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  • richardmitnick 5:18 pm on August 13, 2016 Permalink | Reply
    Tags: , , , , , Rosetta@home   

    From Rosetta@home: “Designed Protein Containers Push Bioengineering Boundaries” 

    Rosetta@home

    Rosetta@home

    Rosetta@home has posted in their forum a new (July 21, 2016) article, Designed Protein Containers Push Bioengineering Boundaries
    from U Washington’s Institute for Protein Design which I highly recommend for anyone interested in Protein Studies.

    3

    This forum article cites Designed Protein Containers Push Bioengineering Boundariess which goes on to cite Icosahedral protein nanocage – new paper and podcast published in Nature, and “Accurate design of megadalton-scale multi-component icosahedral protein complexes”, published in Science.

    Of this second paper, they write, “In this paper, former Baker lab graduate student Jacob Bale, Ph.D. and collaborators describe the computational design and experimental characterization of ten two-component protein complexes that self-assemble into nanocages with atomic-level accuracy. These nanocages are the largest designed proteins to date with molecular weights of 1.8-2.8 megadaltons and diameters comparable to small viral capsids. The structures have been confirmed by X-ray crystallography (see figure). The advantage of a multi-component protein complex is the ability to control assembly by mixing individually prepared subunits. The authors show that in vitro mixing of the designed subunits occurs rapidly and enables controlled packaging of negatively charged GFP by introducing positive charges on the interior surfaces of the two copmonents.

    The ability to design, with atomic-level precision, these large protein nanostructures that can encapsulate biologically relevant cargo and that can be genetically modified with various functionalities opens up exciting new opportunities for targeted drug delivery and vaccine design.”

    Also referenced in the forum is an article in Science, Jul. 21, 2016 by Robert F. Service This protein designer aims to revolutionize medicines and materials, about Dr David Baker.

    3
    From this Science article, David Baker shows off models of some of the unnatural proteins his team has designed and made.© Rich Frishman

    included also is this video from Science.

    See the full article here.

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

    About Rosetta

    One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

    To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

    Start with a fully unfolded chain (like a metal chain with its ends pulled).
    Move a part of the chain to create a new shape.
    Calculate the energy of the new shape.
    Accept or reject the move depending on the change in energy.
    Repeat 2 through 4 until every part of the chain has been moved a lot of times.

    We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

    A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

    Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

    To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

    Rosetta screensaver

    BOINC

    1

     
  • richardmitnick 8:45 am on July 22, 2016 Permalink | Reply
    Tags: , , Proteins, Rosetta@home, , , This protein designer aims to revolutionize medicines and materials   

    From Science: “This protein designer aims to revolutionize medicines and materials” 

    AAAS

    Science

    1
    David Baker shows off models of some of the unnatural proteins his team has designed and made.

    Jul. 21, 2016
    Robert F. Service

    David Baker appreciates nature’s masterpieces. “This is my favorite spot,” says the Seattle native, admiring the views from a terrace at the University of Washington (UW) here. To the south rises Mount Rainier, a 4400-meter glacier-draped volcano; to the west, the white-capped Olympic Mountain range.

    But head inside to his lab and it’s quickly apparent that the computational biochemist is far from satisfied with what nature offers, at least when it comes to molecules. On a low-slung coffee table lie eight toy-sized, 3D-printed replicas of proteins. Some resemble rings and balls, others tubes and cages—and none existed before Baker and his colleagues designed and built them. Over the last several years, with a big assist from the genomics and computer revolutions, Baker’s team has all but solved one of the biggest challenges in modern science: figuring out how long strings of amino acids fold up into the 3D proteins that form the working machinery of life. Now, he and colleagues have taken this ability and turned it around to design and then synthesize unnatural proteins intended to act as everything from medicines to materials.

    2

    Already, this virtuoso proteinmaking has yielded an experimental HIV vaccine, novel proteins that aim to combat all strains of the influenza viruses simultaneously, carrier molecules that can ferry reprogrammed DNA into cells, and new enzymes that help microbes suck carbon dioxide out of the atmosphere and convert it into useful chemicals. Baker’s team and collaborators report making cages that assemble themselves from as many as 120 designer proteins, which could open the door to a new generation of molecular machines.

    f the ability to read and write DNA spawned the revolution of molecular biology, the ability to design novel proteins could transform just about everything else. “Nobody knows the implications,” because it has the potential to impact dozens of different disciplines, says John Moult, a protein-folding expert at the University of Maryland, College Park. “It’s going to be totally revolutionary.”

    Baker is by no means alone in this pursuit. Efforts to predict how proteins fold, and use that information to fashion novel versions, date back decades. But today he leads the charge. “David has really inspired the field,” says Guy Montelione, a protein structure expert at Rutgers University, New Brunswick, in New Jersey. “That’s what a great scientist does.”

    Baker, 53, didn’t start out with any such vision. Though both his parents were professors at UW—in physics and atmospheric sciences—Baker says he wasn’t drawn to science growing up. As an undergraduate at Harvard University, Baker tried studying philosophy and social studies. That was “a total waste of time,” he says now. “It was a lot of talk that didn’t necessarily add content.” Biology, where new insights can be tested and verified or discarded, drew him instead, and he pursued a Ph.D. in biochemistry. During a postdoc at the University of California, San Francisco, when he was studying how proteins move inside cells, Baker found himself captivated instead by the puzzle of how they fold. “I liked it because it’s getting at something fundamental.”

    In the early 1960s, biochemists at the U.S. National Institutes of Health (NIH) recognized that each protein folds itself into an intrinsic shape. Heat a protein in a solution and its 3D structure will generally unravel. But the NIH group noticed that the proteins they tested refold themselves as soon as they cool, implying that their structure stems from the interactions between different amino acids, rather than from some independent molecular folding machine inside cells. If researchers could determine the strength of all those interactions, they might be able to calculate how any amino acid sequence would assume its final shape. The protein-folding problem was born.

    From DNA to proteins

    The machinery for building proteins is essential for all life on earth. Click on the arrows at the bottom or swipe horizontally to learn more.

    One way around the problem is to determine protein structures experimentally, through methods such as x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. But that’s slow and expensive. Even today, the Protein Data Bank, an international repository, holds the structures of only roughly 110,000 proteins out of the hundreds of millions or more thought to exist.

    Knowing the 3D structures of those other proteins would offer biochemists vital insights into each molecule’s function, such as whether it serves to ferry ions across a cell membrane or catalyze a chemical reaction. It would also give chemists valuable clues to designing new medicines. So, instead of waiting for the experimentalists, computer modelers such as Baker have tackled the folding problem with computer models.

    They’ve come up with two broad kinds of folding models. So-called homology models compare the amino acid sequence of a target protein with that of a template—a protein with a similar sequence and a known 3D structure. The models adjust their prediction for the target’s shape based on the differences between its amino acid sequence and that of the template. But there’s a major drawback: There simply aren’t enough proteins with known structures to provide templates—despite costly efforts to perform industrial-scale x-ray crystallography and NMR spectroscopy.

    Templates were even scarcer more than 2 decades ago, when Baker accepted his first faculty position at UW. That prompted him to pursue a second path, known as ab initio modeling, which calculates the push and pull between neighboring amino acids to predict a structure. Baker also set up a biochemistry lab to study amino acid interactions, in order to improve his models.

    Early on, Baker and Kim Simons, one of his first students, created an ab initio folding program called Rosetta, which broke new ground by scanning a target protein for short amino acid stretches that typically fold in known patterns and using that information to help pin down the molecule’s overall 3D configuration. Rosetta required such extensive computations that Baker’s team quickly found themselves outgrowing their computer resources at UW.

    Seeking more computing power, they created a crowdsourcing extension called Rosetta@home, which allows people to contribute idle computer time to crunching the calculations needed to survey all the likely protein folds. Later, they added a video game extension called Foldit, allowing remote users to apply their instinctive protein-folding insights to guide Rosetta’s search. The approach has spawned an international community of more than 1 million users and nearly two dozen related software packages that do everything from designing novel proteins to predicting the way proteins interact with DNA.

    “The most brilliant thing David has done is build a community,” says Neil King, a former Baker postdoc, now an investigator at UW’s Institute for Protein Design (IPD). Some 400 active scientists continually update and improve the Rosetta software. The program is free for academics and nonprofit users, but there’s a $35,000 fee for companies. Proceeds are plowed back into research and an annual party called RosettaCon in Leavenworth, Washington, where attendees mix mountain hikes and scientific talks.

    Despite this success, Rosetta was limited. The software was often accurate at predicting structures for small proteins, fewer than 100 amino acids in length. Yet, like other ab initio programs, it struggled with larger proteins. Several years ago, Baker began to doubt that he or anyone else would ever manage to solve most protein structures. “I wasn’t sure whether I would get there.”

    Now, he says, “I don’t feel that way anymore.”

    What changed his outlook was a technique first proposed in the 1990s by computational biologist Chris Sander, then with the European Molecular Biology Laboratory in Heidelberg, Germany, and now with Harvard. Those were the early days of whole genome sequencing, when biologists were beginning to decipher the entire DNA sequences of microbes and other organisms. Sander and others wondered whether gene sequences could help identify pairs of amino acids that, although distant from each other on the unfolded proteins, have to wind up next to each other after the protein folds into its 3D structure.

    Clues from genome sequences

    Comparing the DNA of similar proteins from different organisms shows that certain pairs of amino acids evolve in tandem—when one changes, so does the other. This suggests they are neighbors in the folded protein, a clue for predicting structure.

    Sander reasoned that the juxtaposition of those amino acids must be crucial to a protein’s function. If a mutation occurs, changing one of the amino acids so that it no longer interacts with its partner, the protein might no longer work, and the organism could suffer or die. But if both neighboring amino acids are mutated at the same time, they might continue to interact, and the protein might work as well or even better.

    The upshot, Sander proposed, was that certain pairs of amino acids necessary to a protein’s structure would likely evolve together. And researchers would be able to read out that history by comparing the DNA sequences of genes from closely related proteins in different organisms. Whenever such DNA revealed pairs of amino acids that appeared to evolve in lockstep, it would suggest that they were close neighbors in the folded protein. Put enough of those constraints on amino acid positions into an ab initio computer model, and the program might be able to work out a protein’s full 3D structure.

    Unfortunately, Sander says, his idea “was a little ahead of its time.” In the 1990s, there weren’t enough high-quality DNA sequence data from enough similar proteins to track coevolving amino acids.

    By the early part of this decade, however, DNA sequences were flooding in thanks to new gene-sequencing technology. Sander had also teamed up with Debora Marks at Harvard Medical School in Boston to devise a statistical algorithm capable of teasing out real coevolving pairs from the false positives that plagued early efforts. In a 2011 article in PLOS ONE, Sander, Marks, and colleagues reported that the coevolution technique could constrain the position of dozens of pairs of amino acids in 15 proteins—each from a different structural family—and work out their structures. Since then, Sander and Marks have shown that they can decipher the structure of a wide variety of proteins for which there are no homology templates. “It has changed the protein-folding game,” Sander says.

    It certainly did so for Baker. When he and colleagues realized that scanning genomes offered new constraints for Rosetta’s ab initio calculations, they seized the opportunity. They were already incorporating constraints from NMR and other techniques. So they rushed to write a new software program, called Gremlin, to automatically compare gene sequences and come up with all the likely coevolving amino acid pairs. “It was a natural for us to put them into Rosetta,” Baker says.

    The results have been powerful. Rosetta was already widely considered the best ab initio model. Two years ago, Baker and colleagues used their combined approach for the first time in an international protein-folding competition, the 11th Critical Assessment of protein Structure Prediction (CASP). The contest asks modelers to compute the structures of a suite of proteins for which experimental structures are just being worked out by x-ray crystallography or NMR. After modelers submit their predictions, CASP’s organizers then reveal the actual experimental structures. One submission from Baker’s team, on a large protein known as T0806, came back nearly identical to the experimental structure. Moult, who heads CASP, says the judge who reviewed the predicted structure immediately fired off an email to him saying “either someone solved the protein-folding problem, or cheated.”

    “We didn’t [cheat],” Sergey Ovchinnikov, a grad student in Baker’s lab, says with a chuckle.

    The implications are profound. Five years ago, ab initio models had determined structures for just 56 proteins of the estimated 8000 protein families for which there is no template. Since then, Baker’s team alone has added 900 and counting, and Marks believes the approach will already work for 4700 families. With genome sequence data now pouring into scientific databases, it will likely only be a couple years before protein-folding models have enough coevolution data to solve structures for nearly any protein, Baker and Sander predict. Moult agrees. “I have been waiting 10 years for a breakthrough,” he says. “This seems to me a breakthrough.”

    For Baker, it’s only the beginning. With Rosetta’s steadily improving algorithms and ever-greater computing power, his team has in essence mastered the rules for folding—and they’ve begun to use that understanding to try to one-up nature’s creations. “Almost everything in biomedicine could be impacted by an ability to build better proteins,” says Harvard synthetic biologist George Church.

    Baker notes that for decades researchers pursued a strategy he refers to as “Neandertal protein design,” tweaking the genes for existing proteins to get them to do new things. “We were limited by what existed in nature. … We can now short-cut evolution and design proteins to solve modern-day problems.”

    Take medicines, such as drugs to combat the influenza virus. Flu viruses come in many strains that mutate rapidly, which makes it difficult to find molecules that can knock them all out. But every strain contains a protein called hemagglutinin that helps it invade host cells, and a portion of the molecule, known as the stem, remains similar across many strains. Earlier this year, Baker teamed up with researchers at the Scripps Research Institute in San Diego, California, and elsewhere to develop a novel protein that would bind to the hemagglutinin stem and thereby prevent the virus from invading cells.

    The effort required 80 rounds of designing the protein, engineering microbes to make it, testing it in the lab, and reworking the structure. But in the 4 February issue of PLOS ONE, the researchers reported that when they administered their final creation to mice and then injected them with a normally lethal dose of flu virus, the rodents were protected. “It’s more effective than 10 times the dose of Tamiflu,” an antiviral drug currently on the market, says Aaron Chevalier, a former Baker Ph.D. student who now works at a Seattle biotech company called Virvio here that is working to commercialize the protein as a universal antiflu drug.

    Another potential addition to the medicine cabinet: a designer protein that chops up gluten, the infamous substance in wheat and other grains that people with Celiac disease or gluten sensitivity have trouble digesting. Ingrid Swanson Pultz began crafting the gluten-breaker even before joining Baker’s lab as a postdoc and is now testing it in animals and working with IPD to commercialize the research. And those self-assembling cages that debut this week could one day be filled with drugs or therapeutic snippets of DNA or RNA that can be delivered to disease sites throughout the body.

    The potential of these unnatural proteins isn’t limited to medicines. Baker, King, and their colleagues have also attached up to 120 copies of a molecule called green fluorescent protein to the new cages, creating nano-lanterns that could aid research by lighting up as they move through tissues.

    Church says he believes that designer proteins might soon rewrite the biology inside cells. In a paper last year in eLife, he, Baker, and colleagues designed proteins to bind to either a hormone or a heart disease drug inside cells, and then regulate the activity of a DNA-cutting enzyme, Cas9, that is part of the popular CRISPR genome-editing system. “The ability to design sensors [inside cells] is going to be big,” Church says. The strategy could allow researchers or physicians to target the powerful gene-editing system to a specific set of cells—those that are responding to a hormone or drug. Biosensors could also make it possible to switch on the expression of specific genes as needed to break down toxins or alert the immune cells to invaders or cancer.

    Protein for every purpose

    The ability to predict how an amino acid sequence will fold—and hence how the protein will function—opens the way to designing novel proteins that can catalyze specific chemical reactions or act as medicines or materials. Genes for these proteins can be synthesized and inserted into microbes, which build the proteins.
    array

    2D arrays can be used as nanomaterials in various applications.

    3

    Information can be coded into protein sequences, like DNA.

    5

    Antagonists bind to a target protein, blocking its activation.

    4

    Channels through membranes act as gateways.

    6

    Cages can contain medicinal cargo or carry it on their surfaces.

    7

    Sensors travel throughout the body to detect various signals.

    8

    Baker’s lab is abuzz with other projects. Last year, his group and collaborators reported engineering into bacteria a completely new metabolic pathway, complete with a designer protein that enabled the microbes to convert atmospheric carbon dioxide into fuels and chemicals. Two years ago, they unveiled in Science proteins that spontaneously arrange themselves in a flat layer, like interlocking tiles on a bathroom floor. Such surfaces may lead to novel types of solar cells and electronic devices.

    In perhaps the most thought-provoking project, Baker’s team has designed proteins to carry information, imitating the way DNA’s four nucleic acid letters bind and entwine in the genetic molecule’s famed double helix. For now, these protein helixes can’t convey genetic information that cells can read. But they symbolize something profound: Protein designers have shed nature’s constraints and are now only limited by their imagination. “We can now build a whole new world of functional proteins,” Baker says.

    See the full article here .

    YOU CAN JOIN IN THIS WORK FROM THE COMFORT OF YOUR EASY CHAIR.

    Rosetta@home runs on software from Berkeley Open Infrastructure for Network Computing (BOINC).
    Visit the BOINC website, download and install the BOINC software, attach to the Rosetta@home project. It is that simple. The project will use the available cpu cycles of your computer, tablet or cell phone to “crunch” data for the Baker Lab.

    While you are at the BOINC website, check out some of the other really important projects running at universities and institutions all over the world. They could all use your help and would run simultaneously with no conflicts on your devices.

    BOINCLarge

    BOINC WallPaper

    The American Association for the Advancement of Science is an international non-profit organization dedicated to advancing science for the benefit of all people.

    Please help promote STEM in your local schools.
    STEM Icon
    Stem Education Coalition

     
  • richardmitnick 5:27 pm on May 10, 2016 Permalink | Reply
    Tags: , , , , Rosetta@home   

    From BOINC project Rosetta at U Washington: “A breakthrough paper” 

    Rosetta@home

    Rosetta@home

    BOINC WallPaper

    May 10, 2016

    We’ve come out with a breakthrough paper in Science titled ‘De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity’.

    This is an exciting and significant breakthrough for de novo protein design. A particular challenge for current protein design methods has been the accurate design of polar binding sites or polar binding interfaces, both of which require hydrogen bonding interactions. Hydrogen bond networks are governed by complex physics and energetic coupling, that until now, could not be computed within the scope of design. The computational method described in this paper, HBNet, now provides a general method to accurately design in hydrogen bond networks. This new capacity should be useful in the design of new enzymes, proteins that bind small molecules, and polar protein interfaces. Thanks Rosetta@home community for your participation and help!

    See the full article here or here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

    About Rosetta

    One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

    To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

    Start with a fully unfolded chain (like a metal chain with its ends pulled).
    Move a part of the chain to create a new shape.
    Calculate the energy of the new shape.
    Accept or reject the move depending on the change in energy.
    Repeat 2 through 4 until every part of the chain has been moved a lot of times.

    We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

    A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

    Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

    To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

    Rosetta screensaver

    BOINC

     
  • richardmitnick 11:20 am on July 18, 2014 Permalink | Reply
    Tags: , , , , Rosetta@home   

    From Rosetta@home 

    Rosetta@home

    Rosetta@home

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

    About Rosetta

    One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

    To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

    Start with a fully unfolded chain (like a metal chain with its ends pulled).
    Move a part of the chain to create a new shape.
    Calculate the energy of the new shape.
    Accept or reject the move depending on the change in energy.
    Repeat 2 through 4 until every part of the chain has been moved a lot of times.

    We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

    A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

    Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

    To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

    Rosetta screensaver

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 11:33 am on June 27, 2012 Permalink | Reply
    Tags: , , , , , Rosetta@home   

    From Argonne Lab APS: “Computer-Designed Proteins to Disarm a Variety of Flu Viruses” 

    News from Argonne National Laboratory

    JUNE 18, 2012
    No Writer Credit

    Computer-designed proteins are under construction to fight the flu. Researchers who carried out studies at the U.S. Department of Energy Office of Science’s Advanced Photon Source at Argonne National Laboratory are demonstrating that proteins that are found in nature, but do not normally bind the influenza virus, can be engineered to act as broad-spectrum antiviral agents against a variety of flu virus strains, including the H1N1 pandemic influenza.

    protein
    Close-up view of the F-HB80.4-SC1918/hemagglutinin interface as determined at . From T.A. Whitehead et al., Nat. Biotech. 30(6), 543 (6 June 2012).

    ‘One of these engineered proteins has a flu-fighting potency that rivals that of several human monoclonal antibodies,’ said David Baker, professor of Biochemistry at the University of Washington, in a report in Nature Biotechnology.

    The research team in this study, from the University of Washington, The Scripps Research Institute, and the Naval Health Research Center is making major inroads in optimizing the function of computer-designed influenza inhibitors. These proteins are constructed via computer modeling to fit exquisitely into a specific nano-sized target on flu viruses. By binding the target region like a key into a lock, they keep the virus from changing shape, a tactic that the virus uses to infect living cells. The research efforts, akin to docking a space station but on a molecular level, are made possible by computers that can describe the landscapes of forces involved on the submicroscopic scale.”

    Dr David Baker heads up the Baker Laboratory at The University of Washington. The Baker Lab is the home of the Rosetta@home project, a Public Distributed Computing project which runs on BOINC software. Rosetta research studies “… the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases…” using the combined resources of thousands of personal computers at home and at work, which give over their unused CPU cycles for the processing of data.

    See the full article here.

    Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

     
  • richardmitnick 8:43 pm on June 13, 2012 Permalink | Reply
    Tags: , , , Rosetta@home, , ,   

    From Berkeley Lab: “Berkeley Lab Scientists Help Define the Healthy Human Microbiome” 

    bl
    Berkeley Lab

    Computing, bioinformatics, and microbial ecology resources play key role in mapping our microbial make-up

    June 13, 2012
    Dan Krotz

    You’re outnumbered. There are ten times as many microbial cells in you as there are your own cells.

    The human microbiome—as scientists call the communities of microorganisms that inhabit your skin, mouth, gut, and other parts of your body by the trillions—plays a fundamental role in keeping you healthy. These communities are also thought to cause disease when they’re perturbed. But our microbiome’s exact function, good and bad, is poorly understood. That could change.

    biom
    The bacterium, Enterococcus faecalis, which lives in the human gut, is just one type of microbe studied in NIH’s Human Microbiome Project. (Courtesy: United States Department of Agriculture)

    A National Institutes of Health (NIH)-organized consortium that includes scientists from the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) has for the first time mapped the normal microbial make-up of healthy humans. [Human Microbiome Project (HMP) is a United States National Institutes of Health initiative with the goal of identifying and characterizing the microorganisms which are found in association with both healthy and diseased humans (i.e. their microbial flora). Launched in 2008, it is a five-year project, best characterized as a feasibility study, and has a total budget of $115 million. The ultimate goal of this and similar NIH-sponsored microbiome projects is to test if changes in the human microbiome are associated with human health or disease. This topic is currently not well-understood.]

    The research will help scientists understand how our microbiome carries out vital tasks such as supporting our immune system and helping us digest food. It’ll also shed light on our microbiome’s role in diseases such as ulcerative colitis, Crohn’s disease, and psoriasis, to name a few.”

    See the full article here.

    For those interested – and you should be interested – the Human Protein Folding Project (HPF2) at the Bonneau Lab, New York University, is a participant in the HMP project. HPF2 is a project in Public Distributed Computing under the aegis of the World Community Grid (WCG), running on software from the Berkeley Open Infrastructure for Network Computing (BOINC) and using the project products of the rosetta@home project from the Baker Lab, University of Washington.

    That is a pretty long sentence. What it means is, if you visit WCG, or BOINC, and download the BOINC agent software for Windows, Linux, or Mac, you can attach to the HPF2 project and process data for HMP. While you are at it, look around at WCG website, there are about a dozen very worthwhile projects all aimed at curing illnesses and solving fundamental problems for mankind. Also, at the BOINC website the are a vast variety of projects in Biology, Chemistry, Physics, Mathematics, and Astronomy.

    Here are some pretty pictures.

    So, you know, when you see graphics, these are serious guys. Give them (us) a look.

    My BOINC stats.
    graph

     
  • richardmitnick 1:03 pm on April 30, 2012 Permalink | Reply
    Tags: , , , , , Dr. David Baker, , Rosetta@home   

    From David Baker at Rosetta@home: Rosetta Chosen for the BOINC Pentathlon 

    This is a post from Dr. David Baker, The Baker Lab at the University of Washington, the site of rosetta@home.


    Dr. David Baker

    “I have just been told the very good news that Rosetta@home will be the first project of the BOINC pentathlon, and would like to thank all of the participating teams. I also just learned from the discussion thread that Rosetta@home will be the project of the month for BOINC synergy-this is more excellent news!!
    bp

    Your increased contributions to rosetta@home could not come at a better time! We’ve been testing our improved structure prediction methodology in a recently started challenge called CAMEO. For most of the targets, the Rosetta@home models are extremely good, but for a minority of targets the predictions are not good at all. We’ve now tracked down the source of these failures and it is what we are calling “workunit starvation”; in the limited amount of time the Rosetta server has to produce models (2-3 days) in these cases very few models were made-this happens because many targets are being run on the server so that only a fraction of your cpu power is focused on any one target. while we are working to fix this internally, by far the best solution is to have more total CPU throughput so each target gets more models.

    You can follow how we are doing at http://www.cameo3d.org/. You will see that Rosetta is one of the few servers whose name is not kept secret-this is because Rosetta is a public project. Our server receives targets from CAMEO and soon CASP, sends the required calculations out to your computers through Rosetta@home, and then processes the returned results and submits the lowest energy models.

    We are excited that the workunit starvation problem may go away through your increased efforts for Rosetta@home. Thanks!!!”

    David’s post is here.

    ————————————————————————

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing, developed at UC Berkeley.

    Visit the BOINC web page, click on Choose projects and check out some of the very worthwhile studies you will find. Then click on Download and run BOINC software/ All Versons. Download and install the current software for your 32bit or 64bit system, for Windows, Mac or Linux. When you install BOINC, it will install its screen savers on your system as a default. You can choose to run the various project screen savers or you can turn them off. Once BOINC is installed, in BOINC Manager/Tools, click on “Add project or account manager” to attach to projects. Many BOINC projects are listed there, but not all, and, maybe not the one(s) in which you are interested. You can get the proper URL for attaching to the project at the projects’ web page(s) BOINC will never interfere with any other work on your computer.

    MAJOR PROJECTS RUNNING ON BOINC SOFTWARE

    SETI@home The search for extraterrestrial intelligence. “SETI (Search for Extraterrestrial Intelligence) is a scientific area whose goal is to detect intelligent life outside Earth. One approach, known as radio SETI, uses radio telescopes to listen for narrow-bandwidth radio signals from space. Such signals are not known to occur naturally, so a detection would provide evidence of extraterrestrial technology.

    Radio telescope signals consist primarily of noise (from celestial sources and the receiver’s electronics) and man-made signals such as TV stations, radar, and satellites. Modern radio SETI projects analyze the data digitally. More computing power enables searches to cover greater frequency ranges with more sensitivity. Radio SETI, therefore, has an insatiable appetite for computing power.

    Previous radio SETI projects have used special-purpose supercomputers, located at the telescope, to do the bulk of the data analysis. In 1995, David Gedye proposed doing radio SETI using a virtual supercomputer composed of large numbers of Internet-connected computers, and he organized the SETI@home project to explore this idea. SETI@home was originally launched in May 1999.”


    SETI@home is the birthplace of BOINC software. Originally, it only ran in a screensaver when the computer on which it was installed was doing no other work. With the powerand memory available today, BOINC can run 24/7 without in any way interfering with other ongoing work.

    seti
    The famous SET@home screen saver, a beauteous thing to behold.

    einstein@home The search for pulsars. “Einstein@Home uses your computer’s idle time to search for weak astrophysical signals from spinning neutron stars (also called pulsars) using data from the LIGO gravitational-wave detectors, the Arecibo radio telescope, and the Fermi gamma-ray satellite. Einstein@Home volunteers have already discovered more than a dozen new neutron stars, and we hope to find many more in the future. Our long-term goal is to make the first direct detections of gravitational-wave emission from spinning neutron stars. Gravitational waves were predicted by Albert Einstein almost a century ago, but have never been directly detected. Such observations would open up a new window on the universe, and usher in a new era in astronomy.”

    MilkyWay@Home Milkyway@Home uses the BOINC platform to harness volunteered computing resources, creating a highly accurate three dimensional model of the Milky Way galaxy using data gathered by the Sloan Digital Sky Survey. This project enables research in both astroinformatics and computer science.”

    Leiden Classical “Join in and help to build a Desktop Computer Grid dedicated to general Classical Dynamics for any scientist or science student!”

    World Community Grid (WCG) World Community Grid is a special case at BOINC. WCG is part of the social initiative of IBM Corporation and the Smarter Planet. WCG has under its umbrella currently eleven disparate projects at globally wide ranging institutions and universities. Most projects relate to biological and medical subject matter. There are also projects for Clean Water and Clean Renewable Energy. WCG projects are treated respectively and respectably on their own at this blog. Watch for news.

    Rosetta@home “Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s….”

    GPUGrid.net “GPUGRID.net is a distributed computing infrastructure devoted to biomedical research. Thanks to the contribution of volunteers, GPUGRID scientists can perform molecular simulations to understand the function of proteins in health and disease.” GPUGrid is a special case in that all processor work done by the volunteers is GPU processing. There is no CPU processing, which is the more common processing. Other projects (Einstein, SETI, Milky Way) also feature GPU processing, but they offer CPU processing for those not able to do work on GPU’s.

    These projects are just the oldest and most prominent projects. There are many others from which you can choose.

    There are currently some 300,000 users with about 480,000 computers working on BOINC projects That is in a world of over one billion computers. We sure could use your help.

    My BOINC

    graph

     
  • richardmitnick 2:46 pm on March 30, 2012 Permalink | Reply
    Tags: , , , , , , Rosetta@home   

    From David Baker at the Rosetta Project – We Need Your Help 

    Dr David baker
    Dr David Baker, Forum moderator, Project administrator, Project developer, Project scientist

    David Baker tells us,

    “In the last two months we believe we have made quite a breakthrough in structure prediction, and are excited to test the new method in CASP10. We need your help though–we are now testing many aspects of the new approach and are seriously limited by available CPU cycles. There are now so many flu inhibitor design and structure prediction jobs queued up on Rosetta@Home that there is an eight day wait before they are getting sent out to you. This would be a great time to temporarily increase Rosetta@Home’s share on your computers and/or recruit new users–we need all the help we can get! thanks! David”

    From The Rosetta web site:

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research [below] for more information). Please join us in our efforts! Rosetta@home is not for profit.

    Disease Related Research

    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:

    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.

    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.

    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.

    bl
    Please visit the Baker Lab web site to read about

    Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

    Increased Diels-Alderase activity through backbone remodeling guided by Foldit players

    Algorithm discovery by protein folding game players

    Crystal structure of a monomeric retroviral protease solved by protein folding game players

    Computational design of protein inhibitors of Spanish and avian flu hemagglutinin

    More.

    udub

    Rosetta@home runs on BOINC software from U.C. Berkeley

     
  • richardmitnick 9:32 am on December 6, 2011 Permalink | Reply
    Tags: , , , Rosetta@home, , ,   

    From the New York Times: “Computer Scientists May Have What It Takes to Help Cure Cancer” – Another Blown Opportunity to boost BOINC 

    By DAVID PATTERSON
    December 5, 2011

    This is copyright protected, so just a couple of hints.
    “The war against cancer is increasingly moving into cyberspace. Computer scientists may have the best skills to fight cancer in the next decade — and they should be signing up in droves….An inspirational example is the Foldit game — developed by the computer scientist Zoran Popovic at the University of Washington.

    Very nice, great article, but, huge gap. No mention of the roots of Dr Popovic’s successful adventure.

    Dr Popovic worked with The Baker Laboratory, the locus of rosetta@home, a project which runs on BOINC software from UC Berkeley. Rosetta@home has currently 37,456 “users” on 60162 “hosts”. The project does currently 58 TeraFLOPS of data per 24 hour period.

    On the one hand, you can certainly visit the Foldit web site to participate. If, on the other hand, you are not fond of games, you can visit the BOINC web site, download and install the small piece of software, and attach to the Rosetta project. You will receive small packs of data called “work units” or “WU’s” to “crunch”. As each WU is finished, your computer will return the results and you will receive more work.

    Rosetta software is also used by World Community Grid (WCG) project Human Proteome Folding. This project is based at New York University in the Bonneau Laboratory

    i3

    At both the WCG and BOINC web sites you will find many other really exciting projects in which you may participate. All WCG projects run on the BOINC software, along with the many independent projects at the BOINC web site.

    Once you have installed the BOINC software and attached to your chosen projects, you can be as active or passive in this process as you wish. You can pretty much simply let the stuff happen in the background and pay it scant attention. However, each project has its own forum covering many topics, including the science involved and the operation of the software. You can also check to see how your are doing by signing on at BOINCstats.com

    There are currently 286,105 “users” (people) on 515,015 “hosts” (computers) in all of BOINC. Currently we are doing 5,337 TeraFLOPS of work in a 24 hour period. That’s over half a PetafLOP, which would put us somewhere around 14th or 15th on the TOP500 list of supercomputers in the world. Except, in that world, we don’t count. WCG currently has 94,007 users on 211,163 hosts. We are currently at 278 TeraFLOPS.

    BOINC software will run on Windows, Mac and Linux based computers. So, whatever your flavor, why don’t you visit BOINC and WCG, give us a look, and try us out? The BOINC process never interferes with anything else that you are doing on the computer. If on occasion you require huge amounts of resources, such as “storming the castle”, BOINC will instantaneously give up its resources and pause until your battle is finished. I hope to run into you in a forum.

    Mr. Patterson work is an example of why I started this blog.

     
  • richardmitnick 5:29 pm on November 25, 2011 Permalink | Reply
    Tags: , , , , Rosetta@home, , ,   

    From WCG Project Human Proteome Folding (HPF2) Exciting Updates 

    Human Proteome Folding (HPF2)., a WCG project in The Bonneau Lab at New York University has posted some very exciting news. The report is copyright protected, so I will not trespass on that.

    i3
    Depictions of proteins

    HPF2 utilizes software developed by BOINC project Rosetta@home, in the The Baker Lab at University of Washington.
    i5
    i4

    You can see the report here.

    But WCG crunchers can be proud of the fact that we have contributed – this from the WCG web site – 96,695 years, 223 days, 09 hours,26 minutes, 30 seconds to this effort. This is the power of Public Distributed Computing via the BOINC software on which our projects are run.

    I cannot begin to contemplate how this work would have gotten to this point without us, except at the expensive cost of processing time on some supercomputer.

    .

    You, too, dear reader, can be a part of this incredible process. Visit either WCG or BOINC, download and install the software, and attach to this and other worthy projects at the WCG web site and also at the BOINC website. You financial cost is about the same as a 100-150 watt light bulb. Your personal satisfaction at being a part of this is immeasurable.

     
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