A computational approach to in
silico
drug discovery was
carried out to identify small drug-like compounds able to show structural and functional
mimicry of the high affinity ligand X77, potent non-covalent inhibitor of SARS-COV-2 main
protease (M
Pro
). In doing so, the X77-mimetic candidates were predicted based
on the crystal X77-M
Pro
structure by a public web-oriented virtual screening
platform Pharmit. Models of these candidates bound to SARS-COV-2 M
Pro
were
generated by molecular docking, quantum chemical calculations and molecular dynamics
simulations. At the final point, analysis of the interaction modes of the identified
compounds with M
Pro
and prediction of their binding affinity were carried out.
Calculation revealed 5 top-ranking compounds that exhibited a high affinity to the active
site of SARS-CoV-2 M
Pro
. Insights into the ligand − M
Pro
models
indicate that all identified compounds may effectively block the binding pocket of
SARS-CoV-2 M
Pro
, in line with the low values of binding free energy and
dissociation constant. Mechanism of binding of these compounds to M
Pro
is
mainly provided by van der Waals interactions with the functionally important residues of
the enzyme, such as His-41, Met-49, Cys-145, Met-165, and Gln-189 that play a role of the
binding hot spots assisting the predicted molecules to effectively interact with the
M
Pro
active site. The data obtained show that the identified X77-mimetic
candidates may serve as good scaffolds for the design of novel antiviral agents able to
target the active site of SARS-CoV-2 M
Pro
.
Communicated by Ramaswamy H. Sarma