Proteins, Proteins Everywhere
science.org
science.org
This is a straight lie and the author should be embarrassed. There's not one scientist alive that can explain how proteins fold from their amino acid sequence. There's not one algorithm in existence that can predict it de novo.
I'm so tired of seeing this lie repeated over and over. AlphaFold did not gain us any new information on how proteins fold. It just got really good at modelling them from existing data where existing data is available. Which is great. But still ultimately limited by the same categorical limitations of homology modeling. It simply has not solved protein folding.
Yes it's amazing.
Yes it's worthy of lots of praise.
No, it did not solve protein folding, ESPECIALLY in the way that is described here.
As a biochemist, what I understood from this editorial was totally reasonable:
- scientists understand that electrostatic forces guide protein folding, and steric factors limit the conformations a protein can take
- scientists understand that the primary structure determines the secondary and tertiary structures of proteins
- scientists know that protein folding is aided by chaperone proteins
- scientists understand how thermodynamics and entropy affect protein folding
Despite all of the above, scientists cannot solve protein folding de novo. New tools now allow us to make better, cheaper predictions which are testable.
Sounds reasonable to me.
> seemed possible that the three-dimensional structure of a protein could be predicted based on the sequence of its amino acids.
That simply is not what AlphaFold (or any homology modeling) is doing, and certainly not:
>This is a breakthrough on two fronts. It solves a scientific problem that has been on the to-do list for 50 years. And just like Fermat’s Last Theorem or gravitational waves, scientists kept at it until it was done.
I don't know if we're getting hung up in semantic differences, but how would you describe homology modelling, or what AlphaFold is doing? Because for me, it takes as input an AA sequence and gives as output a 3D structure prediction.
AlphaFold isn't simulating folding at all. It's modeling what the final structure should look like based off other data, not knowledge of how a protein folds. That's what is frustrating me so much with this topic. It's being sold as "we can predict how proteins will fold" as opposed to "we can model a folded protein based on its sequence". There's a huge difference in our understanding of protein folding, between those two points.
There is a lot of value in learning more about folding (in the gerundive sense), but the focus has always been on finding the folded structure, because as you very well know, structure is function.
It seems the purist definition of solving protein folding means specifying the sequence of actions to arrive at the native structure for a given input sequence. This implies your solution is grounded in a physical reality. Such approaches (i.e MD simulations) have not been very successful to my knowledge and are computationally intractable for most proteins. Even if you are able to do this, how do you verify that your folding mechanism is in fact correct? We don't have ground truth experimental data at the temporal resolution one would want to verify such a prediction.
For now, the only practical framing of the protein folding problem is to predict a native structure given an input sequence. This is simply because we have the requisite data...
The OP is right to complain that there is no scientific knowledge gained by the mere act of modelling a poorly understood process, when the modelling is performed automatically and its results are also poorly understood. The goal of science is to gain knowledge of the processes in nature, not merely to predict their results. That's how science has advanced in the last few thousands of years and it's startling to see Nature and Science standing behind such a different idea of "science".
Ha! The reality is we have very little idea of about what any neural net AI is doing. Not no idea as provided the raw material these things build themselves out of, but then we throw billions of training points at it with the desired results, and a vast stew of polynomials begins to self organise into something that produces the results.
How those polynomials do it? We certainly don't know from first principles. If we alter compression in a car engine, or moderators in a nuclear reactor, or tension in prestressed concrete beam and an engineer will be able to make a fair stab at the result. Ask the people who built these vats of self organising what happens if they change the polynomials coefficients - and they will have no idea, whatsoever.
Any claim you know what AlphaFold "is doing" is simply fanciful. We've crossed Aruthor C Clark's threshold of "Any sufficiently advanced technology is indistinguishable from magic". It seemed so reasonable when he said it. What he, and we didn't foresee is how it would look if you were the ones who built the technology. "I've build a machine so complex I don't know how it works" crosses a line even SciFi authors had trouble imagining. And now we are confronting it as as our new reality.
I wonder if the all the comments on HN about how AI's don't really understand the subject, or don't fit some definition of "conscious", or can't really "think" are just a form a self denial. It really doesn't matter people. The reality is the only way people could tell it was GPT-3 posting to reddit wasn't because of any of those things. It was because no human could churn out so much error free high quality text indistinguishable from what a human would produce in such a short time.
We need, now more than ever, an "eli5" or "get up to speed in an hour" source for these very interesting fields where all the progress of the past century is easily visualized at a high level. I get the feeling much of academia shuts down such attempts to preserve their grip on knowledge and thus their authority.
There might be fields like that, but in biology, it really just is that we've accumulated so much knowledge over the past 150 years that I genuinely can't explain modern CRISPR research to a layperson until they've taken courses in molecular biology, microbiology, chemistry, genetics and biochemistry (or the equivalent amount of self-study).
The field is genuinely vast and complex, and there's no way to hide that complexity behind an abstraction without being misleading.
I just wanted to say that this is immensely under-appreciated, even by most people in the field. The more you learn, the more you realize that everything matters and every little bit of knowledge connects to a bunch of other knowledge.
Most people in undergraduate classes ask "what part of the textbook is important" or "what part of this is on the exam", or even "what part of this will I need to know for my job", but they have the wrong attitude altogether.
I think this really depends on what you mean by "explain". If you mean explain the precise mechanisms and systems involved in minute detail then sure, but that was never possible in a y subject. Arguably you wouldn't be able to explain with precision how modern CPUs work at the gate level and their manufacture, or how network congestion control works, or this topology to the same level of detail either.
Per Feynman, you should still be able to give pretty accessible abstract descriptions or analogies that are faithful enough, or arguably you don't understand the subject well enough yourself.
I disagree. You can easily liken CRISPR to a new innovative tool, like an auto service pit. That innovation lets you make changes and fixes to your car much more easily and more quickly than you could with the tools that came before it, but it obviously won't let you turn your Ford Pinto into an F1 racer, or get your Tesla to fly.
Of course, I agree all analogies have their limits, but I think you can almost always come up with an analogy that stays within the limits you're trying to describe so people don't get too fanciful an idea.
There’s no need to go all conspiracy theory on it. “People who like doing things like doing them”, i.e. if you like doing protein chemistry you probably like talking about it with other people who do, but aren’t really into, or don’t even know how to talk to people who aren’t into it. The closest to that you’re likely to get is writing grant applications, which are typically read by people who’s are generally knowledgeable about your domain.
The kind of translation you’re talking about is itself generally the domain of specialists, such as science journalists.
And as for “preserving their grip”: typically institutions are the opposite, doing their best to get the message out, all to better their reputation and improve probability of grants and donations. That’s what university press offices do.
"There is no royal road to geometry." - Reply given when the ruler Ptolemy I Soter asked Euclid if there was a shorter road to learning geometry than through Euclid's Elements.
What OP seems to be talking about is systems modelling, where you model the network of genes and proteins (genomics and proteomics) using tools partly derived from modelling of computer networks.
In other words, a bioinformatician is to a computer scientist what a computational biologist is to a data scientist.
Using biological systems to do computing is more in the realm of "biological computers".
However, they are also self-assembling, self-repairing, wet, massively parallel, etc. Artificial biological systems are in a sense programmable, but with the difficulties of a physical system that can be error-prone and extremely complex.
https://pubmed.ncbi.nlm.nih.gov/30237357/
Programmable protein circuits in living cells
Governments and their figurehead „scientists“ like Fauci have rightfully lost all our trust by contradicting the nonsense and lies they spew and moving the goalposts every other day, under the weak guise of „developing/new knowledge“.
You appear, with that formulation, to have missed the paragraph just above:
> And scientists have been attacked in new ways and with new methods by politicians who are exploiting social media and long-tested methods of indoctrination to undermine scientific authority on issues ranging from vaccines in the midst of a pandemic to the impending devastation of climate change
In a stub (unfortunately), the author claimed that the role of science is under political attack.
Very clearly (in some regions), there as been a devastating blow against civilization attempting through narratives for propaganda to make of science a fideistic sect, a doctrine - its opposite.