Comment by sytelus | Hacker News Reader

Comment by sytelus | Hacker News Reader

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This is a fantastic achievement. About 8 years ago I'd an interesting conversation with a friend on how any material with arbitrary desired properties can be constructed using proteins. You can build something as hard as turtle's shell or as soft as jellyfish. You can build liquids that dissolves plastics or you can build the most flexible fibers known in the universe. One way to think about proteins is generalized parametrized materials. If you knew the inverse function of properties -> protein structure, it would change the world far beyond dollar value of the invention. The DNA mechanism is evolution's best effort yet to build exactly that. This was such a beautiful insight that I remember rushing to Amazon and getting few books to understand basics of protein folding. The subject has some extremely beautiful foundational simplicity that is easy to understand but it quickly gets complex enough that it would be hard to navigate without interdisciplinary mind melds. With this new progress, hope is that protein engineering through AI would get huge boost in community attention and more accelerated progress!

This is...rather exaggerated. Scores based on residue distance-matrices have been around for a long time and don't perform especially well, so it was surprising to me that the method described on the webpage would trounce the competition. Then I read the abstract....

While the description of method (page 11 from this paper: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf) is pretty vague, they make clear that much of their scoring and structure refinement uses the scoring function from Rosetta. That's a tell that the neural-network part of the method probably isn't sufficient to pick out good structures. The AI, in this case, is generating fragments (which is not exceptionally different from what Rosetta already does), and doing a beta-carbon-distance score.

Basically, the machine-learning part is generating protein fragments and quickly stack-ranking structures that are created by monte carlo search. Everything else is done by a much more complicated physical model that has little/nothing to do with AI.

How far are we from this technology and what can we do to get there?

Forget clunky mechanical robots, Boston Dynamics can just engineer fleshy bullet proof self healing skin system. Think skinned dogs.

It is our job at Serotiny. We take plain-language requests for proteins with novel functions (not just binds more tightly, or produces this enzymatic product over that one), and we use the catalog of existing natural protein components to build you a protein that has those desired capabilities.

Imagine any natural input that life can read (light, heat, glucose levels, hormone levels, force, etc.) and any natural output that life can produce (temp, colors, fluorescence, electrical impulse, etc.). For many of those options, we can design a novel protein that achieves a linkage between that I/O.

However, our approach to the problem is very much not like AlphaFolds - we don't try to scan the 20^600 space by changing individual amino acids, but rather we don't worry about folding or structure (too much) and instead play around with discreet functional modules that already exist in nature. Our approach is a bit more sociological than it is a simulation of physics/chemistry. But it works.

Optogenetics tools, CARs, SynNotches, BaseEditors are all curious examples, and there are many more coming online right now.

> How far are we from this technology and what can we do to get there?

Less long than you think; more funding ;)

I know that's a bit cheeky, but the ability to understand and properly manipulate the chemistry of life (not just the D/RNA coding) is finally coming online. Efforts such as the OP's are along those lines.

Many still do not understand (myself included) how revolutionary CRISPR and other such techniques are. The world of fine-tuned, genetically based, auto-organizing chemistry is within sight. And man, it is going to weird.

Academician Prokhor Zakharov was wrong, we will be able to put an elephant's nose on a giraffe [0].

[0] https://en.wikiquote.org/wiki/Sid_Meier%27s_Alpha_Centauri#U...

Or 'Skin jobs'. Blade runner, here we come!

Any particularly good books you can recommend that you came across on your Amazon binge?

I can recommend Engines of Creation:

1.0 summary: http://web.mit.edu/cortiz/www/3.052/3.052CourseReader/3_Engi...

1.0: http://e-drexler.com/d/06/00/EOC/EOC_Table_of_Contents.html

2.0: https://web.archive.org/web/20140810022659/http://www1.appst...

I highly recommend Engines 2.0 rather than the original. Drexler's conception of nanobots before he had done the heavy lifting of working out the designs that showed up in Nanosystems probably aren't very practical compared to more factory assembly line structures. Thermal motion is just so much easier to deal with in an enclosed tube than at the end of a wobbly arm.

Essential Cell Biology is pretty good one to start (Ch 2-7): https://www.amazon.com/Essential-Cell-Biology-Bruce-Alberts/.... There are numerous books like Machinery of Life that don't go in to enough details. Most books specifically on subject of protein structure, dynamics and folding are advanced textbooks like Introduction to Protein Structure by Branden and Tooze. My experience has been that following all the details is very difficult without taking formal classes in these subjects and there is an immense amount of details that is probably not relevant to our purpose of computational prediction. Hope is that someone soon writes Protein Folding for Programmers :).

Machinery of Life is a fantastic introduction to an intuition for what proteins and the mechanics of their scale.

https://www.amazon.com/Machinery-Life-David-S-Goodsell/dp/03...

One of the big reasons I love this place ... always find great book recommendations. Thanks for that and the insight!

Aka, Polymer Science with a different name.

Polymers are a simple monomer repeated many times. Proteins can be arbitrary non-repeating sequences. These are not the same.

Not so.

There are of course, 'uniform' polymers made from a single repeating subunit. However, natural non-uniform ones like proteins are also polymers, as are synthetic ones with two repeating units (EVA, for example).

From wikipedia:

> Polymers that contain only a single type of repeat unit are known as homopolymers, while polymers containing two or more types of repeat units are known as copolymers.

Trivially, there are protein homopolymers like polyglycine. They are definitely proteins.

This is technically correct, but it doesn't really capture the spirit of what I was saying - an arbitrary long keyboard mash of the 20 bases is a protein, most things called "copolymers" are substantially simpler. Perhaps it is useful to differentiate between (simple) polymers and (arbitrarily complex polymers called) proteins, perhaps it is not. I can see why if you dealt with polyglycine a lot it would be annoying :)

You are quite right that the properties of most proteins are very different to that of even copolymers.

In fact, simple structural proteins (like keratin, collagen, etc) are much more like synthetic homopolymers than the complex nanomachines such as ATP-synthase or the proteosome.

Proteins are polymers, just not homopolymers (those are simple repeats).

Not really, this is more of a route to nanotechnology via biology. It’s more than just stringing together amino acids.

This is probably be the next step in human societies, small scale (both for ~production rate and size of ~tooling) natural hacking. This would avoid large chemicals shops that may or may not process waste properly and also bring back people into a closer relationship to ~nature in a way, hopefully saving energy on the way. You can't brand and market proteins much.

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