Protein folding: Much more intricate than we thought (2017)

Re:Protein folding: Much more intricate than we thought (2017) by fabian2k on Thursday December 03, 2020 @08:38AM (#25288672)

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...

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Re:Protein folding: Much more intricate than we thought (2017) by natechols on Thursday December 03, 2020 @12:01PM (#25291124)

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.

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