The matrix 2 (M2) protein is a multifunctional protein found within the influenza A virus, and considered an attractive drug target. The transmembrane domain of the M2 protein acts as a proton channel and contains the binding site of adamantyl-amine drugs, such as amantadine and rimantadine. Whilst clinical use of these drugs has been discontinued, owing to widespread resistance, their study remains useful for the design of the next generation of antiviral compounds. Of special interest is the network of water molecules which facilitate proton transport and also appear to stabilise the binding of these drugs to the hydrophobic channel. Grand canonical Monte Carlo (GCMC) is a simulation technique which allows kinetic barriers that inhibit the sampling of buried water molecules to be bypassed, by alchemically inserting and deleting waters from the system, according to a specified chemical potential. Additionally, carrying out a GCMC titration of the system (by simulating at a range of chemical potential values) allows the binding free energy of the water network to be rigorously calculated, accounting for cooperative binding effects. GCMC moves are carried out alongside molecular dynamics sampling (GCMC/MD) in this work, using the Python module, grand. We apply GCMC/MD titration calculations to determine the binding free energies of water networks in M2-drug complexes. First, we study the potential enantiomeric selectivity of the chiral drug rimantadine, when bound to the wild type (WT) structure of M2. Second, we perform a similar analysis for amantadine with both the wild type and a resistant mutant, and compare and contrast these results with a recently developed inhibitor which can inhibit both forms of the protein. The results obtained provide insights into the mechanisms of drug binding to the M2 protein, and how drug resistance is achieved.

Insights into the effect of hydration on enantiomeric selectivity and drug resistance using grand canonical Monte Carlo simulations

ACS Fall 2021

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