AI Revolutionized Protein Science, but Didn't End It

AI Revolutionized Protein Science, but Didn't End It
quantamagazine.org

It mentions multiple times that ~”the protein folding problem is solved” as well as multiple instances of ~”but there are limitations to this technique and it is often missing crucial details”.

It really is difficult to conceptualize these highly nonlinear problem spaces, like protein folding, until you attempt to work with them.

Many in software development have an intuitive understanding of the difficulty evidenced in the community’s ~“the last 10% took 100% of the time” meme.

Even in a nonlinear problem spaces you have “trivial” solutions.

Terry Tao famously coauthored a paper finding arithmetic progressions for generating sequences of primes.[1] The sequences found are “trivial” in terms of “solving the prime sequence problem” in that they are sparse, the sequences are finite, and progressions lack a method of find more.

These machine learning tools are by design approximation engines. I’m unsure of any results that prove one way or the other that it is possible to pass a bound of approximation that provides exact solutions. (think, an approximate solution that only fails to provide exact solutions for solutions that are trivial using a different method, I think a lot of work I p-adics is motivated similarly)

I feel these machine learning techniques are expanding the definition of “trivial solutions” to include those capable of being solved by their convoluted methods (back prop, etc). Since this new subset of the space that can be labeled “solved” appear more complex than known trivial solutions people assume the whole space must be known, and this is where the difficult conceptualization rears its influence.

Protein folding is still an unsolved problem, and I’m dubious of the notion machine learning will ever solve it, but hopefully we get some helpful science out of it.

[1] https://en.m.wikipedia.org/w/index.php?title=Green%E2%80%93T...

> The “good news is that when AlphaFold thinks that it’s right, it often is very right,” Adams said. “When it thinks it’s not right, it generally isn’t.” However, in about 10% of the instances in which AlphaFold2 was “very confident” about its prediction (a score of at least 90 out of 100 on the confidence scale), it shouldn’t have been, he reported: The predictions didn’t match what was seen experimentally.

> That the AI system seems to have some self-skepticism may inspire an overreliance on its conclusions. Most biologists see AlphaFold2 for what it is: a prediction tool. But others are taking it too far. Some cell biologists and biochemists who used to work with structural biologists have replaced them with AlphaFold2 — and take its predictions as truth. Sometimes scientists publish papers featuring protein structures that, to any structural biologist, are obviously incorrect, Perrakis said. “And they say: ‘Well, that’s the AlphaFold structure.’” ...

> Jones has heard of scientists struggling to get funding to determine structures computationally. “The general perception is that DeepMind did it, you know, and why are you still doing it?” Jones said. But that work is still necessary, he argues, because AlphaFold2 is fallible.

> “There are very large gaps,” Jones said. “There are things that it can’t do quite clearly.”

Crazy how we tend to wave away even errors rates of 1% or less. One in a thousand is a lot.

I was trying to point to a situation where you have a clear problem: a generating function for the prime number sequence; and a solution that identifies a small subset of the intended sequence without addressing, or even informing in any substantial way, the full breadth of the original problem.

> At the time of writing the longest known arithmetic progression of primes is of length 23, and was found in 2004 by Markus Frind, Paul Underwood, and Paul Jobling: 56211383760397 + 44546738095860 · k; k = 0, 1, . . ., 22.'.

The triviality was overloaded to both imply that calculating this subset is trivial, it is a simple arithmetic progression, and that subset of the full prime number sequence is now trivial to produce.

In the same way that the Green-Tao theorem has yet to lead to a complete solution to the prime number sequence, I feel, the machine learning techniques will fail to lead to a complete solution to protein folding.