Affiliation:
1. Molecular Modeling Lab, Food and Drug Department, University of Parma, Parco Area delle Scienze 17/A, 43121 Parma, Italy
2. Department of Mathematical, Physical and Computer Sciences, University of Parma, 43121 Parma, Italy
Abstract
The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins’ mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. “Mutations rules” that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.
Funder
L.A.V.-Lega Anti Vivisezione
CINECA
Subject
Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science
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