My statement is correct; both AF papers were published in nature, and both won casp. AF3 is superior to AF2 which means if adams wrote another paper, it would be on increasingly less interesting fine details.
To be clear, I don't think anyone distrusts the benchmarking work nor even the reported architecture, but also no one should need to operate on faith when it comes to work that presents itself as groundbreaking. Probably the first thing everyone did when they tested the model was run a sequence w/ a known cryo structure, but that's insufficient for how deepmind knows researchers will use the model.
> Also, for work of the highest art (of which AF3 is an example), publication in nature really is the fundamental unit of scientific currency because it ensures all their competitors will get hyped up and work extra-hard to disprove it.
IDK about disproving it, again nobody is distrusting the work, but let's also not pretend that a prestige journal is necessary to promote AF3. They could publish in the Columbia Undergraduate Science Journal and get the same amount of press. And to be clear the controversy has largely center on Nature for allowing AF3 to get away with more than they would most other projects, and the wasted time and effort it's taking to reimplement the work so people can add to it. FWIW an author did state that they're attempting to release the code but that's not like a binding vow.
Finally, AF3 strictly speaking didn't win CASP (it almost certainly would) but again this isn't necessarily the point when people talk about validation. The diffusion process does seem to result in notable edge cases (most obviously in IDPs and IDRs but also non-existent self-interactions), it's not a straight improvement in that respect.
I also worked with the same people (and share most of the same biases) and that paper is about as close to a ringing endorsement of AlphaFold as you'll get.
The DeepMind team was essentially forced to publish and release an earlier iteration of AlphaFold after the Rosetta team effectively duplicated their work and published a paper about it in Science. Meanwhile, the Rosetta team just published a similar work about co-folding ligands and proteins in Science a few weeks ago. These are hardly the only teams working in this space - I would expect progress to be very fast in the next few years.
How much has changed- I talked with David Baker at CASP around 2003 and he said at the time, while Rosetta was the best modeller, every time they updated its models with newly determined structures, its predictions got worse :)
It's kind of amazing in retrospect that it was possible to (occasionally) produce very good predictions 20 years ago with at least an order of magnitude smaller training set. I'm very curious whether DeepMind has tried trimming the inputs back to an earlier cutoff point and re-training their models - assuming the same computing technologies were available, how well would their methods have worked a decade or two ago? Was there an inflection point somewhere?
This must be some kind of sharp generational divide, right? I'm over 40 and I can't think of anything that has made me feel as old as I do reading the "green message shame" discourse.
Seconded, and it touches on the key themes he developed later. I love how a throwaway plot element became a central part of an unrelated novel later, like he had more ideas than he had time to fully explain.
I think it's also one of the best descriptions of living at the onset of massive, disruptive technological changes, and how disorienting (and occasionally terrifying) this would feel. The fundamental problem with that book, for me, is that the main protagonist is (deliberately) an utterly loathsome individual, who somehow ends up as a good guy but doesn't seem to do very much learning or self-reflection.
> Not evil for the sake of evil, but rather reasoned decisions with terrible prices
The Emergents and their system are pretty clearly just evil, and there's never any indication given that they actually care about those terrible prices, or even reflect on them for long. Vinge is very good at channeling the Orwellian language that regimes like these use, but I didn't find his intent at all ambiguous.
The really compelling and ambiguous character in that book is [redacted spoiler], who really does grapple with the moral implications of his decisions, but ultimately chooses the not-evil path. Personally I think this also highlight's Vinge's biggest flaw as an author for me, which is that in all of his books, the most fully realized and believable protagonist is a scheming megalomaniac, with second place going to the abusive misanthrope of Rainbows End, and third to the prickly settlement leader in Marooned in Realtime. All of the more sympathetic characters feel like empty vessels that just react to the plot.
The goal of protein folding simulations like Folding@Home is not to predict 3D structures - it's to understand how folding actually works, and why it sometimes doesn't work. When FAH came out it was already very obvious that there were good computational shortcuts to predicting the end state (the Rosetta approach), but those don't tell you very much about the physical process. Different questions call for different approaches.
7) Mitochondria (and chloroplasts) have double membranes, exactly like they would if they were smaller cells engulfed by the host cell.
8) There are multiple examples of ongoing endosymbiosis where the engulfed cell remains a true symbiont, not yet an organelle. Paramecium bursaria is my favorite - a ciliated protozoan with blue-green algae symbionts.
Bonus: there is evidence for secondary and tertiary endosymbiosis too.
