avogt27

Pedantic semantics gripe: DNA damage refers to actual damage to the DNA molecule (breakage of the sugar-phosphage backbone, loss of nucleosides, etc). DNA accumulates MUTATIONS over time, which lead to the loss of genetic fidelity.

Many cancers have unregulated DNA repair pathways, which is one of the mechanisms by which they can sustain proliferation without succumbing to apoptosis. Common chemotherapeutic targets are actually DNA repair factors that can both help kill the cells and sensitize them to radiation. It's well known in the DNA repair field that cells maintain rather delicate balance between carcinogenics and death by regulating repair. The vast majority of research into DNA repair is aimed at solving problems treating cancer, with some peripheral voices (albeit ones that garner more publicity) working on anti-aging applications. I personally wouldn't sign up for any of these start-up nonsense treatments; traditional scientific orthodoxy may be overly reductionist, move slowly, and lack imagination but good god does it beat all of these people that treat grand problems in biology like some sort of app you just need to take the right angle on to figure out.

Several crystal structures of the catalytic domain of the protein had already been determined. The DNA binding domain of the protein which AlphaFold predicted is a relatively common fold that probably could have been figured out using homology modeling, which was common 10+ years ago. Even the small molecule docking used pretty old school computational techniques, and all but one drug interacted with the predetermined structures. The analysis was indeed aided by AI in the form of AlphaFold, but my guess is it sped a couple things up rather than making them possible.

I agree that this is probably at least partially a motivation, but it seems like a losing strategy to me. AlphaFold is run by a private company, and falsely elevating the importance of its use in the paper could be used to fuel the argument that all this research needs to be privatized. Given the current situation, I hope people realize that the breakthrough in structure prediction is literally impossible without 70+ years data generated by publicly funded research. Most of the foundational work in deep learning guided structure prediction was also publicly funded, with Deep Mind getting in at the tail end of the race once it seemed like the problem could be brute forced by throwing enough resources at it.

I think my problem is that this is maybe the most minimal and mundane use of AlphaFold, but it is treated like one of the main points of the paper. The small molecules they tested were already known to inhibit this enzyme, the structural modeling done based on AlphaFold is a minute part of the story compared to the dozens of incredibly difficult experiments they did - it almost seems the sort of thing one of the reviewers suggested during the initial submission and was added after the first round of edits.

I can't tell you how many times I've sat through talks where someone (usually ill-equipped to really engage with the research) suggests that the speaker tries AlphaFold for this or that without a clear understanding of what sort of biological insight they're expecting. It's also a joke at this point how often grad students plug their protein into AlphaFold and spend several minutes giving a half-baked analysis of the result. There are absolutely places where structure prediction is revolutionizing things including drug discovery, but can we acknowledge the hype when we see it?

I'm very sorry for your loss, my aunt is also declining due to this disease. I think statistically everyone either goes through it or becomes a caretaker if they live long enough.

It's really a bummer to see this marketed as 'AI Discovers Something New'. The authors in the actual paper carried out an enormous amount of work, the vast majority of which is relatively standard biochemistry and cell biology - nothing to do with computational techniques. The AlphaFold3 analysis (the AI contribution) literally accounts for a few panels in a supplementary figure - it didn't even help guide their choice of small molecule inhibitors since those were already known. AlphaFold (among other related tools) is absolutely a game changer in structural biology and biophysics, but this is a pretty severe case of AI hype overshadowing the real value of the work.