Rice’s web server helps identify Covid drug candidates

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HOUSTON – (November 1, 2021) – Researchers at Rice University have set up an online portal to help researchers screen for COVID-19 drug candidates that could attack specific proteins of the SARS-CoV-2 virus.

Lydia Kavraki, computer scientist at George R. Brown School of Engineering, and colleagues at the University of Houston, the University of Edinburgh, Scotland, and the Federal University of Ceará, Brazil, have put online a “user-friendly” web server that provides scientists with the ability to virtually select their drug candidates against protein binding pockets on the SARS-CoV-2 virus.

Better yet, the program incorporates what they say is an often overlooked factor in the computer models of these pockets: their flexibility.

The project, detailed in an open access article in Computers in biology and medicine, integrates models of three drug targets – the main protease (Mpro), RNA-dependent RNA polymerase (RdRp) and papain-like protease (PLpro) – for overall mooring through DINC-COVID.

The whole docking approach allows researchers to select candidates ligands (reactive molecules) against different conformations of SARS-CoV-2 proteins and their binding pockets. DINC-COVID then marks the successful binding of ligands.

DINC stands for “Docking INCrementally”, a protocol developed by the Kavraki laboratory in 2013 to speed up protein-peptide docking simulations that help researchers design drugs, vaccines, and other processes involving large ligands. a 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 is based on the “overwhelming 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, inactivate the virus.

The study also offers a literal twist, best represented by the main protease, a virus host site 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 host 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 provide are not static; they’re not a single conformation, ”he said. “We are using states to reflect the dynamics of this protein in a physiological environment.”

The team used several programs to reduce the sets of 100,000 possible conformations generated by a simulation of molecular dynamics, for example, to a set of representative conformations. This allows researchers to decouple generation of sets from docking in DINC-COVID, saving hours or days on complicated calculations.

“We think this 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 his 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 graduate student and co-lead author of the article. “When you try to find a drug to inhibit a virus, you are 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 happy with the community 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 that DINC-COVID will help shed light on the complex mechanisms of SARS-CoV-2 infection. “

Didier Devaurs, former Rice postdoctoral researcher and now research fellow at the University of Edinburgh, is co-lead author of the article. Geancarlo Zanatta, associate professor of physics at the Federal University of Ceará, is a corresponding author. Kavraki is Professor of Computer Science Noah Harding, 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), Cancer Prevention and Research Institute of Texas (RP170593), the National Institutes of Health (U01CA258512), and a National Library of Medicine Training Program Fellowship (T15LM007093-29) supported the research.

/ Public distribution. This material from the original organization / authors may be ad hoc in nature, edited for clarity, style and length. The views and opinions expressed are those of the author (s). here.


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