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AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

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Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#61

Post by ghostpepper »

derefr wrote: post #37
fabian2k wrote: post #26

Earlier quoted context omitted.

Protein folding is a big and important problem, so this is certainly big news if it works as well as it seems. But I wouldn't assume that this changes everything, we can already determine how proteins fold by experimental work. The disadvantage is that this is a lot of work, though the methods there also improved a lot. One question is how robust the predictions are that DeepMind produces. I would also assume that ri…

We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predict…

This does indeed sound like a game changer then, if true

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#66

Post by jcims »

empiricus wrote: post #17

I am actually scared. This plus CRISPR means real nanotechnology is within reach.

I think this is the interesting part because there aren't going to be the same regulatory hurdles for using ribosomes to manufacture technology as there are for medicines. Synthetic organelles that weave fibers, build metamaterials, etc could lead to pretty magical advances in our capability.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#67

Post by Rochus »

TrackerFF wrote: post #16

What are the immediate real-world applications of this? Just asking, because I have very little knowledge in this area.

> What are the immediate real-world applications of this?

A protein is actually a linear sequence of amino acids, but in a cell this sequence has a three-dimensional arrangement like a clew of thread. The arrangement is not random, but dependent on the specific composition of the sequence (i.e. selection and order of amino acids) and some other factors. To understand the function of a protein, we need to know this three-dimensional arrangement (i.e. structure). Up to now the structure determination process was mostly manual, complex, time-consuming (several months up to more than a year) and error prone. If structure determination by DNN is reliable, this is a big win for life science. There are still a lot of problems open: e.g. the structure is not constant over time but there are "moving parts" in the structure which are important for its function.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#68

Post by asdfasgasdgasdg »

woeirua wrote: post #36

This is a big step forward, but the outstanding question as far as to whether or not this is useful for evaluating novel proteins, is going to be how good is the confidence metric at telling the user to trust or not trust the results. You can see from their examples, that AlphaFold is very good but not perfect. I imagine for some proteins it will still give misleading or erroneous results and if you can’t tell when t…

> the outstanding question as far as to whether or not this is useful for evaluating novel proteins

That is not an outstanding question. The test on which DeepMind scored high marks is a test of how well the algorithm folds novel proteins -- proteins whose ground-truth structure has not yet been published.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#69

Post by dekhn »

harperlee wrote: post #12

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

It seems unlikely there will be any large changes in life from solving protein folding. Knowing the structure of a protein (or really, its dynamics) is useful for identifying drugs that bind, but the real bottlenecks n drug discovery and biotech are elsewhere.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#70

Post by sgt101 »

echelon wrote: post #20

This sounds wonderful and frightening. On the one hand, now we can engineer drugs at light speed. But wasn't protein folding supposed to be NP-hard? Can deep learning find the cracks in P vs NP? Perhaps making clever guesses at prime factors because it learned some weird structural fact that has eluded mathematicians. If we break crypto, there goes the modern world. Banks, bitcoin, privacy, Internet, the whole sheban…

I think that this is a heuristic "near optimal" method rather than an exact analytic method (I have little to no idea of what that would be in protein folding). A domain I do understand a bit which is np-hard is the travelling sales man. Computing an exact solution is unrealistic, but doing heuristic searches that get you to 99% of the optimal 99% of the time is relatively doable.

But - you don't know that you are 1% from the solution... even if you are pretty confident that you are. It's quite possible (unlikely) that you are way off the optimal, but if you have a decent solution that's ok.

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