ESMFold by Meta and AlphaFold by Google's DeepMind

Model Confidence:

dark blue: Very high (pLDDT > 90)

light blue: Confident (90 > pLDDT > 70)

yellow: Low (70 > pLDDT > 50)

orange: Very low (pLDDT < 50)

Protein: Serotonin receptor 2beta

Organism: Gryllus bimaculatus

Protein Sequence: EQKATKVLGVVFFTFVVLWAPFFVLNLVPTVCGEECERRIDHRVFDFVTWLGYASSMVNPIFYTIFNKVFRQAFKKVLTCQYRKKVWRPPA

is there any experimental data to compare?

I don't think so for this protein

Ukraine protein?

It's probably a membrane protein, which would make it difficult to crystalize to get a structure experimentally. That's the promise of software like AlphaFold.

They’re slightly different though. Wouldn’t that make their functions slightly to wildly different?

Seems to me that they’re such small changes, they would be part of the normal dynamics of the protein at room temperature. So they could very easily interconvert. If that’s indeed the case they’re essentially the same thing.

Very. Proteins must maintain their shape as much as possible if they are function. When a protein interacts with another compound or structure, the positioning of the amino acid side chains is crucial. The side chain positioning acts lkle a key in a lock, and exert their influence based on their angle, distance, and positioning relative to what they interact with.

Can you post another one which has been experimentally tested to see which one’s more accurate?

While this might be true, getting close to the correct shape can speed research, I assume.

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.

So which one is correct

Possibly both of them have parts correct that the other one does not. The ESMFold shows more confidence than Alphafold but I'm also not sure if they measure confidence differently.

This may or may not be always true, so a good scientist would not make that assumption unless they didn't have a choice.

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I would like to introduce you to the world of mesophase crystallisation, good sir.

Can't wait till the first experiment where AI generated proteins are tested on animals, turning into a prion-like disease that is impossible to cure and spreads like wildfire, creating a zombie apocalypse.

I was is also going to start my reply 'As a layman'. Want to start a really basic 2chord rocknroll band? The Laymen. PS no I can't explain.

With so many opportunities for them to go after why did they copy Deepmind?

This.

Note to all posters, it would be helpful if you explain things like this.

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.

It’s not a copy. Completely different architectures. ESMfold uses a language model while AlphaFold2 does not. ESMfold can predict on a single sequence, and AlphFold2 works best when you give it multiple related sequence in a multiple sequence alignment. ESMfold is also way faster (though not as accurate than AlphaFold2)

Only if those 2chords are B7add13(No5) and E7#5 .

You mean the CoViD vaccine?

Sorry, I am not aware of such a protein

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.

Really? Could these both not be viable structures that a protein could switch between due to thermal fluctuations?

It looks like it’s not a particularly complex protein, so I imagine it’s some ligand or subunit for something, in which case the “correct” structure would be stabilized by its interaction with another object.

He's discussing possibilities. No concrete or final assumptions being made. A "good scientist" leaves all possibilities open. Not like it matters here, we're not the researchers, just nerds discussing.

Advantages?

It provides an artificial lipid matrix which stabilises multiple domains simultaneously making some membrane proteins more amenable to crystallisation.

Protein structures have both static and dynamic sections inside of them, but computer models are not not well suited to predict them. You'd need to confirm you findings with an imaging experiment, like 3d NMR, that is capable of capturing those dynamic, shifting structures.

Sure, but I’m just skeptical of the claim that these two predicted structures would give wildly different functions, or that they really are distinct on something this simple.

I cold believe it if for example the catalytic core of a barrel protein had small alterations in structure, but this is just two helices next to each other with a small disordered domain on the bottom.

It can. A single point mutation of one amino acid is enough to cause a deviation in normal function, as is the case of some diseases.

Yes I’m aware.

But these arguments by analogy don’t do it for me for something this simple, that doesn’t even look like it would have a catalytic core without some other subunit. Do you even know what protein this is?

Edit:

It’s a serotonin receptors from cricket. It’s a membrane protein so it should be stabilized by going through the membrane.

Ahhh, will need to look into it more. Some cutting edge stuff right there. Biology is way too complex. Endless forever evolving complexity. Absolutely beautiful. Make it stop. 😭😭😂

Ok. I guess I meant to say why not focus on a new application?

What's that? can you explain this concept to me and why it'd help

im a biochemist, so yes i know what a protein is. Im trying to explain the importance of maintaining the 3d structure of a protein to its function. Both proteins have the same sequence, but there is a slight difference in what the software rendered. Now, based on the sequence, a cell would reliably recreate the same protein. However, in the context of having 2 slightly different structures from the same amino acid strand, yes, you can have widely varying functions that result depending on the original purpose of the peptide strand.

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.

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And I’m a synthetic biologist in protein engineering. What I’m skeptical about is that for this protein specifically, this change in structure plays a major role in function determination, due to its simplicity, and that we are seeing two distinct folds that are locked from each other.

The point ultimately is moot, the protein chosen is a membrane bound protein, so the lipid layer will provide stabilization.