https://www.reddit.com/r/askscience/comments/xkjni6/googles_alphafold_breakthrough_allows_us_to_see/
created by _dekappatated on 21/09/2022 at 22:48 UTC
7 upvotes, 3 top-level comments (showing 3)
How exactly does this help in the creation of new medications? Do we know that if a protein looks like X, it can cure/treat Y disease?
Comment by Saedius at 22/09/2022 at 01:37 UTC*
21 upvotes, 0 direct replies
Most of designing a new medicine is making a new molecule that sticks to a protein and either activates or inactivates it. If we can see the shape of the protein, it can be easier to design something that fits in the target (maybe the protein has a narrow pocket here I can fill with a phenyl group, maybe a more open one that I target with a trifluoromethyl - maybe there's a lysine so I want a negative, or partially negative region in my molecule to interact with that in a favorable electrostatic interaction, etc). That being said, knowing how to bind to the protein is only one of a number of steps towards a new medicine, but anything that accelerates one of them has the change to speed development.
This of course presumes that we understand the disease sufficiently to know that the protein is implicated in the causation of said disease. That is another, bigger problem altogether. Been on a number of programs where the early studies looked good, but when we dug deeper the whole thing unraveled (there's many reasons that can happen, including some that are literally just bad luck).
Comment by hatsune_aru at 22/09/2022 at 08:38 UTC
6 upvotes, 0 direct replies
the functionality of protein largely depends on the final folded shape, and the problem of predicting the folded shape based on the amino acid chain it was made from is a hard problem (which is what the field of protein folding is supposed to solve).
the folded protein has features that need to be in the right spot and in the right shape for it to work with the existing chemistry inside your body--that's why the folded shape is important.
Comment by physics_defector at 23/09/2022 at 16:07 UTC
2 upvotes, 0 direct replies
There are a few ways, and the primary one I know of via existing methods for what's called "docking" and "molecular dynamics". Both are computational techniques which have proved enormously helpful for accelerating drug development, and have been used by the pharmaceutical industry and academic research groups for a decade or two now at least.
In molecular docking methods, you take the known 3D structure of a protein and input it into a docking software along with some set of small molecules (a term used to describe most drugs). The software then uses the physics of charge densities and the geometric structure of both protein and small molecule to assess where on the protein a molecule might fit, and also returns energy scores for any locations which pass a user-specified or automatic threshold. From here you can sometimes heuristically assess the drug's potential viability, depending on what's known about the protein, what binds to it, and where this drug binds to it. For example, if you want to create a competitive inhibitor[1] you'll usually want it to bind the same binding pocket as that protein's usual ligand or substrate. Here are a pair of highly cited articles on molecular docking. The first[2] is a review which gives an overview of the method for small molecules binding to proteins, and the second[3] is for using docking to study how proteins interact with other proteins. Protein-protein docking also allows you to study how drugs might be able to disrupt the interactions between proteins, which mediate a huge number of medically relevant processes in physiology.
1: https://www.ucl.ac.uk/~ucbcdab/enzass/inhibition.htm
2: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3151162/
3: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213718/
In molecular dynamics, you actually simulate the movement of every atom in the protein and a small molecule using a mix of Newtonian mechanics, electrical physics, and a bunch of fudge-factors which have been thrown in because they empirically improve performance. Here it's usually helpful to have already done a docking analysis, because molecular dynamics can then show you the dynamics of the drug binding (or failing to bind!) the identified site(s) on the protein, in addition to showing you how the protein's structure changes when the drug binds to it. This article[4] is an overview of how molecular dynamics is used for both basic protein science and for drug discovery, and one of the co-authors is Ron Dror - a pioneer and leader in the use of high-performance computing to accelerate MD.