HOUSTON – (November 1, 2021) – Researchers at Rice University have introduced an online portal to help researchers screen for COVID-19 drug candidates that could attack specific proteins in the SARS-CoV-2 virus.
Lydia Kavraki, computer scientist at the George R. Brown School of Engineering, and his colleagues at the University of Houston, the University of Edinburgh, Scotland, and the Federal University of Ceará, Brazil, have released a “user-friendly” web server offering scientists the ability to screen virtually their drug candidates against known protein-binding pockets on the SARS-CoV-2 virus.
Even better, the program incorporates what they say is an often overlooked factor in computer models of these pockets: their flexibility.
The project, detailed in an open access article in Computers in biology and medicineincorporates models of three drug targets – major protease (Mpro), RNA-dependent RNA polymerase (RdRp), and papain-like protease (PLpro) – for ensemble docking via DINC-COVID.
The ensemble docking approach allows researchers to screen candidate ligands (reactive molecules) against different conformations of SARS-CoV-2 proteins and their binding pockets. DINC-COVID then marks the success of the ligands to bind.
DINC stands for “Docking INCrementally,” a protocol developed by Kavraki’s lab in 2013 to accelerate protein-peptide docking simulations that help researchers design drugs, vaccines, and other processes involving large ligands. An improved version led by Kavraki and Dinler Antunes, then a postdoctoral researcher in her lab and now an assistant professor at the University of Houston, appeared in 2017.
The new iteration builds on the “impressive number” of SARS-CoV-2 protein structures that have been resolved so far. Understanding these structures allows researchers to find binding partners that could, ideally, deactivate the virus.
The study also offers a literal twist, best represented by the main protease, a docking site on the virus that has received a lot of attention over the past 18 months. The researchers found that the Mpro site can dramatically distort its shape in response to binding, allowing it to accommodate a diverse set of potential ligands.
This malleability makes Mpro and other sites difficult to simulate, with a much higher computational cost, said Mauricio Rigo, postdoctoral researcher and co-author of Rice. “Unlike other servers, the proteins we make available are not static; they are not a single conformation,” he said. “We use states to reflect the dynamics of this protein in a physiological environment.”
The team used several programs to reduce sets of the 100,000 possible conformations generated by a molecular dynamics simulation, for example, to a set of representative conformations. This allows researchers to decouple ensemble generation from docking in DINC-COVID, saving hours or days of complicated calculations.
“We think it was the right way to go,” Rigo said. “Our tests of the algorithm gave us a good match with the experimental results.”
With Mpro, the team modeled sets of catalytic binding sites on PLpro and RdRp. For Mpro, they modeled its catalytic and allosteric binding sites, for a total of 12 sets.
“We chose them because they can be targeted by different drugs,” said Sarah Hall-Swan, Rice’s graduate student and co-lead author of the paper. “When you’re trying to find a drug to inhibit a virus, you’re going to look for the protein parts that are important for that virus to work and try to inhibit them.”
The lab is working to increase the number of sets available in DINC-COVID.
“We are very pleased with the community’s response to our work,” Kavraki said. “DINC-COVID has already been used by around 500 researchers in 16 different countries, while our old DINC web server has been accessed by 11,000 users. We hope DINC-COVID will help shed light on the complex mechanisms of SARS-CoV-2 infection. »
Didier Devaurs, a former Rice postdoctoral fellow and now a researcher at the University of Edinburgh, is co-lead author of the paper. Geancarlo Zanatta, associate professor of physics at the Federal University of Ceará, is a corresponding author. Kavraki is the Noah Harding Professor of Computer Science, Professor of Bioengineering, Mechanical Engineering, and Electrical and Computer Engineering, and Director of the Ken Kennedy Institute.
The National Science Foundation (2033262), the National Council for Scientific and Technological Development of Brazil (437373/2018-5), a scholarship from the University of Edinburgh and the Medical Research Council (MC_UU_00009/2), the Texas Institute for Cancer Prevention and Research (RP170593), the National Institutes of Health (U01CA258512), and a National Library of Medicine Training Program Fellowship (T15LM007093-29) supported the research.
Read the abstract at https://www.sciencedirect.com/science/article/pii/S001048252100737X#.
This press release can be viewed online at https://news.rice.edu/news/2021/rice-web-server-helps-identify-covid-19-drug-candidates.
Follow Rice News and Media Relations via Twitter @RiceUNews.
Kavraki Laboratory: https://profiles.rice.edu/faculty/lydia-e-kavraki
Department of Computer Science: https://csweb.rice.edu
George R. Brown School of Engineering: https://engineering.rice.edu
Pictures to download:
Rice University engineers are leading the development of a web server to help researchers identify drugs to treat COVID-19. Their use of a set of conformations allows researchers to account for protein flexibility in molecular docking studies. (Credit: Illustration by Mauricio Rigo/Rice University)
A Rice University web server can help researchers identify potential drugs to treat COVID-19. In the physiological environment, a protein is not a static entity, but can move and adopt a set of conformations. In this illustration, the main protease of SARS-CoV-2 is shown in green and the multiple conformations are shown in light gray. (Credit: Illustration by Mauricio Rigo/Rice University)
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Computers in biology and medicine
The title of the article
DINC-COVID: a web server for ensemble docking with flexible SARS-CoV-2 proteins
Publication date of articles