Tag: Folding@home

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Folding@Home: How You, and Your Computer, Can Play Scientist

by Alex Gardner

Greg Bowman kneels, working on a server.
Folding@home is led by Gregory Bowman, a Penn Integrates Knowledge Professor who has appointments in the Departments of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of Penn Medicine News)

Two heads are better than one. The ethos behind the scientific research project Folding@home is that same idea, multiplied: 50,000 computers are better than one.

Folding@home is a distributed computing project which is used to simulate protein folding, or how protein molecules assemble themselves into 3-D shapes. Research into protein folding allows scientists to better understand how these molecules function or malfunction inside the human body. Often, mutations in proteins influence the progression of many diseases like Alzheimer’s disease, cancer, and even COVID-19.

Penn is home to both the computer brains and human minds behind the Folding@home project which, with its network, forms the largest supercomputer in the world. All of that computing power continually works together to answer scientific questions such as what areas of specific protein implicated in Parkinson’s disease may be susceptible to medication or other treatment.

Led by Gregory Bowman, a Penn Integrates Knowledge professor of Biochemistry and Biophysics in the Perelman School of Medicine who has joint appointments in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science, Folding@home is open for any individual around the world to participate in and essentially volunteer their computer to join a huge network of computers and do research.

Using the network hub at Penn, Bowman and his team assign experiments to each individual computer which communicates with other computers and feeds info back to Philly. To date, the network is comprised of more than 50,000 computers spread across the world.

“What we do is like drawing a map,” said Bowman, explaining how the networked computers work together in a type of system that experts call Markov state models. “Each computer is like a driver visiting different places and reporting back info on those locations so we can get a sense of the landscape.”

Individuals can participate by signing up and then installing software to their standard personal desktop or laptop. Participants can direct the software to run in the background and limit it to a certain percentage of processing power or have the software run only when the computer is idle.

When the software is at work, it’s conducting unique experiments designed and assigned by Bowman and his team back at Penn. Users can play scientist and watch the results of simulations and monitor the data in real time, or they can simply let their computer do the work while they go about their lives.

Read the full story at Penn Medicine News.

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Gregory Bowman Appointed Penn Integrates Knowledge University Professor

by Ron Ozio

Greg Bowman
Gregory Bowman, the Louis Heyman University Professor, has joint appointments in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of School of Engineering and Applied Sciences)

Gregory R. Bowman, a pioneer of biophysics and data science, has been named a Penn Integrates Knowledge University Professor at the University of Pennsylvania. The announcement was made today by President Liz Magill and Interim Provost Beth A. Winkelstein.

Bowman holds the Louis Heyman University Professorship, with joint appointments in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science.

His research aims to combat global health threats such as COVID-19 and Alzheimer’s disease by better understanding how proteins function and malfunction, especially through new computational and experimental methods that map protein structures. This understanding of protein dynamics can lead to effective new treatments for even the most seemingly resistant diseases.

“Delivering the right treatment to the right person at the right time is vital to sustaining—and saving—lives,” Magill said. “Greg Bowman’s novel work holds enormous promise and potential to advance new forms of personalized medicine, an area of considerable strength for Penn. A gifted researcher and consummate collaborator, we are delighted to count him among our distinguished PIK University Professors.”

Bowman came to Penn from the Washington University School of Medicine’s Department of Biochemistry and Molecular Biophysics, where he served on the faculty since 2014. He previously completed a three-year postdoctoral fellowship at the University of California, Berkeley.

Bowman’s research utilizes high-performance supercomputers for simulations that can better explain how mutations and disease change a protein’s functions. These simulations are enabled in part through the innovative Folding@home project, which Bowman directs. Folding@home empowers anyone with a computer to run simulations alongside a consortium of universities, with more than 200,000 participants worldwide.

His research has been supported by the National Science Foundation, National Institutes of Health, National Institute on Aging, and Packard Foundation, among others, and he has received a CAREER Award from the NSF, Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and Thomas Kuhn Paradigm Shift Award from the American Chemical Society. He received a Ph.D. in biophysics from Stanford University and a B.S. (summa cum laude) in computer science, with a minor in biomedical engineering, from Cornell University.

“Greg Bowman’s highly innovative work,” Winkelstein said, “exemplifies the power of our interdisciplinary mission at Penn. He brings together supercomputers, biophysics, and biochemistry to make a vital impact on public health. This brilliant fusion of methods—in the service of improving people’s lives around the world—will be a tremendous model for the research of our faculty, students, and postdocs in the years ahead.”

The Penn Integrates Knowledge program is a University-wide initiative to recruit exceptional faculty members whose research and teaching exemplify the integration of knowledge across disciplines and who are appointed in at least two schools at Penn.

The Louis Heyman University Professorship is a gift of Stephen J. Heyman, a 1959 graduate of the Wharton School, and his wife, Barbara Heyman, in honor of Stephen Heyman’s uncle. Stephen Heyman is a University Emeritus Trustee and member of the School of Nursing Board of Advisors. He is Managing Partner at Nadel and Gussman LLC in Tulsa, Oklahoma.

This story originally appeared in Penn Today.

Dr. Bowman is Penn Bioengineering’s third PIK Professor after Kevin Johnson and Konrad Kording. See the full list of University PIK Professors here.

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More Cancers May be Treated with Drugs than Previously Believed

by Alex Gardner

3D illustration of cancer cells
nucleus and membrane of pathogen micro organisms in blue background

Up to 50 percent of cancer-signaling proteins once believed to be immune to drug treatments due to a lack of targetable protein regions may actually be treatable, according to a new study from the Perelman School of Medicine at the University of Pennsylvania. The findings, published this month in Nature Communications, suggest there may be new opportunities to treat cancer with new or existing drugs.

Researchers, clinicians, and pharmacologists looking to identify new ways to treat medical conditions—from cancer to autoimmune diseases—often focus on protein pockets, areas within protein structures to which certain proteins or molecules can bind. While some pockets are easily identifiable within a protein structure, others are not. Those hidden pockets, referred to as cryptic pockets, can provide new opportunities for drugs to bind to. The more pockets scientists and clinicians have to target with drugs, the more opportunities they have to control disease.

The research team identified new pockets using a Penn-designed neural network, called PocketMiner, which is artificial intelligence that predicts where cryptic pockets are likely to form from a single protein structure and learns from itself. Using PocketMiner—which was trained on simulations run on the world’s largest super computer—researchers simulated single protein structures and successfully predicted the locations of cryptic pockets in 35 cancer-related protein structures in thousands of areas of the body. These once-hidden targets, now identified, open up new approaches for potentially treating existing cancer.

What’s more, while successfully predicting the cryptic pockets, the method scientists used in this study was much faster than previous simulation or machine-learning methods. The network allows researchers to nearly instantaneously decide if a protein is likely to have cryptic pockets before investing in more expensive simulations or experiments to pursue a predicted pocket further.

“More than half of human proteins are considered undruggable due to an apparent lack of binding proteins in the snapshots we have,” said Gregory R. Bowman, PhD, a professor of Biochemistry and  Biophysics and Bioengineering at Penn and the lead author of the study. “This PocketMiner research and other research like it not only predict druggable pockets in critical protein structures related to cancer but suggest most human proteins likely have druggable pockets, too. It’s a finding that offers hope to those with currently untreatable diseases.”

Read the full story in Penn Medicine News.