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Concentrated computing power against SARS-CoV-2

Jülich, 19 March2021 - The first vaccines authorised for use are raising hopes for an end to the pandemic. What is still missing, however, is an effective cure. In the European joint project EXSCALATE4CORONAVIRUS (E4C), scientists are searching for molecules that block central proteins of the coronavirus. In a recent publication – which resulted from an academic collaboration of scientists consisting of E4C, the “Human Brain Project” and other European research institutions – the team headed by Prof. Giulia Rossetti from Jülich reports a method to predict more precisely which molecules inhibit “Mpro”, the main protease of SARS-CoV-2.

The Jülich researchers use the high-performance computing power of Europe’s largest supercomputer centres, including the capacities of the Jülich Supercomputing Centre (JSC). With the help of the computers, they analyse, within weeks, the effect of millions of molecules against selected target structures of the virus. One promising target is the main 3C-like protease of the coronavirus, which is also called “Mpro” (short for “main protease”).

The virus can no longer reproduce if this protein-cleaving enzyme is inhibited. Since there is no human protease with similar characteristics, the scientists expect inhibitors against this main viral protease to be highly unlikely to be harmful to humans. Initially, researchers worldwide assumed rapid success. This was because the main proteases of the SARS-CoV coronavirus discovered in 2002 and the new SARS-CoV-2 are 96 per cent identical in their amino acid sequence. The sequences of the active sites, which are crucial for the function of the enzymes, are even 100 per cent identical. Surprisingly, however, molecules that successfully blocked the main protease of SARS-CoV had little or no effect on SARS-CoV-2.

“Proteins are no rigid entities,” explains Prof. Giulia Rossetti from the Jülich Institute of Neuroscience and Medicine, Computational Biomedicine, and the JSC. “They are three-dimensional, constantly in motion and extremely flexible. For example, a mutation far from the active site can nevertheless substantially alter its plasticity and binding properties.” Simulations of the two active sites indeed showed significant differences in their flexibility and in the dynamics of how they react to inhibitory molecules. Under certain conditions, for instance, the volume of the active site of SARS-CoV is larger than that of SARS-CoV-2 by 50 per cent. This affects the active site’s accessibility for inhibitors and their ability to successfully dock there.

In order to understand even more precisely which other factors influence the enzyme’s shape and function, the scientists, together with colleagues from the Heidelberg Institute for Theoretical Studies (HITS) and the Stockholm Royal Institute of Technology (KTH), investigated more than 30,000 possible spatial arrangements of its three-dimensional shape. “It’s like an animated film composed of many individual snapshots,” explains Rossetti. “This way, we calculated which molecules could theoretically fit into all these possible conformations.”

When searching for a therapeutic agent, researchers usually follow the lock-and-key principle: they take a library of known drugs and substances, i.e. possible keys, and run a computer test to see which molecule fits into the active site of the enzyme, the lock. “In the case of SARS-CoV-2, however, we have the phenomenon that the shape of the main protease turns out to be extremely flexible, i.e. the lock is constantly changing – depending on the ligands that bind here and several other factors such as temperature, pH level, biochemical environment and more,” says Jonas Goßen, lead author of the study and doctoral researcher at INM-9. Therefore, the team reversed the search and started “from the lock side”. “We calculated which numerous conformations the active site can take and what possible keys would have to look like in order to block it.” With this “blueprint”, the scientists then searched the substance libraries for suitable keys.

Prof. Giulia Rossetti adds: “With our advanced approach, we have succeeded in identifying two novel Mpro inhibitors through computer simulations alone. Tests on cell cultures have confirmed a moderate antiviral effect of the two substances – the plant flavonoid Myricetin and the agent Benserazide,” she says. “The key point of our work is that our method of simulation calculations can now be used to find precisely fitting inhibitors for proteins that have flexible properties similar to the main protease of the SARS-CoV-2 virus.” Whether the two substances will actually be suitable as therapeutic agents is uncertain. It usually takes several years before a potential active ingredient is developed into a drug. “With the new possibilities of computer-assisted modelling and ultra-fast virtual screening, however, we are already saving a tremendous amount of time in the search for potential candidates,” concludes Prof. Rossetti.

This research also demonstrates the value of interdisciplinary trans-European networks and infrastructures such as EXSCALATE4CORONAVIRUS and HBP when it comes to pooling expertise and resources and developing new approaches to active ingredients research. In this respect, follow-ups of such research will also make use of the Human Brain Project’s FENIX infrastructure.

Original publication: Jonas Gossen, Simone Albani, Anton Hanke, Benjamin P. Joseph, Cathrine Bergh, Maria Kuzikov, Elisa Costanzi, Candida Manelfi, Paola Storici, Philip Gribbon, Andrea R. Beccari, Andrea R. Beccari, Carmine Talarico, Francesca Spyrakis, Erik Lindahl, Andrea Zaliani, Paolo Carloni, Rebecca C. Wade, Francesco Musiani, Daria B. Kokh, Giulia Rossetti: A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics. ACS Pharmacol. Transl. Sci. 2021, Publication Date: March 16, 2021
DOI: 10.1021/acsptsci.0c00215
Associated Publications in back-to-back submission:


Prof. Giulia Rossetti
Institute for Advanced Simulation, IAS-5/INM-9: Computational Biomedicine
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