Basically, you see these 3D representations of specific proteins as a crumple of ribbons-- literally like someone ran multi-colored ribbons though scissors to make curls and dumped it on the floor (like a grade school craft project).
So... I understand that proteins are huge organic molecules composed of thousands of atoms, right? Their special capabilities arise from their structure/shape. So basically the molecule contorts itself to a low energy state which could be very complex but which enables it to "bind?" to other molecules expressly because of this special shape and do the special things that proteins do-- that form the basis of living things. Hence the efforts, like Alphafold, to compute what these shapes are for any given protein molecule.
But what does one "do" with such 3D shapes?
They seem intractably complex. Are people just browsing these shapes and seeing patterns in them? What do the "ribbons" signify? Are they just some specific arrangement of C,H,O? Why are some ribbons different colors? Why are there also thread-like things instead of all ribbons?
Also, is that what proteins would really look like if you could see at sub-optical wavelength resolutions? Are they really like that? I recall from school the equipartition theorem-- 1/2 KT of kinetic energy for each degree of freedom. These things obviously have many degrees of freedom. So wouldn't they be "thrashing around" like rag doll in a blender at room temperature? It seems strange to me that something like that could be so central to life, but it is.
Just trying to get myself a cartoonish mental model of how these shapes are used! Anyone?
There are tools such as DSSP https://en.wikipedia.org/wiki/DSSP_(hydrogen_bond_estimation... which will take out the 3d structure determined by crystallography and spit out hte ribbons and helices- for example, for helices, you can see a specific arrangement of carbons along the protein's backbone in 3d space (each carbon interacts with a carbon 4 amino acids down the chain).
Protein motion at room temperature varies depending on the protein- some proteins are rocks that stay pretty much in the same single conformation forever once they fold, while others do thrash around wildly and others undergo complex, whole-structure rearrangements that almost seem magical if you try to think about them using normal physics/mechanical rules.
Having a magical machine that could output the full manifold of a protein during the folding process at subatomic resolution would be really nice! but there would be a lot of data to process.
Certainly, in a corpo like mine (Genentech/Roche) protein structures have a long history of being used in drug discovery- not typically a simple "dock a ligand to a protein" but more for constructing lab experiments that help elucidate the actual mechanistic biology going on. That is only a tiny part of a much larger process to work on disease targets to come up with effective treatments. Genentech is different from most pharma in that their treatments are themselves typically proteins, rather than small molecules.
It seems like that's the basic principle to understand.
In practice, the force fields are well understood but to be computationally efficient, they have to approximate just about everything. Examples: since number of inter-atom distance pairs goes up with N**2 atoms, you need to have tricks to avoid that and instead scale around n log n or even n if you can do it. When I started, we just neglected atoms more than 9 angstrom apart, but for highly charged molecules like DNA, that leads to errors in the predicted structure. Next, typically the force fields avoid simulating polarizability (the ability of an atom's electron cloud to be drawn towards another atom with opposite charge), also because expensive. They use simplified spring models (lterally hooke's equation) for bond lengths, bond angles. The torsions (the angle formed by 4 atoms in a row) haev a simplified form. The interatomic relationships are not handled in a principled way, instead treating atoms as mushy spheres....
After having made major contributions in this area, I don't think that improvements to force fields are going to be the most effective investment in time and energy. There are other bits of data that can get us to accurate structures with less work.