TACC contributes to understanding the dynamics of SARS-COV-2 spike proteins

For many people, COVID-19 has highlighted the role of science in fighting disease around the world. From the perspective of a young scientist, the thrill of discovering something no one has done before and producing answers that are badly needed in society is an experience unmatched.

“Biomedical systems research can be very complex and always exciting. But knowing that your findings can have a direct impact on a major global issue makes this work even more exciting and immensely rewarding,” said Genevieve Kunkel, graduate student at the Tarakanova Lab at the University of Connecticut (UConn). “I want to use traditional fundamentals of physics and engineering to understand disease. »

Graduate student Genevieve Kunkel (left) and Professor Anna Tarakanova of the Tarakanova Lab at the University of Connecticut (UConn)

In the summer of 2020, Kunkel and his colleague Mohammed Madani, another graduate student in the lab, began collecting information about the SARS-CoV-2 virus under the guidance of Professor Anna Tarakanova. Like everyone else in the world, they wanted to understand the virus as new variants began to emerge – Alpha, Delta, Omicron.

The graduate students are co-authors of a recent article in the Biophysical Journal published in November 2021. Other co-authors are Simon J. White, Paulo H. Verardi, and Anna Tarakanova. The research required massive supercomputing power, which the team drew on through the NSF-funded Extreme Science and Engineering Discovery Environment (XSEDE).

“We were trying to find a way to solve the dynamics of the protein in a fast and targeted way – by gaining a better understanding of the spike protein mutations that can be applied to more aggressive variants,” Kunkel said. “This is how we came to the process of ‘normal mode analysis’, which is the method we used to solve protein dynamics, which in turn allows us to identify dynamic domains – regions keys to spike protein function. We also looked at the thermal stability and longevity of the proteins. If we learn how to control these factors, we can provide insights for the design of future vaccines.”

“To assess the thermal stability of spike protein mutations in a fast and accurate way, we also built a machine learning-based tool using the XSEDE-allocated Stampede2 supercomputer to train our model,” Madani said. “Access to the supercomputer was essential for our work. These types of simulations would not be possible on our local machines.

why it matters

The results of this study allow researchers to make recommendations on the design of future SARS-CoV-2 spike protein variants for effective immunogens that trigger neutralizing antibodies to hamper virus activity. The integrated computational approach they used can be applied to optimize vaccine design and predict antibody responses of SARS-CoV-2 variants.

The researchers studied key regions associated with specific dynamic mechanisms – such as movement of the receptor-binding domain (RBD), a key component of the virus located on its spike protein that allows it to dock to cell receptors of the human body to enter these cells and trigger the infection.

“The mechanisms we saw from the project’s combined research offered insights into the types of mutations that could stabilize or destabilize certain regions of the spike protein to alter RBD movements so that antibodies can recognize it.” , Kunkel said. “This is important for identifying disease mechanisms in later variants or for vaccine design.”

Additionally, the methods used in this study – a combination of normal mode analysis (an approach to extracting the most biologically relevant motions undergone by molecules) and dynamic domain analysis – examine a large number of different variants at once.

“That was the key to the search,” Kunkel said. “It’s useful for the ongoing development of treatments because it allows researchers to quickly resolve and compare the different movements of many advanced protein variants, which is more essential now that we are dealing with many variants around the world. entire.”

How Supercomputing Helped

The researchers used XSEDE allocations on TACC’s Stampede2 supercomputer and the center’s Ranch data storage system for this study.

“When you’re looking at 10 to 20 or more proteins, it’s best to use a supercomputer to speed up the simulations,” Kunkel said. “Another component of the work, the Thermal Stability Predictor, was developed using Stampede2 – it’s a machine learning predictor, and we needed many core hours of computing power to train the model.

The Ranch storage system was used to archive each protein studied.

Anna Tarakanova, their teacher, said, “I started using XSEDE about 10 years ago as a graduate student at MIT. I use XSEDE all the time, first alone, then with my students. Most of the work I do wouldn’t be possible without XSEDE. It has been an extremely helpful resource.

Some of the main findings of the study are:

  • By comparing the dynamic signatures of different spike proteins, the researchers uncovered key differences between spike protein variants (natural variants and modified proteins used in vaccine design research). By identifying mutational effects on key functional regions, they began to understand how this research could be used to help create custom spike proteins for future immunogen design.
  • Researchers have developed a comprehensive antigenic map of the human body’s immune response, linking how the dynamic signatures of different spike protein variants coincide with key antibody binding regions. This will help scientists understand how effective a protein variant can be at neutralizing the virus. Until this body of research, there were no resources with comprehensive information on antigen binding analyzed as a function of protein dynamics.

Tarakanova concluded: “The computational methods we used are transferable and can be applied more broadly – ​​not just to SARS-CoV-2, but to any other type of virus that may arise in the future.”

This work was supported by the fast-start funding program from the Office of the Vice President for COVID-19 Research at the University of Connecticut. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation grant number ACI-1548562. XSEDE Stampede 2 and Ranch resources from the Texas Advanced Computing Center were used through grant TG-MCB180008.

Source: Faith Singer, Texas Advanced Computing Center – republished with permission

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