NASA is using and developing its supercomputing power to help crack some of the most pressing questions surrounding COVID-19, from basic science on how the virus interacts with cells in the human body to genetic risk factors to screening for potential therapeutic drugs.
NASA has joined a consortium of institutions that is pairing up supercomputing resources with proposals for using high-end computing power for COVID-19 studies. The effort was organized by the White House Office of Science and Technology Policy and includes industry partners IBM, Hewlett Packard Enterprise, Amazon, Microsoft and others, as well as the Department of Energy’s National Labs, the National Science Foundation, and many universities. The consortium is supporting 64 projects and is open to new proposals.
Supercomputers are suited for processing large amounts of data. For NASA’s usual projects this means simulating the movements of air masses and water around the planet to study Earth’s climate, hunting for exoplanets, studying the behavior of black holes, or designing aeronautic or aerospace vehicles. Each piece of these very large puzzles is guided by certain physical and chemical laws in their interactions and relationships with other components. Zooming in to the atomic level to study the coronavirus is no different. Each molecule and cell moves and reacts based on physics and chemistry, which makes simulation a powerful tool for understanding the coronavirus.
Ames’ supercomputing power is being drafted to look at the genetic risk factors associated with COVID-19 patients developing Acute Respiratory Distress Syndrome, or ARDS. ARDS is a complication of COVID-19 that occurs when the disease causes fluid to build up in the lungs, which often requires a ventilator to help patients breathe.
The same NASA researchers who apply their expertise to studying how biology changes in space are studying these genetic variations. They are partnering with health care provider, Northern California Kaiser Permanente, for their COVID-19 study.
he Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts, is harnessing the Ames supercomputer to train an algorithm to detect potential molecules that could inhibit the novel coronavirus from attacking cells. The machine learning algorithm will be taught with 300,000 molecules that laboratory experiments have shown to be active or not against SARS, the related coronavirus that had a deadly outbreak in 2003.
The MIT software that will run on the supercomputer produces new 3D models of the molecules from their known chemical compositions. This allows the computer to represent the molecule more accurately, so that, when presented with a new molecule it hasn’t seen before, it can better predict whether it will bind with the novel coronavirus.
Understanding how the novel coronavirus uses its spiked proteins on its exterior to enter cells will better help researchers to determine what drugs or therapies may be most effective against it. Once in the cell, the novel coronavirus hijacks the cell’s functions to replicate itself, so more virus can spread through the body.
“It’s a fairly complex process, because the spike protein is in what’s called a pre-fusion state, the state before it fuses to specific receptor molecules on the cell membrane,” said principal investigator Michael Peters from Virginia Commonwealth University, in Richmond. Like many viruses, it goes through a transformation process, and the team wants to understand in detail how the spike protein behaves, he said.
Peters and his colleagues are using the Ames supercomputing resources to simulate each complex molecule at the atomic level, that is, the carbon, oxygen, nitrogen and hydrogen atoms that make up the molecules of the spike protein and its receptor. Following each atom’s movements in concert allows the study of conformational changes — changes in the overall molecule’s shape — that naturally take place with these complex molecules and their interactions.