[whateveryou381]

I know that people say this but I have questions: 1. Are there concrete examples where drugs have been discovered from protein folding structures? What are the biggest ones? 2. Is there machinery that already exists to take in 3D protein structures and create drugs? or is this yet another issue? 3. How does folding of a protein in the current state impacting the use of the protein when it is used? Presumably these proteins are similar to polymers where they are not super rigid in all environments, how does the environment effect the protein folding?

[beowulfey]

Two of the first, back in 1999 [1], were Zanamivir and Oseltamivir (Tamiflu). Influenza neuraminidase inhibitors. Researchers examined a structure of neuraminidase co-crystallized with its substrate and designed a sialic acid paralog that was designed to bind with residues that are more conserved across different known sequences of influenza.

I found a recent review with some others listed here [2]. It has a nice overview of the process too!

Forgot to answer your other questions. I'm not up to date on the structure-based drug design workflow but back when I did similar work (5 years ago) there definitely were rudimentary systems for generating molecules and docking them. It may have improved significantly since then. But I would probably characterize it as a problem it itself for sure.

Your other question is a VERY good one. Proteins usually fold into whatever may be most favorable based on the sequence, and it mostly stays consistent once it does. However, they are very flexible and structures solved by EM or x-ray crystallography are like a photograph of bird flapping its wings: you will see the wings in a position, and if you happen to have a few birds in the photograph, you might get a sense of where those wings can move to, but it's never going to be perfect. But like wings, proteins usually still have a limited amount of movement. There are other types that are much harder to understand that have less structure, but globular proteins that bind to drugs like this are usually pretty well-predicted by the snapshots we can get.

[1] https://pubmed.ncbi.nlm.nih.gov/10480735/

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6601033/

[ricksunny]

For docking: Autodock Vina (from Scripps) is the most frequently cited docking software in the biomedical research literature. It's open source.

Researchers use docking software to run libraries of existing drugs as well as design never-seen-before drugs out of the enzyme protein's active-site pocket.

These operations have been performed extensively this year (by research groups all over the world) on covid's main protease enzyme as well as the spike-ACE2 interface, for example.

[ricksunny]

[followup] and so frankly, it's hard to imagine a world where drug discovery isn't enormously sped up by an automated protein-folding approach which docking software like Autodock Vina require to be run. I know that not all of the pharma industry agrees with this assertion however...: https://twitter.com/michael_gilman/status/133375535280704307...

My take: Since 0.1% of proteins whose amino acids have been sequenced have ever seen a crystal structure (i.e. the folded model) generated of them. an automated approach to 3D model generation 1) will have enormous implications on drug development, and 2) will most likely come from a new and very different generation of drug developers, who don't have a lot in common with the generation that produced the tweet pasted above.

[natechols]

My take is exactly the opposite: since 3D structures of proteins alone are almost never the bottleneck in drug discovery, this won't actually change anything. Knowing how the drug is going to bind, and knowing how it'll behave in vivo, are not something you can predict from deep sequencing data.

[SomeoneFromCA]

Some proteins require help to fold properly (because there are more than one enrgetically favored conformations). The helping enzymes are called chaperons.

[fabian2k]

A known 3D structure for your target protein is very useful to improve molecules that bind to it, but we can't yet determine which molecules bind to a target without actually trying it experimentally. Of course there are methods to predict binding, but they not reliable enough and in the end the drug candidates are discovered by throwing a lot of molecules at a specific target or assay.

Once you have a candidate, it is very useful to determine the structure of the protein together with the drug candidate. There you can see how it binds, and can make some educated guesses on how to change the molecule to make it bind better, or to improve other aspects without making it bind worse.

Determing the protein fold from scratch without experimental data is impressive, but it doesn't have an immediate use for drug development. But a few steps further and it could certainly help if you can also predict which molecules bind to the protein structure.

I would strongly recommend the following blog post from Derek Lowe to put the importance of this into context for drug development:

https://blogs.sciencemag.org/pipeline/archives/2020/12/01/th...

[natechols]

The other point missing in most of these discussions is we already know how most drug targets fold, even if we don't know the exact structure at atomic detail. It's everything else about their structure, dynamics, and in vivo function that remains very difficult. The real promise in AlphaFold IMHO isn't that we can magically solve protein structures without experiments (most really interesting structures are beyond what it can do anyway), but the more general application of these AI methods to human health.

[ramraj07]

For the most part, people don't even try to make drugs for proteins that don't have structures, so that's one. As was mentioned in the anecdote, even as it exists AlphaFold can be an extremely powerful ally in structure elucidation in combination with lab methods. So this will help us increase the targetable list of proteins, especially the tricky ones that were harder to crystallize.

Once Alpha Fold or future programs get better with side chain modeling (not even for the entire protein just some parts), they will also allow complete computer based design of new antibodies against any target of choice (this is currently only possible through experiments and the technologies that allow this are all heavily patented and proprietary).

Variations of AlphaFold will also be significantly useful in research in general, potentially becoming fundamental enough that every project working with proteins might reach out to this tool like they reach out to to say mass spectrometry or flow cytometry.