Originally posted by: FrankyJunior
I'm currently running Predictor and am doing well. I want to run the Aids project whenever they get around to making a Windows BOICN client but that won't happen until next year sometime.
I know Rosetta is made by the same people making the Aids project but how are they different? And how is Rosetta different from predictor?
Also any idea how the WUs compare? Say I average 100 per day in predictor, how many would I get per day with Rosetta if I switched?
Just curious.
From the Rosetta website
Comments from David Baker
My research group is involved both in fundamental methods development research and in trying to fight disease more directly. Most of the information on this site focuses on basic research, but I thought you might be interested in hearing about some of the disease related work we are doing that you will be contributing to at Rosetta@home.
Malaria: We are part of a collaborative project headed by Austin Burt at Imperial College in London that is one of the Gates Foundation "Grand Challenge Projects in Global Health". Malaria is caused by a parasite that spends part of its life cycle inside the mosquito, and is passed along to humans by mosquito bites. The idea behind the project is to make mosquitoes resistant to the parasite by eliminating genes required in the mosquito for the parasite to live. Our part of the project is to use our computer based design methods (ROSETTA) to engineer new enzymes that will specifically target and inactivate these genes.
Anthrax: We are using ROSETTA to help John Collier's research group at Harvard build models of anthrax toxin that should contribute to the development of treatments. You can read the abstract of a paper describing some of this work at
http://www.pnas.org/cgi/content/abstract/102/45/16409.
HIV: One of the reasons that HIV is such a deadly virus is that it has evolved to trick the immune system. We are collaborating with researchers in Seattle and at the NIH to try to develop a vaccine for HIV. Our role in this project is central--we are using ROSETTA to design small proteins that display the small number of critical regions of the HIV coat protein in a way that the immune system can easily recognize and generate antibodies to. Our goal is to create small stable protein vaccines that can be made very cheaply and shipped all over the world.
Other viruses: We have been collaborating with Pam Bjorkman's laboratory at Cal Tech to use the ROSETTA protein-protein docking methodology to build models of herpes simplex virus proteins in complex with human proteins.
Alzheimer's disease: Alzheimer's and many other diseases are likely to be caused by abberant protein folding in which proteins form large aggregated structures called amyloids rather than folding up into their normal biologically active states. A big advance was made recently by David Eisenberg's research group at UCLA in solving the first structure of an amyloid. We are collaborating with their research group to use the structure to predict which parts of proteins are likely to form amyloids, which will be a first step to blocking amyloid formation and hopefully disease.
Cancer: Cancer can be caused by mutations in key genes that disrupt normal cellular control processes. We are developing methods for cutting DNA at specific sites in the genome, and we will be targeting sites that are implicated in cancer. After these sites are cut, they should be repaired by the cell using a second, unmutated copy of the gene and the cell should no longer be cancerous. This is a very specific form of gene therapy that, if successful, will circumvent one the main objections to current gene therapy methods; namely, current methods insert the unmutated copy of a gene randomly into the genome, and if the insertion point happens to be near an oncogene, the gene therapy will cure one disease but cause another. Because our methods will target specific sites instead of random sites, they should avoid this pitfall.
Prostate Cancer: The androgen receptor (AR) binds testosterone and is responsible for normal male development. When the AR becomes hypersensitive to testosterone, prostate cancer is the result. The current treatment for prostate cancer, called "hormone therapy", involves lowering the amount of testosterone available (sometimes by castration). Many malignant tumors are resistant to this therapy, however, so we are applying our protein design methodology to find different ways to inhibit the AR and to treat prostate cancer. Specifically, we are trying to design proteins that will disable the AR even in the presence of testosterone. We are doing this by designing proteins that will prevent the AR from entering the nucleus of the cell (which is where it does its dirty work), and also preventing it from binding DNA and activating tumor-specific genes even if it does get into the nucleus.
The above projects are not currently running on BOINC because we don't yet have an efficient queuing system which lets people submit jobs easily, but look for them soon! Also, rest assured that the structure prediction calculations currently running on your computers will have direct bearing on treating disease. There is a three-fold explanation for this direct relationship between structure prediction and disease treatment:
1. Structure prediction and protein design are closely related.
Improvements in structure prediction lead to improvements in protein design, which in turn can be directly translated into making new enzymes, vaccines, etc. For more information on protein design you might be interested in looking at the review we recently wrote in science which is available at our home page (
http://depts.washington.edu/bakerpg).
Schueler-Furman, O., Wang, C., Bradley, P., Misura, K., Baker, D. (2005). Progress in modeling of protein structures and interactions Science 310, 638-642.
2. Structure prediction identifies targets for new drugs.
When we predict structures for proteins in the human genome on a large scale, we learn about the functions of many proteins, which will help in understanding how cells work and how disease occurs. More directly, we will be able to identify many new potential drug targets for which small molecule inhibitors (drugs) can be designed. To put this in context, one major road-block to developing new treatments for human disease is identifying new "drugable" protein targets. Most new drugs these days interact with the same targets as the old drugs, so these drugs lead to only small improvements in disease treatment. Structure prediction helps us identify new drug targets, and so will help us find innovative, perhaps even breakthrough, treatments for disease.
3. Structure prediction allows us to use "rational design" to create new drugs.
If we know the structure of a protein, we can determine its functional sites, and specifically target those sites to be inactivated by a new drug. Calculation of whether a small molecule (drug) will bind to and inactivate a protein target is similar in many ways to the structure prediction calculations we are doing here--it is basically a problem of finding the lowest energy structure of the protein plus drug system--and we have recently developed a new module in ROSETTA to do this docking problem. Results are very promising, and in the near future your machines will likely be running drug docking calculations along with the vaccine and therapeutic protein design projects described above, in addition to the protein folding calculations you are doing now.
