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Folding@Home - Yet Another Distributed Client
braind writes: "The Stanford group has developed a new way to simulate protein folding ("distributed dynamics") which should remove the previous barriers to simulating protein folding. However, this method is extremely computationally demanding and we need your help.
You can read more on the site." It's interesting seeing all these projects coming out - just a reminder that distributed is still around and we can always use more on our team. *grin* [addendum from timothy:] Note that the SDK used for this project was discussed here a few days ago, so you can even roll -- err, fold -- your own.
to bring back the forum2000.org IMHO.
Take the cheese to sickbay, the doctor should see it as soon as possible - B'Elanna Torres, "Learning Curve"
Whilst projects like distributed.net and Seti@Home have clocked up shocking amounts of processor time (410497.11 years on Seti@Home), they're still running on the 'cool factor' of having your machine break codes or search for aliens.
Sites such as ProcessTree, and others, have been talking of paying for your computer time with micropayments, but so far nothing seems to have got off the ground.
Presumably with the added incentive of cash, the number of computers taking part will rocket. Does anyone have any firm information on the progress of these schemes?
(1) proteins are not static structures, they tend to change conformations in response to stimuli like binding to a ligand, or changes in the electrostatic microenvironment around them.
(2) many proteins don't like to fold in isolation, they require the presence of other proteins that they naturally interact with.
(3) protein sequence is linear (so-called primary structure); while local structural details may be predictable with some reliability (the so-called secondary structure, things like alpha helices and beta sheets), ultimately it is the final 3D fold with long range interactions (tertiary and higher structures) that form the final structure. You can imagine that the longer the protein, the harder it is to fold, due to the increased number of potential tertiary interactions.
determination of the structure of a protein, and even relatively large protein complexes is not as technically challenging as it used to be for biophysicists these days. Tom Steitz's group at Yale has managed to crystalize and solve the structure of the large ribosomal subunit (a **HUGE** molecule as far as the average biological molecular complex goes) at 2.4 angstrom resolution, which in itself is a monumental feat. I would not be surprised if Steitz is in contention for the Nobel prize for this work.
The holy grail is eventually being able to reverse engineer a protein or ligand that is able to bind to part of a particular protein, using rational design. This is much harder than solving a structure. Pharmaceutical companies would love to be able to design this type of molecule for use as designer drugs, since it would take away much of the cost of R&D through trial and error. Big companies such as Merck basically screen for drugs the way Thomas Edison used to test materials; by having a warehouse full of stuff and testing it all.
That being said, it's still a cool project :)
NO CARRIER
I like protien folding, but when I downloaded the client there was no source code, just a binary. How do I know I'm not running a trojan, or am I too paranoid.
Does anyone know exactly what models they will be using? Because there are only a few ways to actually go about this:
1 - Use a known protein structure that is similar to the one under study, but silghtly different. You can also look for common motifs in a structure / sequence to compare the two. Basically you look at the sequences, and say, "Hey, those two proteins have similar sequences, so they probably look the same too."
2 - Good old ab initio methods where you reduce the conformational energy to the optimal folding pattern. This is basically looking only at the sequence and saying "If I were a protein, what would I look like."
Both are relatively time consuming, but I'm not sure how suited distribution is to this task. The first method requires a great deal of database lookups, and the second requires a lot of computing power under the hood. With distribution, you don't have the database backend to work with, so it must be the brute force method. But I have yet to see any studies where ab initio have been anywhere near a 95% level of accuracy (compared to x-ray crystal structures). The best I've seen is around 75%. This isn't quite as helpful as it might sound. You can get some good results and working models this way, but you can't do a great deal with drug design with an inaccurate model.
They had links to the papers citing their algorithms, but they links were not yet active... If they have a better way to do this, I'll be quite impressed, but for now, I think that a machine like IBM's Blue Gene has a better chance.
And neither of these methods really takes into account post-translational modifications, phosphorylations, cleavage, activation, etc... (basically all the extra stuff your cells do to proteins before they are "activated").
"If we knew what we were doing, it wouldn't be called research." - Einstein
A few years ago I worked in computational chemistry for a pharmaceutical company. Determining the conformation of a molecule is a *hard* problem. We're dealing with quantum mechanics (QM) rather than classical mechanics and many-body QM problems are notoriously difficult. For example if you have just *two* particles the space of possible configurations is 6 dimensional (in this simple example you can use symmetry to simplify things). The wavefunction is a function on a six dimensional space. For a protein you might want to deal with hundreds or thousands of nuclei and many more electrons. You might be determining a wavefunction on a 100,000 dimensional space. Let me give a taste of how big that is. Imagine we discretise this space so that we only have *ten* steps along each dimension. Then we have 1 with 100,000 zeros after it discrete points in the space. That's *big*. So clearly any attempt to solve this problem on a classical (ie. non-quantum) computer is a gross approximation. I have serious doubts about our ability to solve this problem today - even with a billionfold increase in the power of computers. When I worked in this computational chemistry department all of the molecular modelling packages had parameters you could tune. A computational chemist would run a simulation. If the result wasn't to their taste they'd tweak the parameters and run it again. Then they'd run it a few more times. As X-ray data came in they'd fine tune their parameters to make their simulated model match. Eventually they'd give a seminar showing how their simulation matched the real results - when in fact all they'd done is find the set of simulation parameters that matched reality. These parameters were purely hacks tweaked to make things look like the experimental results. They had no a priori worth. If you took these tweaked parameters and tried them on the next simulation with a different parameter guess what! They wouldn't work. And this was for relatively simple biologicaly active compounds - not entire proteins. This is a problem that grows exponentially with the number of bodies. Thankfully some of these people realised that what they were doing was no better than Voodoo. So I hope someone can convince me that there have been big improvements before we collectively build the world's biggest waster of CPU time. Keep your cycles for SETI@home - at least then they might be useful.
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-- SIGFPE
From their site:
Presumably if you volunteer to port to system x they'll have to let you see the source code. They might even let you see it if you ask nicely for all I know.
As for SETI, I don't know if their code is available at all (I think not --at least officially); but I know they do not want any unofficial versions around and that they've even refused assistance to produce versions optimized for the 3DNow extensions in AMD chips (none exist now AFAIK).
> I hope they come out with a version that can work without the screensaver.
yea, you're not alone and we do have one (for linux and windows): check out the Folding@home site and go to the download page, sign up, and then download.
Their windows client sucks. I installed it on 3 different machines, 2 of them locked up after 10 minutes and required a reboot, and the 3rd one rebooted itself after 15 minutes. Yes, I know windows normally does that, but it does it consitently with this screensaver thingy installed.
Need Free Juniper/NetScreen Support? JuniperForum
FOR RELEASE: 27 SEPTEMBER 2000 AT 14:00 ET US University of Chicago Medical Center http://www.medcenter.uchicago.edu/ Prions, abnormally folded proteins associated with several bizarre human diseases, may hold the key to a major mystery in evolution: how survival skills that require multiple genetic changes arise all at once when each genetic change by itself would be unsuccessful and even harmful. In a study in the September 28, 2000, issue of Nature researchers at the Howard Hughes Institute at the University of Chicago describe a prion-dependent mechanism that seems perfectly suited to solving this dilemma, at least for yeast. It allows yeast to stockpile an arsenal of genetic variation and then release it to express a host of novel characteristics, including the ability to grow well in altered environments. "We found that a heritable genetic element based on protein folding, not encoded in DNA or RNA, allows yeast to acquire many silent changes in their genome and suddenly reveal them," said Susan Lindquist, Ph.D., professor of molecular genetics and cell biology at the University of Chicago, Howard Hughes Investigator and principal author of the study. There are thousands of proteins in every cell and each one has to fold into just the right shape in order to function. In prion diseases, which include mad cow disease and Creutzfeldt-Jakob disease, a normal cell protein, PrP, assumes an abnormal shape. Mis-folded proteins are usually just degraded, but the prion protein causes other PrP proteins to mis-fold, too, creating a protein-folding chain reaction. Thus, they act as infectious agents. As more and more of the proteins fold into the prion shape, they form inactive aggregates which lead to dysfunction and disease. A few years ago geneticists made the startling discovery that yeast, the organism found in bread and beer, has prions, too. Yeast prions are unrelated to the mammalian prions, and don't harm humans or yeast. They do, however, have the unusual property of mis-folding in the same peculiar way and spreading their change in shape from one protein to another. Mother cells pass these proteins to their daughters, so the change, once it occurs, is inherited from generation to generation. Because yeast prions act much like mammalian prions and are easier to study, scientists hope they will offer clues about how these mis-folding chain reactions get started and how they might be stopped. But the real puzzle is why these things exist in yeast cells in the first place. University of Chicago researchers appear to have found the answer, and it has broad and unexpected implications: the yeast prion seems to play an adaptive role and may greatly influence evolutionary processes. The prion protein they studied is called Sup35. It normally ensures that yeast faithfully translate the genetic code. Specifically, Sup35 recognizes special signals that tell the entire protein production machinery to stop when it is supposed to stop. Sup35 doesn't function in its prion state. As a result, the protein production machinery runs right through the "stop signs." This means that usually silent regions of the genetic code are suddenly expressed. Because these regions are normally not expressed, they don't face selective pressures that prevent mutations from accumulating. The prion therefore uncovers, all at once, a wealth of previously hidden genetic mutations and creates a completely new set of growth properties. Suddenly cells change the kind of food they eat, change their resistance to antibiotics and even grow colonies with completely different shapes. In some cases the prion may simply cause the protein production machinery to read through the "stop sign" at the end of a normal gene. This would create a protein whose function is altered by the addition of a new tail. In other cases the cell machinery may produce a completely new protein from a mutated gene that is not ordinarily translated because it contains a stop signal. The key to its effect is the stable inheritance of the prion state and the normal state. A spontaneous switch between the two states occurs approximately once in a million generations. Because a yeast colony produces a new generation every two hours, in a short time a colony will produce some members that have switched their state. "It's an 'all or nothing' switch, with the changes immediately inherited by all the progeny," said Lindquist. "But because the cell maintains the ability to switch back, the prion switch allows cells to occupy a new niche without losing their capacity to occupy the old." The researchers exposed seven distinct genetic strains of yeast in their prion and non-prion states to 150 different growth conditions. The prion-positive state had a substantial effect on the growth of the yeast in nearly half of the conditions tested. In more than 25 percent of these cases its effects were positive. The incredible diversity of the advantages conveyed by the prions indicated that each strain had different novel genes turned on in its prion-positive state. This prion switch is conserved in yeast across very distantly related genetic strains. Though the switch may have evolved as an accidental consequence of a shape change in an unimportant functioning part of the Sup35, its conservation suggests an evolutionary advantage. "It may be that the prion switch offers yeast a way to respond to commonly fluctuating environments," said Lindquist. "During its evolution S. cerevisiae (brewers' yeast) must have met with such erratic environments that it needed to maintain a global mechanism for exploiting genome-wide variation." By providing yeast with a way to respond to fluctuating environments, the prion switch may offer a significant evolutionary advantage. "Though we haven't shown it yet, selective pressure should operate to 'fix' the advantageous genes, which could then be read and translated at all times," said Lindquist. Prion mechanisms could be more common than previously suspected and exert an important influence on the rates and mechanisms of evolutionary change. "We need to expand our understanding of inheritance," said Lindquist. "It involves much more than a certain nucleic acid sequence of DNA." Susan L. Lindquist is the Albert D. Lasker Professor of Medical Sciences, Department of Molecular Genetics & Cell Biology at the University of Chicago and a Howard Hughes Medical Institute Investigator. Her co-author is Heather L. True, a Fellow in the Department of Molecular Genetics & Cell Biology at the University of Chicago. http://www.eurekalert.org/releases/ucmc-pmp092500. html
Seastead this.