Shape of a protein predicted by two different AI models (ESMFold on the left, AlphaFold on the right)
Submitted by greentea387 t3_yt6job in singularity
Reply to comment by NineMinded in Shape of a protein predicted by two different AI models (ESMFold on the left, AlphaFold on the right) by greentea387
For research purposes, this is perfect. It's the best approximation to the native protein structure we can get. Before this, we had xray crystallography, but the problem with that was it cause the protein to change shape.
It's just in silico model, it needs a whole process to test a protein in vitro.
Just combine this with a 3D NMR and cryo EM model and you're good to go.
What's that? can you explain this concept to me and why it'd help
the 3 dimentional structure of a protein is key to its function, so to understand what a particular protein does or how disease occurs, we want to look at it. We have multiple different methods to do this, and each has their advantages. Our main goal when looking at a protein is to see it in its original form, as would be found in the cell, otherwise known as the native structure. The issue with some techniques, such as x ray crystallography, is that the conditions required to cause a protein to crystalize lie outside the normal function range the protein works in, so the shape we see is more of an approximation. 3D NMR is a technique that is capable of seeing a molecule based on how atom react to an external magnetic field. With 3D NMR we are looking at this reactivity using 3 atoms: hydrogen, carbon, and nitrogen. From the gathered data, we can form a 3d computer model that is more closly resembling the native structure. The additional advantage of 3D NMR is that we can see the areas of the protein that are static and other parts that are more dynamic. Cryo EM or cyrogenic electron microscopy requires us to freeze the proteins on a platoform, and then send a beam of electrons to see the proteins. The resulting image is a blurred representation of the protein, but acts as a quick and easy starting point for research. When combined together, we get a good idea of what the structure looks like.
Xray crystallography produces the most detailed images we can get, up to the resolution of 1 angstrom, which lets us see hydrogen bonding, but getting the protein and having it synthesized in a manner that allows it to be imaged is just one of the few headaches researchers have to go through before they can get an image. When I was a researcher the lab next to us worked with cryo EM. It was cool.
To be fair, this modeling is based a lot off of learning from previous crystal structures and the biases those may or may not impose. An x-ray crystal model (or NMR/Cryo-EM structure if you have a very small or very large protein respectively) is still considered the “ground truth”, while these machine learning generated structures are more for hypothesis generation/approximations (that may help in building models from experimental structural data).
Crystal packing needed for x-ray crystallography may rigidify a protein, but at least within the field of structural biology it is not believed to alter the shape of the protein. Likely it just hides the dynamic conformations a protein can occupy - and machine learning methods also suffer from this flaw as they only predict a single structure.
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