Content with tag covid-19 .

Combination of XSEDE, other resources allows team to span time scales in simulating dagger-like microbe-killing molecule

By Ken Chiacchia, Pittsburgh Supercomputing Center

Assembly of cell membrane components (red) and human beta-defensin type 3 (blue) from first principles. As the simulation plays out, the membrane components form a double-layered membrane, seen side-on, and the peptide binds to it.

 

Medical science is in a race to develop new and better antimicrobial agents to address infection and other human diseases. One promising example of such agents is the beta-defensins. These naturally occurring molecules stab microbes' outer membranes, dagger-like, causing their contents to spill out. A scientist at Tennessee Tech University used the XSEDE-allocated Bridges platform at PSC, as well as the D .E. Shaw Research (DESRES) Anton 2 system hosted at PSC, in a "one-two" simulation that shed light on a beta-defensin's initial binding to a microbial membrane. The work promises clues to agents that can better destroy microbes with membranes.

Molecular dynamics simulations of human beta-defensin type 3 (red and blue) in wildtype (left) and analog (right) forms binding to a simulated cell membrane (green). In both cases the two loops (R36 and L21) stick to the membrane, but in the analog form the "head" of the molecule (I2) sticks as well.  Reprinted in part with permission from The Journal of Physical Chemistry B. © 2020, American Chemical Society.

Why It's Important

Living in a post-pandemic world, it's hardly necessary to point out how important new antimicrobial agents can be for treating afflictions from drug-resistant bacterial infections to COVID-19. One promising avenue of research focuses on the beta-defensins, a family of small protein-like peptides. These molecules, which consist of a chain of amino acids, are naturally produced by the body to kill bacteria. Better, since intact cell membranes are so fundamental to survival, bacteria can't become resistant to this kind of attack. Beta-defensins can also suppress some viruses that have membranes, such as HIV. Engineered versions of beta-defensin may also be able to attack the Coronavirus. Scientists would like to know more about how the beta-defensins work. Such information could help them to design both new antimicrobial agents and drugs that help the natural versions of these peptides work better.

"My lab works on the defensins, which work by first crossing the microbial lipid membrane. By breaking the lipid membrane, they cause leaking of the microbial contents. Our goal is to understand defensins' structure, their dynamics, and the functional relationship between them."—Liqun Zhang, Tennessee Tech

Beta-defensins work like little daggers, stabbing their way into the membrane of a microbe and spilling its contents so it can no longer infect healthy cells. But these peptides work in a changing environment. The conditions in the body range from oxygen-rich oxidizing conditions surrounding cells, for example, in the lungs to oxygen-poor reducing conditions in cells in the intestines. This causes important changes in the folding of a beta-defensin peptide such as human beta-defensin 3. The amino-acid chain in this beta-defensin's wild-type form is crosslinked to itself in three places via disulfide bonds. These links form in oxidizing conditions and break in reducing conditions. Scientists have long wondered how, and whether, beta-defensin can still destroy microbes in both forms.

To shed light on this question, Liqun Zhang at Tennessee Tech University found she needed to combine the complementary powers of the XSEDE-allocated Bridges platform at PSC and the DESRES Anton 2 supercomputer hosted at PSC.

How XSEDE Helped

As a first step, Zhang simulated the equilibrated form of human beta-defensin type 3. This consists of starting with the peptide's chain in a disordered tangle and using the rules of chemical interaction to let that chain find the combination of twists and turns that it naturally settles into. Zhang found Bridges to be a great tool for this step. The National Science Foundation-funded platform's massive computational abilities in both large memory and computational efficiency allowed her to simulate the initial 20 to 500 nanoseconds—billionths of a second—needed for the chain to find this lowest-energy form.

To simulate beta-defensin sticking to the membrane, though, she needed a much longer simulation—5 to 7.5 microseconds (millionths of a second), over 10 times longer. To perform this simulation, she used Anton 2, which is made available to PSC without cost by DESRES and supported through operational funding from the National Institutes of Health. Anton 2 is a highly specialized supercomputer designed and developed by DESRES that greatly accelerates such molecular dynamics simulations. Because of its specialized hardware and software, Anton 2 can perform simulations nearly two orders of magnitude longer in a given length of real time than a general-purpose supercomputer. While not an XSEDE system, Anton 2's location at XSEDE-member PSC and the common support staff was a big help to her in making use of both supercomputers.

"The combination is necessary. Bridges can equilibrate a system for maybe 20 to 500 nanoseconds. Then we can move to Anton for a long time span. We appreciate the computer resources that PSC and XSEDE offer; without their support, it's hard for me to imagine how we could finish the work."—Liqun Zhang, Tennessee Tech

Zhang simulated both the disulfide-crosslinked wild-type peptide and the uncrosslinked analog version of the peptide as it interacted with a virtual membrane typical of bacteria. In the initial Bridges simulations, she found that the wild-type version is much more rigid. Its crosslinks hold its shape more firmly than the analog version, which because of the lack of crosslinks is more flexible. The Anton 2 simulations showed an interesting difference that stems from this difference in flexibility. Two loops of the peptide chain initially stick to the membrane in both versions. But the analog version is flexible enough for an additional region, the "head" of the peptide, also to stick. Zhang reported her results in the Journal of Physical Chemistry in February, 2020.

It isn't yet clear what the effects of this different way of initial binding to the membrane may mean for beta-defensin's ability to destroy microbes. An important next step will be for Zhang to simulate the actual insertion of the peptide into the membrane and the disruption of the membrane. Another important step will be for Zhang's colleagues to test her predictions on real peptides in the lab, verifying the results and in turn uncovering details she can use to create better simulations. Ultimately, she hopes that these simulations will offer clues for designing drugs to combat microbes that cause disease. Zhang and her colleagues also plan to design beta-defensin-based small antimicrobial peptides to combat Coronavirus.

This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges system, which is supported by NSF award number ACI-1445606, at PSC. Anton 2 computer time was provided by PSC through Grant R01GM116961 from the National Institutes of Health. The Anton 2 machine at PSC was generously made available without cost by D. E. Shaw Research. Some of the short-term simulations and analysis were carried out at the high performance computers at Tennessee Technological University.

You can read Zhang's Journal of Physical Chemistry article here.

 

 

 

 

 

At a Glance:

  • Medical science is in a race to develop new and better antimicrobial agents

  • The beta-defensins are naturally occurring molecules that stab microbes' outer membranes, destroying them

  • A Tennessee Tech University team used the XSEDE-allocated Bridges platform at PSC, as well as the D. E. Shaw Research Anton 2 system hosted at PSC, in a "one-two" simulation of beta-defensin's initial binding to a microbial membrane

  • The work promises clues to agents that can better destroy microbes with membranes, such as bacteria and SARS-CoV-2


Catholic University of America researcher uses XSEDE resources to fast-track passive immunity 

by Faith Singer-Villalobos, Texas Advanced Computing Center (TACC)

Structural model of SARS-CoV-2 infection. This structural model was built with UCSF Chimera using high-performance computers (Bridges Large and Frontera). The model shows 16 viruses, with the spike proteins shown in green (PDB ID: 6VSB) and an actual lipid bilayer membrane, with ACE2 dimers shown in magenta. All these structures are at atomic resolution. The length of the membrane is approximately 1 micrometer. Credit: Victor Padilla-Sanchez, The Catholic University of America

 

With millions of COVID-19 cases reported across the globe, people are turning to antibody tests to find out whether they have been exposed to the coronavirus that causes the disease. But what are antibodies? Why are they important? If we have them, are we immune to COVID-19? And if not, why not?

Antibody tests look for the presence of antibodies, which are specific proteins made in response to infections. Antibodies are disease specific. For example, measles antibodies will protect you from getting measles if you are exposed to it again, but they won't protect you from getting mumps if you are exposed to mumps.

"Antibodies are important because they prevent infection and heal patients affected by diseases," said Victor Padilla-Sanchez, a researcher at The Catholic University of America in Washington D.C. "If we have antibodies, we are immune to disease, as long as they are in your system, you are protected. If you don't have antibodies, then infection proceeds and the pandemic continues."                

Victor Padilla-Sanchez, Researcher, The Catholic University of America

This form of foreign-antibody-based protection is called passive immunity — short-term immunity provided when a person is given antibodies to a disease rather than producing these antibodies through their own immune system.

"We're at the initial steps of this now, and this is where I'm hoping my work might help," Padilla-Sanchez said. Padilla-Sanchez specializes in viruses. Specifically, he uses computer models to understand the structure of viruses on the molecular level and uses this information to try to figure out how the virus functions.   

For his research, Padilla-Sanchez relied on supercomputing resources allocated through the Extreme Science and Engineering Discovery Environment (XSEDE). XSEDE is a single virtual system funded by the National Science Foundation used by scientists to interactively share computing resources, data, and expertise.

Severe acute respiratory syndrome (SARS) was the first new infectious disease identified in the 21st century. This respiratory illness originated in the Guangdong province of China in November 2002. The World Health Organization identified this new coronavirus (SARS-CoV) as the agent that caused the outbreak.

Now we're in the middle of yet another new coronavirus (SARS-CoV-2), which emerged in Wuhan, China in 2019. COVID-19, the disease caused by SARS-CoV-2, has become a rapidly spreading pandemic that has reached most countries in the world. As of July 2020, COVID-19 has infected more than 15.5 million people worldwide with more than 630,000 deaths.

To date, there are not any vaccines or therapeutics to fight the illness.

Since both illnesses (SARS-CoV and SARS-CoV-2) share the same spike protein, the entry key that allows the virus into the human cells, Padilla-Sanchez's idea was to take the antibodies found in the first outbreak in 2002 — 80R and m396 — and reengineer them to fit the current COVID-19 virus.

A June 2020 study in the online journal, Research Ideas and Outcomes, describes efforts by Padilla-Sanchez to unravel this problem using computer simulation. He discovered that sequence differences prevent 80R and m396 from binding to COVID-19.

Structural analysis of SARS-CoV spike glycoprotein. In A the SARS-CoV spike protein (PDB ID: 6ACG) is shown bound to ACE2 (brown) and 80R antibody (cyan), superimposed on the same binding site. In B the spike protein is shown bound only to the 80R antibody (PDB ID: 2GHW), with the structural model of the RBD of the SARS-CoV-2 spike protein (magenta) containing the missing loops. This homology model served as the basis for the docking experiments. In C it is shown a spike colored by subunit and showing the glycans. There are only two possible glycans in RBD region at 331 and 343 and neither of these sites affect the 80R binding. Credit: Victor Padilla-Sanchez, Researcher, The Catholic University of America

"Understanding why 80R and m396 did not bind to the SARS-CoV-2 spike protein could pave the way to engineering new antibodies that are effective," Padilla-Sanchez said. "Mutated versions of the 80r and m396 antibodies can be produced and administered as a therapeutic to fight the disease and prevent infection."

His docking experiments showed that amino acid substitutions in 80R and m396 should increase binding interactions between the antibodies and SARS-CoV-2, providing new antibodies to neutralize the virus.

"Now, I need to prove it in the lab," he said.

The XSEDE-allocated Stampede2 and Bridges systems at the Texas Advanced Computing Center (TACC) and Pittsburgh Supercomputer Center supported the docking experiments, macromolecular assemblies, and large-scale analysis and visualization.

"XSEDE resources were essential to this research," Padilla-Sanchez said.

Docking interface between the modified 80R antibody and the RBD of the SARS-CoV-2 spike protein. The model shows the structural interface with the 80R antibody above and the RBD below. The seven substitutions in 80R are shown in magenta and RBD residues are shown in cyan. Notice how the substitutions in 80R allow new aromatic-aromatic interactions that improve binding to the RBD and are not present in wild type 80R. E484 is shown pointing towards the beta strand of 80R and a glycine substitution was therefore introduced to avoid clashes. Credit: Victor Padilla-Sanchez, Researcher, The Catholic University of America

He ran the docking experiments on Stampede2 using the Rosetta software suite, which includes algorithms for computational modeling and analysis of protein structures. The software virtually binds the proteins then provides a score for each binding experiment. "If you find a good docking position, then you can recommend that this new, mutated antibody should go to production."

TACC's Frontera supercomputer, the 8th most powerful supercomputer in the world and the fastest supercomputer on a university campus, also provided vital help to Padilla-Sanchez. He used the Chimera software on Frontera to generate extremely high-resolution visualizations. From there, he transferred the work to Bridges because of its large memory nodes.

"Frontera has great performance when importing a lot of big data. We're usually able to look at just protein interactions, but with Frontera and Bridges, we were able to study full infection processes in the computer," he said.

Padilla-Sanchez's findings will be tested in a wet lab. Upon successful completion of that stage, his work can proceed to human trials.

Currently, various labs across the world are already testing vaccines.

"If we don't find a vaccine in the near term we still have passive immunity, which can prevent infection for several months as long as you have the antibodies," Padilla-Sanchez said. "Of course, a vaccine is the best outcome. However, passive immunity may be a fast track in providing relief for the pandemic."

Acknowledgement 
Molecular graphics and analyses were performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.

At a Glance:

  • Passive immunity is short-term immunity provided when a person is given antibodies to a disease rather than producing these antibodies through their own immune system.
  • Victor Padilla-Sanchez at The Catholic University of America is researching an idea to fast-track passive immunity to COVID-19 using computer simulation.
  • Both illnesses -- SARS-CoV (2002) and SARS-CoV-2 (2019) -- share the same spike protein. He took the antibodies found in the first outbreak in 2002 — 80R and m396 — and re-engineered them to see if they would fit with the current virus, COVID-19.
  • He discovered that sequence differences prevent 80R and m396 from binding to COVID-19. However, his in silico experiments showed that amino acid substitutions in 80R and m396 should increase binding interactions between the antibodies and COVID-19, providing new antibodies to neutralize the virus.
  • XSEDE resources Stampede2 (TACC) and Bridges (PSC) supported the docking experiments, macromolecular assemblies, and large-scale analysis and visualization.

COVID-19 Response: To our valued stakeholders and XSEDE collaborators,
By now you have received a flurry of communication surrounding the ongoing COVID-19 pandemic and how various organizations are responding, and XSEDE is no exception. As XSEDE staff have transitioned out of their usual offices and into telecommuting arrangements with their home institutions, they have worked both to support research around the pandemic and to ensure we operate without interruption.


XSEDE provides large-scale compute infrastructure for the analysis of thousands of genomes

By Faith Singer-Villalobos, Texas Advanced Computing Center

 

Scientists don't know how SARS-CoV-2, the virus that causes COVID-19, will evolve, but they say it's not going away anytime soon.

Human coronaviruses were first identified in the mid-1960s and are named for the crown-like spikes on their surface. The current pathogen is new to the human population.  

About 100 organizations worldwide have already contributed genomic data to the study of the pandemic, mainly academic labs and genome sequencing facilities. Genomic data is critical because it helps identify how the virus is evolving, which can provide critical clues to how to stop it.  A number of these teams have experience with response efforts to rapidly ramp up genome sequencing as has been done in the past with HIV, Ebola, Zika, influenza, and Hepatitis C.

SARS-CoV-2 is different, however.

"The community wasn't expecting this much data this quickly," said Sergei Pond, a professor in Biology at Temple University in Philadelphia. "We're seeing the epidemic develop in real-time. This is a unique feature of the current outbreak. It has never happened before."

Sergei Pond, Professor in Biology, Temple University

Pond and his colleague, Anton Nekrutenko of Penn State, are collaborating on the Galaxy project, one of the world's largest, most successful, web-based bioinformatics platforms. More than 30,000 biomedical researchers run approximately 500,000 computing jobs a month via the platform.

The researchers perform the majority of their parallel processing and analyses on the XSEDE-allocated Stampede2 (TACC) and Jetstream (IU/TACC) supercomputers using parallel processing and big data analytics. In addition, Galaxy employs the Bridges (PSC) platform, also an XSEDE-allocated resource, for assembly jobs that require large amounts of shared memory. XSEDE awards supercomputer resources and expertise to researchers and is funded by the National Science Foundation.

"For us, the open public resources on XSEDE are a way to show the value of a public cloud which is built specifically for research," Nekrutenko said. "We're enabling anyone in the world to do analysis using proven tools and robust workflows. We think XSEDE-allocated resources through TACC and PSC are ideal platforms for doing this," Nekrutenko said. Nekrutenko and his close collaborator, James Taylor, started Galaxy in 2005 at Penn State.

Taylor, the Ralph S. O'Connor Professor of Biology and Computer Science at Johns Hopkins University, passed away unexpectedly on April 2, 2020, at the age of 40. The official eulogy from the Galaxy Project is published here.

Anton Nekrutenko, Professor of Biochemistry and Molecular Biology, Penn State

Galaxy uses open source tools and public cyberinfrastructure for transparent, reproducible analyses of viral datasets. "We run hundreds of thousands of analyses per month, and we're spiking now in terms of usage and viral analyses," Nekrutenko said.

As a renowned expert in infectious disease evolution, Pond develops software tools and methods for people who do this research. Currently, he and Nekrutenko are working feverishly on several research projects funded by federal agencies to integrate the tools that Pond's lab has developed to bring them into Galaxy and to the broader community.

"We're well positioned to address the current issue with SARS-CoV-2 because we've been working in this domain for several years now," Pond said.

Pond's methods allow researchers to trace where viruses come from and how they evolve. He developed a widely used set of tools called HyPhy, specifically for selection analysis in infectious diseases.

With Galaxy and HyPhy working together, researchers can perform robust, reproducible analysis of SARS-CoV-2 genomic sequences. 

James Taylor (pictured) along with Anton Nekrutenko started Galaxy in 2005 at Penn State. Taylor, the Ralph S. O'Connor Professor of Biology and Computer Science at Johns Hopkins University, passed away unexpectedly on April 2, 2020, at the age of 40.

Nekrutenko also leads an NIH grant that puts tools for HIV analyses into the Galaxy. "Conceptually, these are the same tools that you would use for studying SARS-CoV-2. We can essentially solve all genomic data analysis needs for the worldwide research community when it comes to SARS-CoV-2," Nekrutenko said.

In February 2020, there were 100-150 genomes of the virus available. In March, the number started growing exponentially, and it's getting faster because diagnostic and academic labs around the world are sequencing these genomes and depositing them into large central databases. "For all we know next week there could be 50,000 genomes," Pond said.

The goal is to decipher these data to understand in real-time whether there's anything unique happening with the virus before it impacts the course of the pandemic.

In the past — as with SARS, MERS, Ebola and Zika—many interesting analyses were performed after the outbreak ended. This was mostly because the outbreaks were contained before they became a pandemic, unlike what is happening currently. Also, until about five years ago, researchers didn't have the sequencing technology that they needed available.

"Now you have instruments that you can set up and run very quickly, and public infrastructure to do data analysis," Pond said. "This is all developing live. We're turning around the analysis as quickly as the data come in."

On the positive side, the SARS-CoV-2 virus is mutating more slowly than influenza because it has an enzyme that does proofreading during RNA synthesis and RNA replication. "What this means is that we should be able to design a successful vaccine that's fairly uniform," Pond said. "You could take a sequence from Japan and a sequence from Africa and they'll be very similar to each other, which means we can develop with a high degree of confidence a fairly reliable vaccine."

People are anticipating that SARS-CoV-2 may become a seasonal infection, which means scientists have to look for evolutionary changes and possibly design a new vaccine every season. Eventually, our immune systems will develop immunity in the host. But it takes time and passage through the population — taking months to years.

"What we're doing is the first step, which is generating the variation of the genome and finding the most important among all of these thousands of positions that we can look at. We're helping to focus the effort on where some of the interesting evolutionary dynamics might be taking place," Pond said.

Researchers know that the virus contains 30,000 base pairs — three times larger than influenza or HIV. "It's as large as a virus can get before it runs into fundamental constraints in molecular replication," Pond said. "It mutates slower compared to influenza or HIV, but mutates much faster than the genomes in mammals or bacteria simply because it goes through rapid replication cycles."

For Nekrutenko, Pond, and other collaborators who work on Galaxy, their idea is to enable researchers to perform these analyses regardless of locale. "For example, if someone in Africa or China or Brazil generates data sets, they can use Galaxy to perform the analysis in an established, standardized way for free. We're establishing a level playing field so that analyses performed by different labs are comparable," Nekrutenko said.

"Here we have a situation where we don't know what's going to happen," Pond said. "So, we're looking forward, trying to do predictive analysis with these data. It's very exciting for a scientist because it's a unique opportunity that's never occurred before. I feel like everyone has to contribute to the best of their ability. And providing data analytics is definitely something that has to be done."

 

 

 

At A Glance

  • The Galaxy project is one of the world's largest, most successful, web-based bioinformatics platforms. More than 30,000 biomedical researchers run approximately 500,000 computing jobs a month on the platform.
  • With regard to Covid-19, researchers Anton Nekrutenko (Penn State) and Sergiei Pond (Temple University) are deciphering a deluge of data to understand in real-time what's unique with the virus before it impacts the course of the pandemic.
  • The researchers perform the majority of their parallel processing and analyses on the XSEDE-allocated Stampede2 and Jetstream supercomputers located at TACC. In addition, Galaxy employs the XSEDE-allocated Bridges platform from PSC for genome assembly jobs that require large amounts of shared memory.
  • "We can essentially solve all genomic data analysis needs for the worldwide research community when it comes to SARS-CoV-2." Anton Nekrutenko, Penn State

Valued stakeholders and XSEDE collaborators,

By now you have received a flurry of communication surrounding the ongoing COVID-19 pandemic and how various organizations are responding, and XSEDE is no exception. As XSEDE staff have transitioned out of their usual offices and into telecommuting arrangements with their home institutions, they have worked both to support research around the pandemic and to ensure we operate without interruption. 

XSEDE is continuing normal operations with most of our staff working remotely, and we expect no interruptions to services or resources at this time. To stay abreast of changing conditions and important messages from our team, visit our COVID-19 information page here. Many of our staff are working remotely, but we do not anticipate any interruption in staff activity.

To lend further stability during this crisis, we are creating processes to track the status of collaborating institutions across the country so that we can adapt rapidly should the situation change.

As the situation develops and more information becomes available, we will share details through our normal communication channels, including this email list.

Thank you for your attention during this rapidly evolving situation.

XSEDE


 

XSEDE and the XRAS system have been called upon to connect researchers to an unprecedented nationwide network of supercomputing resources in response to the COVID-19 pandemic.

 

XSEDE and the XSEDE Resource Allocations System (XRAS) are proud to be contributing to the newly-announced COVID-19 HPC Consortium from The White House's Office of Science and Technology Policy, a private/government/academic partnership that seeks to expedite applications for advanced computing research to combat the COVID-19 pandemic.

Researchers who are interested in conducting this timely work are asked to submit research proposals to the COVID-19 Online Portal here, which is handled by the XRAS team.

You can read the full release below, and visit the dedicated COVID-19 HPC Consortium webpage here.

The COVID-19 High Performance Computing (HPC) Consortium

The COVID-19 High Performance Computing Consortium is a unique private-public effort spearheaded by the White House Office of Science and Technology Policy, the U.S. Department of Energy and IBM to bring together federal government, industry, and academic leaders who are volunteering free compute time and resources on their world-class machines.

 

Consortium partners include:

 

Industry

  • IBM

  • Amazon Web Services

  • Google Cloud

  • Microsoft

 

Academia

  • Massachusetts Institute of Technology

  • Rensselaer Polytechnic Institute

 

Department of Energy National Laboratories

  • Argonne National Laboratory

  • Lawrence Livermore National Laboratory

  • Los Alamos National Laboratory

  • Oak Ridge National Laboratory

  • Sandia National Laboratories

 

Federal Agencies

  • National Science Foundation

  • NASA

 

Researchers are invited to submit COVID-19 related research proposals to the consortium via this online portal, which will then be reviewed for matching with computing resources from one of the partner institutions. An expert panel comprised of top scientists and computing researchers will work with proposers to assess the public health benefit of the work, with emphasis on projects that can ensure rapid results.

Fighting COVID-19 will require extensive research in areas like bioinformatics, epidemiology, and molecular modeling to understand the threat we're facing and form strategies to address it. This work demands a massive amount of computational capacity. The COVID-19 High Performance Computing Consortium helps aggregate computing capabilities from the world's most powerful and advanced computers to help COVID-19 researchers execute complex computational research programs to help fight the virus.


Previous years' ECSS seminars may accessed through these links:

Content with tag covid-19 .

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