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

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

    AAAS

    Science

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    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.

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    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.

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    Information can be coded into protein sequences, like DNA.

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    Antagonists bind to a target protein, blocking its activation.

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    Channels through membranes act as gateways.

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

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    Sensors travel throughout the body to detect various signals.

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    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.

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  • richardmitnick 11:21 am on July 9, 2016 Permalink | Reply
    Tags: , BOINC, ,   

    From Outsmart Ebola Together at WCG: “Summer Plans for Outsmart Ebola Together” 

    New WCG Logo

    WCGLarge

    World Community Grid (WCG)

    9 Jul 2016
    Dr. Erica Ollmann Saphire, PhD
    The Scripps Research Institute

    Summary
    In this brief update, Dr. Erica Saphire talks about the continuing need for research on the Ebola virus, and the search for funding to help analyze the data generated so far by World Community Grid volunteers.


    Access mp4 video here .

    As of June 2016, the World Health Organization (WHO) reports that there have been more than 28,000 cases of the Ebola virus in Guinea, Liberia and Sierra Leone, with more than 11,000 deaths. While WHO has declared that the most recent outbreak has ended, most researchers and public health experts believe that it is only a matter of time before another Ebola virus outbreak occurs. Sudan virus and Marburg virus, two diseases that are related to the Ebola virus, can also cause severe hemorrhaging and have high potential for outbreaks. Lassa virus also causes similar symptoms and is endemic, causing thousands of cases every year in Western Africa.

    For these reasons, our research is crucial to helping contain future disease outbreaks. In order to move more rapidly towards laboratory testing of the most promising compounds screened on World Community Grid, we are looking for funding to hire an additional lab member that would be shared between our lab and the Olson Laboratory here at The Scripps Research Institute. This person would help with data analysis for Outsmart Ebola Together and Fight AIDS@Home and would also help prepare the future targets for Ebola and related diseases for us to explore using World Community Grid.

    Funding is always an issue in scientific research, and resources are becoming more scarce for science throughout the world. But Outsmart Ebola Together remains a top priority for my lab, and our work will continue. We are very grateful to the many volunteers who continue to contribute to this project. We look forward to announcing receiving funding for a new position in the future!

    See the full article here.

    You can help stamp out EBOLA.
    Visit World Community Grid (WCG). Download and install the BOINC software on which it runs. Attach to the Outsmart Ebola Together project. This will allow WCG to use your computer’s free CPU cycles to process computational data for the project.

    WCGLarge
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    Outsmart Ebola Together

    While you are at WCG and BOINC, check out the other very worthwhile projects running on this software. All project results are “open source”, free for the use of scientists world while to advance health and other issues of mankind.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.
    BOINCLarge

    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.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BET!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    FightAIDS@home Phase II

    FAAH Phase II
    OpenZika

    Rutgers Open Zika

    WCG Help Stop TB
    Help Stop TB
    WCG Help Stop TB
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

     
  • richardmitnick 9:52 am on June 15, 2016 Permalink | Reply
    Tags: , BOINC, , ,   

    From NPR: “Here’s Really Where Zika Mosquitoes Are Likely In The U.S.” 

    NPR

    National Public Radio (NPR)

    June 13, 2016
    Michaeleen Doucleff

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    Counties where Aedes aegypti was reported between Jan. 1, 1995, and March 2016. Counties in yellow recorded one year of A. aegypti being present; those shown in orange recorded two years; and those shown in red, three or more years. Centers for Disease Control and Prevention

    A few months ago, the Centers for Disease Control and Prevention published a startling map that showed the parts of the U.S. that could harbor mosquitoes capable of carrying Zika.

    Many readers, including myself, thought, “Zika could come to my town! It could come to Connecticut! To Ohio and Indiana! Or to Northern California! Oh goodness!”

    The map made it look like a vast swath of the country was at risk for Zika, including New England and the Upper Midwest.

    Well, not quite.

    On Thursday, CDC scientists published another mosquito map for the U.S. And it paints a very different picture.

    The new map shows counties in which scientists, over the past two decades, have collected Aedes aegypti mosquitoes — the type of insect thought to be spreading Zika in Latin American and the Caribbean.

    “The new map is more accurate than the initial one,” says Thomas Scott, an entomologist at the University of California, Davis. “The distribution of the A. aegypti mosquito is much more restricted than the initial map showed.”

    In the map, counties colored yellow reported A. aegypti mosquitoes during one year between 1995 and 2016. Orange counties had the mosquitoes in two years. And red counties are the hot spots: Scientists there found A. aegypti mosquitoes during three or more years in the past two decades.

    This map represents “the best knowledge of the current distribution of this mosquito based on collection records,” entomologist John-Paul Mutebi and his colleagues at the CDC wrote in the Journal of Medical Entomology.

    Many of the hot spots for this mosquito aren’t surprising. They’re places that we already knew are vulnerable to Zika, including counties in southern Florida, along the Gulf Coast and southern Texas. These places have had problems with a virus closely related to Zika, called dengue. They’re already on high alert for Zika.

    But several hot spots are bit more unexpected — and concerning. “Perhaps the most concerning development for A. aegypti is its establishment in the Southwest, most recently in California in 2013,” Mutebi and his co-authors write.

    Other surprises include parts of the Bay Area, greater Washington, D.C., and the Dallas-Fort Worth region, which all have established populations of A. aegypti, the map shows.

    “The country is really a patchwork,” Scott says. “When you drill down into one particular state, you find that the mosquito isn’t found across the whole state. And when you drill down into a county, you find the same thing. The mosquito is found in just a small part.”

    So why did the first map from the CDC make it look like such an extensive part of the country was at risk for Zika?

    “The two maps show different things,” Mutebi tells Shots. “The first map showed where the climate is able to sustain populations of A. aegypti. This new map shows reports from counties where these mosquitoes were found in the last 20 years.”

    And the new map, Mutebi says, is not complete. “Not all counties have mosquito surveillance programs looking for mosquitoes,” he says. In places that do, they are often targeting the mosquito that causes West Nile virus, not A. aegypti.

    “So just because a county hasn’t reported having any A. aegypti mosquitoes doesn’t mean they’re not there,” Mutebi says.

    A. aegypti mosquitoes are nasty critters. They chase down people so they can feed on their blood, says virologist Scott Weaver at the University of Texas Medical Branch in Galveston.

    A. aegypti lives in close association with people, feeds almost exclusively on people — not animals — and even comes into people’s home,” he says. “Its behavior and its ecology are almost ideal for a mosquito to transmit a human virus.”

    See the full article here.

    YOU CAN HELP FIND A CURE FOR THE ZIKA VIRUS.

    There is a new project at World Community Grid [WCG] called OpenZika.
    Zika
    Image of the Zika virus

    Rutgers Open Zika

    WCG runs on your home computer or tablet on software from Berkeley Open Infrastructure for Network Computing [BOINC]. Many other scientific projects run on BOINC software.Visit WCG or BOINC, download and install the software, then at WCG attach to the OpenZika project. You will be joining tens of thousands of other “crunchers” processing computational data and saving the scientists literally thousands of hours of work at no real cost to you.

    WCGLarge
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    BOINC WallPaper
    Please help promote STEM in your local schools.
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    Stem Education Coalition

    Great storytelling and rigorous reporting. These are the passions that fuel us. Our business is telling stories, small and large, that start conversations, increase understanding, enrich lives and enliven minds.

    We are reporters in Washington D.C., and in bunkers, streets, alleys, jungles and deserts around the world. We are engineers, editors, inventors and visionaries. We are Member stations around the country who are deeply connected to our communities. We are listeners and donors who support public radio because we know how it has enriched our own lives and want it to grow strong in a new age.

    We are NPR. And this is our story.

     
  • richardmitnick 10:27 pm on June 11, 2016 Permalink | Reply
    Tags: , BOINC, ,   

    Meet the Quake-Catcher Network, Another Great Project Running on BOINC Software 

    QCN bloc

    Quake-Catcher Network

    6.11.16

    The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.

    After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).

    The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).

    BOINCLarge

    BOINC WallPaper

    The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).

    There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.

    Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.

    USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.

    If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click 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.

    See the full article here .

    Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.

    Below, the QCN Quake Catcher Network map
    QCN Quake Catcher Network map

    Please help promote STEM in your local schools.

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  • richardmitnick 10:27 am on May 20, 2016 Permalink | Reply
    Tags: , BOINC, , , The planet's health is essential to prevent infectious disease,   

    From The Guardian: “The planet’s health is essential to prevent infectious disease” 

    The Guardian Logo

    The Guardian

    15 May 2016
    Sonila Cook
    Oren Ahoobim

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    ‘The environmental degradation of natural ecosystems has resulted in many negative outcomes, one of which is the outbreak of infectious disease.’ Photograph: AFP/Getty Images

    The Zika virus, now detected in 42 countries, is only the latest in a series of diseases establishing a new normal for pandemics. Sars ravaged South China in 2003, Middle East Respiratory Syndrome (Mers) shocked the Middle East in 2012, and Ebola devastated west Africa in 2014. We have seen avian influenza emerge in new geographies alongside mosquito-borne viruses, such as Chikungunya. Over the past 50 years, more than 300 infectious pathogens have either newly developed or reemerged in places where they had never been seen before.

    These trends raise questions: Why are infectious diseases occurring with such frequency? Why are pandemics the new normal? The increased rate of outbreak is typically framed as a failure of the health system. Indeed, that is a critical component. But the conditions that allow for outbreak in the first place are rooted in environmental change.

    The environmental degradation of natural ecosystems has resulted in many negative outcomes, one of which is the outbreak of infectious disease. The vast majority of human infectious diseases, such as malaria, Zika, and HIV/Aids, originate in animals. When we disrupt the natural environment and habitat of animals, we are poking the beast, so to speak.

    Take deforestation. Destroying the delicate balance of ecological conditions in forests increases contact between humans and potential reservoirs of disease in the animal population. Evidence shows that Ebola may have been spread to humans who came into contact with infected wildlife, enabled by widespread deforestation. The environment plays a critical role in serving as a buffer against infectious disease. A failure to recognise the value of this service that forests provide means that deforestation and infectious disease outbreaks are likely to continue at alarming rates.

    2
    Jambi province, Sumatra. A logged-over area in the vast track of pulp wood concessions. Photograph: Romeo Gacad/AFP/Getty Images

    Infectious disease is a systems problem that requires systems solutions. Treating only one part of the overall problem – whether by vaccination, quarantine or awareness campaigns – merely scratches the surface. Effective solutions must address the system as a whole, including changes to underlying ecosystems. The field of planetary health has emerged to better understand and solve the integrated relationship between human health and the environment. It aims to shed light on health problems induced by large-scale changes to the environment, and to highlight new ways of working to address these often intractable issues.

    The connection between environmental change and human health is increasingly clear, but this big-picture view is not how we currently orient ourselves. Take existing public health solutions to Ebola, for example, which are to treat the disease, contain its spread, and prevent it by developing a vaccine. These are all necessary, but they miss a large set of tools found further upstream.

    3
    Sawmills processing illegally logged trees from the Amazon rainforest near Rio Pardo, Brazil. Photograph: Nacho Doce/Reuters

    A way to access these tools might be to ask ourselves: can we prevent transmission of the Ebola virus from animals to humans to begin with? With planetary health, we have an opportunity to redefine prevention to include upstream solutions that safeguard the environment. For Ebola, this would mean that forest protection efforts would be added to the arsenal of tools we use to fight the disease. These solutions can have multiple benefits to the environment and to human health; for example, in addition to preventing pandemics, reducing deforestation can combat climate change, protect biodiversity, and preserve watersheds that provide clean water to nearby communities.

    4
    Sino County, Liberia: A person stands amid the remnants of slash and burn deforestation. Photograph: Evan Bowen-Jones/Alamy

    Planetary health draws attention to the cross-sector innovation that is needed to tackle complex problems such as infectious disease, using integrated surveillance tools incorporating both environmental and health data. For example, USAid and the Wildlife Conservation Society are creating a surveillance system, Predict, to detect and prevent spillover of potentially pandemic pathogens that can move between wildlife and people – and inform environmental and health policy to prevent it. Another example of cross-sector innovation is the Norway-Liberia agreement; Norway will give Liberia up to $150m (£104m) over the next six years to fund protective measures to squash illegal logging in its agricultural sector, with the aim of averting a future Ebola crisis.

    A growing community of practice is forming around planetary health. The US-based Rockefeller Foundation and UK-based Wellcome Trust are shaping and nurturing this emerging field. They are funding research to better understand complex human-environmental systems and the range of responses that local communities, governments and international bodies can bring to bear.

    Together with leading scientists, including those at the Harvard School of Public Health, the Wildlife Conservation Society, and the London School of Hygiene and Tropical Medicine, Rockefeller and Wellcome are looking into a host of issues for which a planetary health approach might be useful. These include the relationship between climate change and human nutrition, and the links between coastal ecosystems and resilience to natural disasters, among others.

    6
    Mato Grosso, Brazil: A single tree is seen on land that was previously jungle. Photograph: Bruno Domingos / Reuters/REUTERS

    “Public health alone can take us only so far in addressing today’s complex health challenges,” said Michael Myers, managing director of the Rockefeller Foundation. “We see the need for a new interdisciplinary field that’s as relevant for this century as public health was for the last – planetary health, or what we consider public health 2.0. By embracing the new reality that our health and the planet’s health are inextricably linked, the field of planetary health will identify more effective approaches to ensuring our own health.”

    We don’t know what pandemics are coming in the future. What we do know is that with continued environmental degradation, outbreaks will occur with greater frequency, and the toolkit we are using to control them is incomplete. Planetary health can help us expand the toolkit by finding ways to prevent outbreaks occurring in the first place, allowing us to proactively manage the health of the human population, rather than reactively try to control deadly diseases that we don’t fully understand.

    n recent years we’ve become more sophisticated at understanding and assessing nature’s value to people; from food and fuel production, to water purification and spiritual renewal, natural ecosystems provide countless services that sustain us. Protection against infectious disease is another critical service. It is time to build a field that fully recognises the important role that the environment plays in our collective health. The survival of our planet and our species depends on it.

    While it is true that “…Treating only one part of the overall problem – whether by vaccination, quarantine or awareness campaigns – merely scratches the surface…” it is still a valuable tool and you can help.

    There are projects at World Community Grid, an initiative IBM Corporation which seek treatment answers in attempts to curb the human degradation.
    Check out what follows:

    WCGLarge

    WCG Logo New

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.
    BOINCLarge

    BOINC WallPaper

    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.

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Open Zika
    7

    Help Stop TB
    WCG Help Stop TB
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

     
  • richardmitnick 5:27 pm on May 10, 2016 Permalink | Reply
    Tags: , , BOINC, ,   

    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 11:23 am on December 24, 2015 Permalink | Reply
    Tags: , BOINC, , ,   

    From U Colorado: “CU-Boulder study reveals evolutionary arms race between Ebola virus, bats” 

    U Colorado

    University of Colorado Boulder.

    December 22, 2015
    Sara Sawyer, 303-735-0531
    ssawyer@colorado.edu

    Trent Knoss, CU-Boulder media relations, 303-735-0528
    trent.knoss@colorado.edu

    Temp 1
    The Ebola virus, isolated in November 2014 from patient blood samples obtained in Mali. The virus was isolated on Vero cells in a BSL-4 suite at Rocky Mountain Laboratories. Credit: NIAID

    The Ebola virus and fruit bats have been waging a molecular battle for survival that may have started at least 25 million years ago, according to a study led by researchers at the University of Colorado Boulder, Albert Einstein College of Medicine and the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID).

    The findings, published today in the journaleLife [no link], shed new light on the biological factors that determine which bat species may harbor the virus in between outbreaks in humans and how bats may transmit the virus to people.

    The researchers showed that a single amino acid change in the Ebola virus could overcome the resistance of the African straw-colored fruit bat cells to infection. These findings hint at one way in which Ebola and other highly infectious filoviruses can evolve to better infect a host.

    “There seems to be a low barrier for Ebola virus to establish itself in this type of bat,” said co-lead author Sara Sawyer, an associate professor in CU-Boulder’s Molecular, Cellular, and Developmental Biology and the BioFrontiers Institute. “One has to wonder why that has not happened yet.”

    To learn more, the researchers exposed cells from four types of African bats (two of them previously linked to Ebola) to several filoviruses, including Ebola. Cells from only one type of bat proved resistant to Ebola virus infection: the African straw-colored fruit bat, which is commonly hunted for bushmeat in West Africa and migrates long distances.

    Outbreaks of Ebola virus disease among humans are thought to begin when a person comes into contact with a wild animal carrying Ebola virus.

    “We knew from our previous research that Ebola virus infects host cells by attaching its surface glycoprotein to a host cell receptor called NPC1,” said Kartik Chandran, an associate professor of microbiology and immunology at Albert Einstein College of Medicine in New York and a co-lead author of the study. “Here, we show how bats have evolved to resist Ebola infection and how, in turn, the virus could have evolved to overcome that resistance.”

    “Identifying potential animal reservoir hosts for Ebola virus will provide a crucial guide for public health prevention and response programs going forward,” said Maryska Kaczmarek, a graduate researcher in Sawyer’s lab at CU-Boulder and a co-author of the study.

    There are currently no FDA-approved treatments or vaccines for the Ebola virus. The 2014 Ebola outbreak in West Africa was the world’s deadliest to date, infecting an estimated 28,000 people and killing more than 11,000, according to the Centers for Disease Control and Prevention.

    The study was co-authored by Melinda Ng, Esther Ndungo, Rohit Jangra and Rohan Biswas, all at Albert Einstein; John Hawkins and Ann Demogines, all at University of Texas at Austin; Andrew Herbert, Ana Kuehne and Rebekah James, all at USAMRIID; Tabea Binger and Marcel Müller at University of Bonn Medical Center; Robert Gifford at University of Glasgow; Meng Yu and Lin-Fa Wang at Duke-NUS Graduate Medical School; Thijn Brummelkamp at Netherlands Cancer Institute; Christian Drosten at the German Centre for Infectious Diseases Research; and Jens Kuhn at the National Institutes of Health’s Integrated Research Facility at Fort Detrick.

    This research was supported by grants from National Institutes of Health, the Defense Threat Reduction Agency, European Union FP-7 Antigone, the EBOKON Project, and the National Research Foundation Singapore.

    See the full article here .

    If you want to help in the fight against Ebola, join World Community Grid [WCG]and attach to the Outsmart Ebola Together project running at the Scripps Institute. WCG runs on BOINC software from UC Berkeley.

    WCGLarge

    Outsmart Ebola Together

    Scripps

    BOINC WallPaper

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    U Colorado Campus

    As the flagship university of the state of Colorado, CU-Boulder is a dynamic community of scholars and learners situated on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions belonging to the prestigious Association of American Universities (AAU) – and the only member in the Rocky Mountain region – we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

    CU-Boulder has blossomed in size and quality since we opened our doors in 1877 – attracting superb faculty, staff, and students and building strong programs in the sciences, engineering, business, law, arts, humanities, education, music, and many other disciplines.

    Today, with our sights set on becoming the standard for the great comprehensive public research universities of the new century, we strive to serve the people of Colorado and to engage with the world through excellence in our teaching, research, creative work, and service.

     
  • richardmitnick 9:22 pm on December 16, 2015 Permalink | Reply
    Tags: , BOINC, , ,   

    From Mapping Cancer Markers at WCG: “Working to detect lung and ovarian cancers before they start” 

    New WCG Logo

    15 Dec 2015
    The Mapping Cancer Markers research team

    Summary
    Recent stages of the Mapping Cancer Markers (MCM) project have illuminated the protein-protein interactions and biological pathways involved in lung cancer, and have also suggested surprising results about its biomarkers. Once this current stage is complete, MCM will transition to analyzing ovarian cancer. Thanks to your help, we are making discoveries and helping the international research community. Dr. Jurisica, in particular, is one of the most frequently cited researchers worldwide.

    Third stage of lung cancer analysis underway

    In our previous update, we announced a second, targeted stage of lung cancer signature discovery. We have since moved to a new, third stage in lung cancer analysis: targeting high-scoring, uncorrelated biomarkers. These different stages are all part of an overall effort to understand lung cancer signatures. The first stage surveyed possible lung cancer signatures drawn from the complete set of biomarkers in our lung cancer dataset. The statistics gathered in this first stage were used to narrow the list of biomarkers to explore in subsequent stages. The second and third stages explore lung cancer signatures drawn from small sets of high-performing signatures, chosen by two different methods. In the second stage, we focused on a 1% subset of biomarkers, selected by the frequency with which each appeared in high-scoring signatures from the initial stage. In the third stage, we selected a different subset of biomarkers that are both high-scoring and largely uncorrelated to one another.

    Correlation is a measure of information shared between two data sources. Two biomarkers are correlated if they exhibit similar patterns in the cancer dataset. For example, two correlated genes might show high activity in one set of tumour samples, low activity in a second set, and average activity in a third. Including two highly-correlated biomarkers in the same signature can reduce the quality of the signature, because they would be contributing redundant information to the signature. For a fixed-size signature, a redundant biomarker would potentially displace another biomarker that has different information content.

    As an analogy, consider the information contained in a small library of textbooks. Say there are three books, A, B, and C. If A and B are two copies of the same textbook, one of them is redundant. Removing B from the library would not change the information contained in the library, and replacing B with a different textbook (D), would increase the information in the library. If A and B were similar, but not identical books (e.g., two books on introduction to molecular biology written by different authors), there would still be some overlap in the texts, and a possible advantage to replacing B with D.

    Signature performance

    Because the target biomarkers in this third stage were selected to be minimally inter-correlated, every signature should be free of redundant information. We therefore hypothesized that signatures in the third stage would perform better on average than those in the second stage. Figure 1 shows the surprising results: second stage signatures (potentially containing correlated biomarkers) outperformed those from the third stage. We are analysing these results further, to determine the main reasons for the performance difference.

    2
    Figure 1. Distribution of signature scores for second (black) and third stage (blue) signatures. As expected, larger signatures generally outperform smaller. Surprisingly, second stage signatures outperform third stage on average.

    Size effects on biomarker rank in top signatures

    Larger signatures (i.e., signatures containing more biomarkers) incorporate more information and can potentially offer better accuracy, but are more complex and expensive to implement in the clinic. All three stages of MCM thus far have explored lung cancer signatures of multiple sizes. For each signature size we considered, the target biomarker subsets for the second stage were chosen separately, based on statistics from the first stage. The set of biomarkers selected for the third is fixed across all signature sizes. This fixed set allows us to compare the effects of signature size on each biomarker’s frequency in high-scoring signatures. Figure 2 shows the frequency change when moving from 10 biomarkers per signature to 20. Each dot in the graph represents a biomarker. The X axis represents the frequency with which biomarkers appear in size_10 signatures. The Y axis indicates frequency in size_20 signatures. Note that the biomarkers change in rank but are generally correlated. Size_10 signatures show greater biomarker frequency spread: some have relatively high frequency, and many are low-frequency. The biomarker frequencies in larger (size_20) signatures are more even.

    Biomarker pairs as protein interactions?

    We applied and extended the analysis of biomarker pairs described in the August 2015 update to early results from third stage data, looking specifically for pairs of biomarkers in both the second and third stages that appear surprisingly frequently in the highest-scoring lung cancer signatures. When two genes or proteins appear in signatures together with greater frequency than expected randomly, we predict a stronger cancer-related connection (interaction).

    We searched for any known connections (interactions) in The Integrated Interactions Database (IID), a database of known and predicted protein-protein interactions created by our lab [1]. We found several interactions in IID that mirror these cancer interactions, but the overlap was not statistically significant.

    2
    Figure 2. Biomarker frequencies in size_10 vs. size_20 signatures. Points to the left of the diagonal line represent biomarkers occurring more frequently in size_20 signatures. Note the overall correlation in ranks between sizes, but greater variation in frequencies for shorter signatures.

    Pathway enrichment in second and third stage targets

    We also took the genes selected for the second and third stages, and searched for them in a database of biological pathways. See Figure 3. We discovered our lists of genes were enriched (present in statistically significant numbers; p ≤ 0.01) in several pathways. See Table 1.

    Although our analysis is ongoing, we can see that two of the identified pathways are components of Mevalonate metabolism. Mevalonate pathways are already targets for many drugs such as statins and have been implicated as targets for treatment in lung cancer [2, 3]. Some of the downstream analysis will focus on how the signatures discovered by World Community Grid processing will ultimately connect to pathways and other research. We have used Mevalonate as an example, but there are many more that can be examined to assess the viability of our best signatures.

    Table 1. List of biological pathways enriched with MCM’s “discovered-pair” genes. P-values < 0.01 indicate statistical significance.
    Pathway Name p-value
    Mevalonate from acetyl CoA step 2 3 0.003236
    Biotinidase Deficiency metabolite pathway 0.004845
    Biotin Metabolism 0.004845
    Biotinidase Deficiency 0.004845
    Multiple carboxylase deficiency neonatal or early onset form
    0.004845
    Mevalonate biosynthesis 0.004845
    Synthesis of Ketone Bodies 0.006449
    Ketone Body Metabolism 0.008048
    Succinyl CoA 3 ketoacid CoA transferase deficiency
    0.008048
    Synthesis and Degradation of Ketone Bodies 0.01
    Fatty acid triacylglycerol and ketone body metabolism
    0.008892
    Vitamin H biotin metabolism 0.009643
    Dermatan sulfate degradation metazoa 0.009643

    4
    Figure 3. Biological pathways enriched by biomarker targets in the second (sizes 10 and 20) and third (all sizes) stages. Some pathways are common to all three.

    The third stage is nearly complete, and will be the final piece of MCM lung cancer analysis on World Community Grid before we switch to ovarian cancer.

    Ovarian cancer is a gynecologic malignancy that ranks 8th for incidence and 5th for death rate among all women’s cancers. The American National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program estimated 22,240 new cases and 14,030 deaths from ovarian cancer in 2013. Patients are usually diagnosed at an advanced stage (61% present metastasized cancer) and have poor prognosis (27.3 months for metastasized stage (SEER)).

    Ovarian cancer was chosen as our next dataset because of long experience with this disease in our own lab, and in those of collaborators. We look forward to using MCM to glean new insights into ovarian cancer.

    We expect the transition to ovarian cancer research to begin in early 2016, and do not anticipate any interruption in the flow of work units.

    Thank you to World Community Grid members

    We wish to thank World Community Grid members for their continued support and interest for this and other projects. Without you, this work would not be possible.

    References

    1. Kotlyar M, Pastrello C, Sheahan N, Jurisica I. Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res. 2015 Oct 29

    2. Hwa Young Lee, In Kyoung Kim, Hye In Lee, Hye Sun Kang, Chan Kwon Park, Jick Hwan Ha, Seung Joon Kim, Sang Haak Lee. Mevalonate pathway inhibitors as chemopreventive agents on lung cancer cell lines: p53 might be a potent regulator. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A48.

    3. Yano K. Lipid metabolic pathways as lung cancer therapeutic targets: a computational study. Int J Mol Med. 2012 Apr;29(4):519-29. doi: 10.3892/ijmm.2011.876. Epub 2011 Dec 30.

    Some additional relevant presentations and publications
    In several papers we have used strategies described above and protein interaction networks to identify better prognostic markers and new treatment options:

    Singh M, Garg N, Venugopal C, Hallett RM, Tokar T, McFarlane N, Arpin C, Page B, Haftchenary S, Todic A, Rosa DA, Lai P, Gómez-Biagi R, Ali AM, Lewis A, Geletu M, Mahendram S, Bakhshinyan D, Manoranjan B, Vora P, Qazi M, Murty NK, Hassell JA, Jurisica I, Gunning P, Singh SK. STAT3 pathway regulates lung-derived brain metastasis initiating cell capacity through miR-21 activation. Oncotarget (accepted June 30, 2015, ONC-2014-02546)
    Navab R, Strumpf D, To C, Pasko E, Kim KS, Park CJ, Hai J, Liu J, Jonkman J, Barczyk M, Bandarchi B, Wang YH, Venkat K, Ibrahimov E, Pham NA, Ng C, Radulovich N, Zhu CQ, Pintilie M, Wang D, Lu A, Jurisica I, Walker GC, Gullberg D, Tsao MS. Integrin a11b1 regulates cancer stromal stiffness and promotes tumorigenecity in non-small cell lung cancer, Oncogene, 2015. In press.
    Agostini M, Zangrando A, Pastrello C, D’Angelo E, Romano G, Giovannoni R, Giordan M, Maretto I, Bedin C, Zanon C, Digito M, Esposito G, Mescoli C, Lavitrano M, Rizzolio F, Jurisica I, Giordano A, Pucciarelli S, Nitti D. A functional biological network centered on XRCC3: a new possible marker of chemoradiotherapy resistance in rectal cancer patients, Cancer Biol Ther, 16(8):1160-71, 2015.
    Agostini M, Janssen KP, Kim LJ, D’Angelo E, Pizzini S, Zangrando A, Zanon C, Pastrello C, Maretto I, Digito M, Bedin C, Jurisica I, Rizzolio F, Giordano A, Bortoluzzi S, Nitti D, Pucciarelli S. An integrative approach for the identification of prognostic and predictive biomarkers in rectal cancer. Oncotarget. 2015. Sep 2.
    Stewart, E.L., Mascaux, C., Pham, N-A, Sakashita, S., Sykes, J., Kim, L., Yanagawa, N., Allo, G., Ishizawa, K., Wang, D., Zhu, C.Q., Li, M., Ng, C., Liu, N., Pintilie, M., Martin, P., John, T., Jurisica, I., Leighl, N.B., Neel, B.G., Waddell, T.K., Shepherd, F.A., Liu, G., Tsao, M-S. Clinical Utility of Patient Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma, J Clin Oncol, 33(22):2472-80, 2015.
    Camargo, J. F., Resende, M., Zamel, R., Klement, W., Bhimji, A., Huibner, S., Kumar, D., Humar, A., Jurisica, I., Keshavjee, S., Kaul, R., Husain, S. Potential role of CC chemokine receptor 6 (CCR6) in prediction of late-onset CMV infection following solid organ transplant. Clinical Transplantation, 2015. In press. doi: 10.1111/ctr.12531
    Fortney, K., Griesman, G., Kotlyar, M., Pastrello, C., Angeli, M., Tsao, M.S., Jurisica, I. Prioritizing therapeutics for lung cancer: An integrative meta-analysis of cancer gene signatures and chemogenomic data, PLoS Comp Biol, 11(3): e1004068, 2015.

    Integrative analyses also help provide better explanations of experimental results and more accurate models:

    Benleulmi-Chaachoua, A., Chen, L., Sokolina, K., Wong, V., Jurisica, I., Emerit, M.B., Darmon, M., Espin, A., Stagljar, I., Tafelmeyer, P., Zamponi, G.W., Delagrange, P., Maurice, P., Jockers, R. Protein interactome mining defines melatonin MT1 receptors as integral component of presynaptic protein complexes of neurons, Journal of Pineal Research, In press

    Some of this work was presented at multiple meetings and institutions: including keynotes at The 14th International Conference on Machine Learning and Applications and The American Society for Blood and Marrow Transplantation, Corporate Council Meeting; and invited highlight talks at Intelligent Systems for Molecular Biology Conference and Basel Computational Biology Conference.

    Media Coverage

    Also, for the second year in a row, Dr. Jurisica has been included in Thomson Reuters highly cited researcher list; Out of 108 in computer science and 3,125 world-wide in 21 fields of science.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    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.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BET!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

     
  • richardmitnick 11:34 am on November 25, 2015 Permalink | Reply
    Tags: , BOINC, ,   

    From AAAS: “Progress, but still much to do, AIDS report finds” 

    AAAS

    AAAS

    24 November 2015
    Jon Cohen

    1
    A cell infected with HIV. NIAID/Flickr (CC BY 2.0)

    Of the estimated 36.9 million HIV-infected people in the world, 70% live in sub-Saharan Africa. Of these, 49% do not know their HIV status and about 57% are not receiving antiretroviral drugs, according to a report released today by the Joint United Nations Programme on HIV/AIDS.

    The report, which arrives in the run-up to World AIDS Day on 1 December, celebrates the progress that has been made in getting antiretrovirals to 15.8 million people by June of 2015. But it also notes how far many countries are from meeting World Health Organization guidelines issued in September, which call for every infected person to receive treatment.

    The huge, ongoing push to start all infected people on antiretrovirals emerged from recent evidence that early initiation of treatment benefits the health of infected people, and also makes it extremely unlikely that they will transmit the virus (if they fully suppress their own infection). But in sub-Saharan Africa, the report notes, an estimated 68% of infected people have not suppressed their HIV levels.

    Some other statistical highlights from the report:

    About 36.9 million people globally were living with HIV at the end of 2014. (That is the midpoint within an estimated range of 34.3 million–41.4 million people).
    2 million (1.9 million–2.2 million) people became newly infected with HIV by the end of 2014. New HIV infections have fallen by 35% since 2000.
    1.2 million (980,000–1.6 million) people died from AIDS-related illnesses in 2014.
    As of June 2015, 15.8 million people living with HIV were accessing antiretroviral therapy, up from 13.6 million in June 2014.

    See the full article here .

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

    You can help in the fight against AIDS. Join World Community Grid (WCG) and attach to the Fight AIDS@home project. You will be donating unused computer capacity to process data in this compute intensive project. WCG runs on BOINC software from UC Berkeley.

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  • richardmitnick 6:00 pm on September 30, 2015 Permalink | Reply
    Tags: , BOINC, ,   

    From WCG’s FightAIDS@Home 

    New WCG Logo

    As part of Office of Science and Technology Policy ‪#‎WHCitSci‬ forum, we are very happy to announce the second phase of our FightAIDS@Home project.

    10 years of virtual simulations for FightAIDS@Home is considered the biggest drug docking experiment ever conducted. Now the challenge is to find the better drugs, fast.

    That’s where Phase 2 and you come in. Join FightAIDS@Home to help support pioneering HIV research.

    1
    Model of a complete HIV Virion with all of the component molecules

    Summary
    The team behind FightAIDS@Home is launching Phase 2 of the project, putting to use a more accurate simulation tool to help them determine which of the Phase 1 results merit further investigation. Phase 2 will also be applying this analysis technique at an unprecedented scale, which if proven successful, can benefit medical research not only for HIV but many other diseases as well.

    There have been some amazing advances in the fight against the human immunodeficiency virus (HIV), including treatments that have improved and extended millions of lives. But the fight continues – HIV is continually mutating, and as it does it evolves resistance to existing treatments. With tens of millions of people currently living with HIV, and millions more infected every year, the search for more effective HIV treatments is as critical as ever. Our team is therefore launching a new phase of HIV research to build on the success of the first phase and more accurately analyze the most promising drug candidates we’ve identified so far.

    For almost a decade, FightAIDS@Home has contributed to this fight by exploring different ways of disabling the virus. World Community Grid members have provided my team an unprecedented amount of computing power, enabling us to investigate a huge number of potential cures. To date, volunteers have performed over 20 billion comparisons between candidate chemicals and different binding sites on the virus. Along the way, our team has improved the tools used in the fight, by developing – and validating – software tools to simulate chemical binding, and discovering new potential binding sites for drugs to attack. These tools have even supported other medical research efforts, both on World Community Grid and elsewhere.

    The massive success of FightAIDS@Home has also generated a new challenge: thousands of potential ‘hits’ (chemicals that might form the basis of effective drugs) – a handful of which we’re synthesizing for additional testing. But because there are so many, it is prohibitively expensive and time consuming to synthesize and lab test all of those chemicals. The project now needs a new computational method to double-check the promising Phase 1 results, and ensure that only the most thoroughly vetted and probable candidate compounds proceed for further investigation. Phase 2 of FightAIDS@Home will address both of these goals: refining the Phase 1 results and validating the technology needed to make more accurate simulations.

    Specifically, Phase 2 uses a new analysis technique called BEDAM (Binding Energy Distribution Analysis Method), which is implemented using software called Academic IMPACT developed by our collaborators at Temple University. BEDAM has proven effective at carrying out more accurate simulations in computational contests, but thanks to World Community Grid volunteers, we now have an opportunity to apply it to analyze molecules at an unprecedented scale. This is important because if successful, these techniques can be applied to other drug discovery searches beyond HIV.

    3
    Collaborating labs for the HIVE Center. Prof. Art Olson directs the Center and collaborators include Prof. Ron Levy, who is partnering with the FightAIDS@Home project.

    Phase 2 is more radical than its name suggests – World Community Grid volunteers have the opportunity to help us validate a new promising research paradigm that can help the search for treatments for many diseases, not just HIV. It’s only because of the commitment shown by volunteers that FightAIDS@Home has been able to accomplish so much thus far. We hope we can count on your continued support as we continue this important journey.

    To contribute to FightAIDS@Home – Phase 2, join World Community Grid, or if you are already a volunteer, make sure the project is selected on your My Projects page.

    See the full article here.

    Please help promote STEM in your local schools.
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    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    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.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
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