MaX (MAterials design at the eXascale) is a European Centre of Excellence aiming at materials modelling, simulations, discovery and design on the exascale supercomputing architectures.

Though the main interest of the MaX flagship codes is then centered on materials science, the CoE is participating in the fight against SARS-CoV-2. Given the critical pandemic situation that the world is currently facing, an unprecedented effort is being devoted to the study of SARS-CoV-2 by researchers from different scientific communities and groups worldwide. From the biomolecular standpoint, particular focus is being devoted to the main protease, as well as to the spike protein. As such, it is an important potential antiviral drug target: if its function is inhibited, the virus remains immature and non-infectious. Using fragment-based screening, researchers have identified a number of small compounds that bind to the active site of the protease and can be used as a starting point for the development of protease inhibitors.

Among other quantities, MaX researchers now have the possibility to model the electronic structure of the protease in contact with a potential docked inhibitor, and provide new insights on the interactions between them by selecting specific amino-acids that are involved in the interaction and characterizing their polarities. This new approach proposed by the MaX scientists is complementary to the docking methods used up to now and based on in-silico research of the inhibitor. Biological systems are naturally composed of fragments such as amino-acids in proteins or nitrogenous bases in DNA.

With this approach, it is possible to evaluate whether the amino acid-based fragmentation is consistent with the electronic structure resulting from the QM computation. This is an important indicator for the end-user, as it enables to evaluate the quality of the information associated with a given fragment. Then, QM observables on the system’s fragments can be obtained, which are based on a population analysis of electronic density of the system, projected on the amino-acid.

A novelty that this approach enables is the possibility of quantifying the strength of the chemical interaction between the different fragments. It is possible to select a target region and identify which fragments of the systems interact with this region by sharing electrons with it.

“We can reconstruct the fragmentation of the system in such a way as to focus on an active site in a specific portion of the protein”, says Luigi Genovese from CEA (Commissariat à l’énergie atomique et aux énergies alternatives) who is heading Max’s efforts on this topic. “We think this modelling approach could inform efforts in protein design by granting access to variables otherwise impervious to observation.”