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Fast and effective drug discovery processes rely on rational drug design tocircumvent the tedious and expensive trial and error approach. However,accurate predictions of new remedies, which are often enzyme inhibitors,require a clear understanding of the nature and function of the key playersgoverning the interaction between the drug candidate and its target. Here, wepropose to calculate electric fields to explicitly link structure to functionin molecular dynamics simulations, a method that can easily be integratedwithin the rational drug discovery workflow. By projecting the electric fieldsonto specific bonds, we can identify the system components that are at theorigin of stabilizing intermolecular interactions (covalent and non-covalent)in the active site. This helps to significantly narrow the exploration spacewhen predicting new inhibitors. To illustrate this method, we characterize thebinding of the non-covalent inhibitor X77 to the main protease of SARS-CoV-2, aparticularly time-sensitive drug discovery problem. With electric fieldcalculations, we were able to identify 3 key residues (Asn-142, Met-165 andGlu-166), that have functional consequences on X77. This contrasts with thenearly 20 residues reported in previous studies as being in close contact withinhibitors in the active site of the protease. As a result, the search for newnon-covalent inhibitors can now be accelerated by techniques that look tooptimize the interaction between candidate molecules and these residues.

Drug design principles from electric field calculations: understanding SARS-CoV-2 main protease interaction with X77 non-covalent inhibitor