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Comment by gaius | Hacker News Reader

I think (and I discovered this far too late :-( ) that the most interesting programming is done by people who aren't really programmers. People who apply software to knowledge domains. A biochemist can learn Python a hell of a lot more quickly than a programmer can learn about protein folding, for example. A friend of mine is an astrophysicist and writes programs to model stellar events (then goes and checks his results with uber-telescopes). Me, I can connect interfaces to apps to data, I do a pretty good job of it (tho' I say so myself!) but what am I really adding to the sum of human understanding? Hmmm.

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Well, I would argue that much of what has emerged on the Internet in the last 15 years has been novel.

And a biochemist can learn the basics of Python about as easily as a programmer can learn how to operate basic lab equipment. A biochemist could no more easily add to human knowledge about compiler optimization than a programmer is likely add anything to protein folding. If anything, I would say the opposite of what you claim is true -- programmers are more likely to be interested in leading-edge science as a hobby than the other way around.

Except that a biochemist learning Python to solve problems he/she has in protein folding research (or whatever) is a useful skill to get things done in his/her field. A programmer learning about basic lab equipment probably isn't going to help too much in developing software (although it might help him/her get more attractive dates).

And protein folding is much more useful to humanity than compiler optimization. Does anyone even work on compiler optimization anymore?

As someone who has published research on protein folding, trust me: compiler optimization is about a thousand times more useful.

Protein folding is the biochemical equivalent of string theory. It's fun to talk about, but the practical applications are perpetually 20 years away.

Then we are in the same club. I've published research on global optimization algorithms for protein folding. I'm not a biochemist, but I understood that protein folding actually has immediate application in understanding disease and drug design.

As a computer scientist, I also understood that compiler optimization is a mature field with most of the low-hanging fruit already picked. So, I guess I'm confused and will ask respectfully what problems in compiler optimization make it a thousand times more useful than protein folding and associated medical problems?

"I'm not a biochemist, but I understood that protein folding actually has immediate application in understanding disease and drug design....what problems in compiler optimization make it a thousand times more useful than protein folding and associated medical problems?"

Short answer:

Compilers are used for real work, every day. Nobody is using protein structure prediction for anything practical, and they likely won't be for decades more. At this point, it's blue-sky research.

Long answer:

"Immediate application" is one of those bits of academic-speak that really means "is related to", but sounds better to grant review boards. While it's true that protein folding is important (after all, most biological processes are mediated by folded proteins), it's not true that protein structure prediction is important. It would be great if we could predict protein structures accurately, but we can't, and until we can, it's not a practically useful discipline.

Even the very best, crystallographically determined protein structures are barely sufficient to do rational drug design, and predicted structures don't come close to that level of quality. For example: we can sometimes (very rarely) predict very small (<150 residue) protein structures to within 1 angstrom RMSD of their experimentally determined shapes (i.e. >2 angstrom resolution, in the best case). However, the interactions important to drug binding, protein design, etc., don't start until a tenth of that (scales of ~0.1 angstrom).

Throw in the fact that the vast majority of proteins are much larger than 150 angstroms, and that we keep creating cheaper, faster, more automated ways of getting actual experimental information on structure, and the role of protein structure prediction looks increasingly marginalized. It's definitely a cool, fun problem -- just not a very practical one.

For whatever it's worth, my first papers were on applying the state-of-the-art method (you've heard of it...I think you're paraphrasing the lab's PR) for protein structure prediction to genome annotation. To call the approach useful was/is a stretch, and that's for a much easier application than drug design (in fact, we were trying to find a practical application for protein structure prediction, and it was the most likely thing we could think of!)

Wow. This is really enlightening; I thought that the computational method was going to open a new phase in disease treatment, but you seem to say here that the empirical method is on its way to making it useless. So the Pande group at Stanford is wasting their time. Interesting.

"So the Pande group at Stanford is wasting their time."

I wouldn't go quite that far. The research is definitely speculative, but lots of interesting things can come from speculative research. My point is that you don't do research into protein structure prediction with the intent of finding anything useful. It's basic science.

We can (and occasionally do) learn things from computer models of proteins. But the PR in this field has been seriously exaggerating the results of a few of the more prominent researchers. We're a long way from curing diseases or designing drugs with this stuff.

Is it the weakness of the modeling or the lack of computational horsepower that limits the research in this area? And would you mind linking to your papers?

That's a matter of debate. Some people think that the problem is search limited, others think that the current models are bad. In my opinion, the bulk of the evidence supports the latter conclusion.

Backchannel me, and I'll be happy to provide you with references to the papers I wrote/helped write. Most of them aren't open access, unfortunately.

Will do. And thanks for the info, you may have spared me a decade or so of wasted effort.

Oops...that third paragraph should read:

"the vast majority of proteins are much larger than 150 residues."

Thanks for the very detailed answer. Amazingly, I can follow the gist of it after over 10 years.

But, you still don't answer the question. What problems in compiler optimization are more important than problems in protein folding? You seems to indicate that protein folding is a basic science problem and not a "practically useful discipline". In fact, your statement "It would be great if we could predict protein structure accurately, but we can't, and until we can, it's not a practically useful discipline" says that the because the problem isn't solved, it's not important, but it will be important when it's solved. So, trying to solve the problem is important, no?

But, you don't say anything about compiler research, and specifically compiler optimization research and development, which you claimed is much more important. What specific areas in compiler optimization (or just in compiler design) are more important than protein structure prediction and modeling?

I did answer your question, but now you're asking a different one. I have no idea how "important" protein structure prediction will ultimately become; I just know that it's currently pretty useless, and getting worse.

My first comment was that compiler optimization is about a thousand times more useful than protein folding. I stand by that remark. However, at the beginning of my long answer, I mistakenly wrote that protein structure prediction is not important, when I had meant to write that it is not useful: protein folding is an important biological process, but protein structure prediction is not particularly useful, for the reasons I've mentioned.

It's not my place to say which area of research is more important. That's a subjective question, and the answer depends on your value system, your outlook, and your willingness to wait. Obviously, I think that compiler optimization is more useful, because compilers are actually in use today. In 100 years...who knows?

That said, I think you're laboring under the assumption that compiler optimization is a "mature" field, and that it is "solved" (and therefore less important), whereas protein folding is not "solved" (and therefore more important). The thing is, people have been doing protein folding research for at least fifty years -- it is a very mature field, and the low-hanging fruit has been picked. I think that a new researcher is equally likely to make significant gains in either field, but that the potential for practical impact is still much greater in compiler design.

I'm asking the same question, and you're not close to answering it. My question was "what problems in compiler optimization make it a thousand times more useful than protein folding and associated medical problems?" because you authoritatively stated that protein folding work (implicitly computational protein folding work) was much less important/useful (pick one) than compiler optimization work. You keep talking about protein folding research, but you don't say anything about compiler optimization. Was that comparison just an off-hand or self-deprecating remark about protein folding work? I'm not asking which area is more important. And, I know enough about both fields to not get lost in the technical details of your answers. So, I'll ask it again: what problems in compiler optimization make it a thousand times more useful than protein folding and associated medical problems?

If you want to know the most important/useful areas in compiler research, go ask a compiler researcher. I'm not a compiler researcher.

Compilers are used on a daily basis for real work; protein structure prediction is not. For this reason alone, research into compilers is more useful.

I didn't mean for people to focus on "protein folding", that was just an example of something that scientists do that's computationally intensive.

I find myself in my early 30s knowing an awful lot about database applications, but zero domain knowledge. I can go into any field and implement a spec, but I don't understand any of it. Someone wants a graph of this data in their application, I'll give them a great graph, but I look at it and it's just squiggly lines to me. I just feel like I'm missing something.

If it wasn't for the programmer creating languages that make massive, fast computation accessible to the biochemist, there would be no Python for the biochemist to use to do their research. The programmer is just once removed from the "important to humanity" aspects.

And if it wasn't for the programmers who get excited about languages and pioneer different uses of them, the biochemist would most likely never even know there is this tool available for them to use.

Can you show me how I can learn biochemistry in two days? I would be really interested!

No.

But if you haven't learned anything new about programming since two days after you started, you're doing it wrong.

Well I remember learning Python in one day. If I could do the same for biochemistry, it would be fantastic.

Maybe it is only the lack of hands on tutorials that is the problem, though. There are tomes of biochemistry books, but I suspect after reading them you are still not sure what to do.

You learned the rules of chess in one day, but how many chess games will you win with only one day of experience?

Coming from someone who has been involved in Biochemistry research and software development - I prefer software development. Here's why: in both fields, you get to explore and break new grounds, but you move much faster in software development.

To do anything interesting self-directed work in the life-sciences, you have to get a PhD and by then, your hands are still tied down to the government because you have to write grants to NIH. Unlike programming, where you hatch a idea, Google sample code-sources, dive into a hackthon and build a prototype after one evening of intense programming. Biochemistry takes months of patience, where you micropipette and shake test-tubes, use the PCR machines. It is hard to deciper the details, you can't step-through the codes of DNA (at least yet) or trace the memory stacks of the cells that you are studying. Coming from a programming background, I was so frustrated in Biochemistry.

Also you should know that the life sciences is slowly being transformed into a information sciences (Bioinformatics). There's a movement in the field called System Biology, that treats the body/a cell/macromolecular as a finite automata, the DNA the source code, and the protein products it makes the output. Sounds familiar?

Friends of mine in neuroscience, who do electrophysiology, love the instantaneous gratification of it: patch onto a cell. Watch/listen to it fire. Treat it with chemicals. Treat the cells innervating it with chemicals. Make them fire with external electrodes. Etc. Do this multiple times, over a period of months/years, until you have dissected some local circuitry properties. Pretty cool, if you get off on listening to neurons fire, day in and day out.

I think the key in science is to do something that is interesting and pleasurable in both the long and short term. My electrophysiologist friends like it day-to-day, and also like the long term agenda of it. Sounds like you liked the long-term agenda of biochemistry, but not the day-to-day.

Probably similar lessons apply to hacking. For me, I got out of it: I didn't like how I felt after looking at a glowing monitor for 12+ hours a day, week after week. I like programming this intensively when I feel like it, but not when I'm pressured to do so for some business goal.

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"I think the key in science is to do something that is interesting and pleasurable in both the long and short term."

I discovered that this (the hard way, after a decade buildng enterprise software) is true for software dev too. Now I do algorithm intensive work and there's more to do than I have time in this lifetime to do.

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