clean-room solution to the problem, and you can't assume that the details (which matter to drug design) are going to be any better than for the older methods.
But that said, if you have a method that can reliably give you a blurry view of the overall shape of a protein, even that could be useful for things like target discovery or inference of biological networks. But this is still a lot closer to pure research than "revolutionizing drug discovery", as is frequently batted around on reddit, HN and the press.
Also I would say that really they just made improvements to protein structure prediction, not protein folding which is the dynamic process by which proteins reach their equilibrium fold.
I do agree with this but would add that you wouldn't want to do static docking to a single protein and sidechain configuration anyway so you're bound to find a usable forcefield. The incoming structure (ideally!) only needs to fall into the right energy landscape valley. If your forcefield and MD simulation can't keep this stable within the natural protein's configuration space you are probably not going to make progress, and you need the simulations to evaluate the natural energy landscape at body temperature, not cryo temperature.
There are some examples of this issue in the AlphaFold blog, some protein loops that they thought were mispredicted but it turned out they were part of an energy degeneracy so the natural state fluctuated pretty wildly, so if you can't simulate this properly it matters less how accurate the incoming structure is (to a certain degree of course).
Any drug target of any real-world interest is going to have local motion (i.e. floppiness that is dependent on circumstance and time) that matters at least as much to the finding of a drug than the fold of the protein itself.
One place I hesitate here is that step 0 to doing that kind of a simulation is having a decent starting structure. So maybe protein folding can help there. But I'm also skeptical, because the quality of the structure matters a great deal to getting a good simulation. A bad/blurry structure is of marginal use.
Short answer: nobody knows. Traditionally, protein folding is a solution in search of a problem, but that's largely because the predictions were...unusably bad. This was always more of a super-difficult validation problem for the force fields and simulation methods, which could then be used for other problems of greater value (such as rational protein design, or simulation of the motion of proteins with known structures).
These predictions are better, but still pretty far from the level of precision that you'd want for any kind of rational drug design, where the exact locations of protein side-chains (for example) matter a lot. You'll note that AlphaFold returns structures that are "relaxed" using one of the oldest simulation systems for proteins: AMBER. So it's not exactly a clean-room solution to the problem, and you can't assume that the details (which matter to drug design) are going to be any better than for the older methods.
But that said, if you have a method that can reliably give you a blurry view of the overall shape of a protein, even that could be useful for things like target discovery or inference of biological networks. But this is still a lot closer to pure research than "revolutionizing drug discovery", as is frequently batted around on reddit, HN and the press.