Identifying Potential SARS-CoV-2 Protease (PLpro) Inhibitors through in
silico Virtual Screening and Text Mining: An Analysis of Toxicity and
Interaction Effects.
Abstract
At the end of 2019 a new coronavirus surfaced in China, SARS-CoV-2,
responsible for the ongoing pandemic. There is a need for novel and
stable therapies to help patients of COVID-19. Drug repositioning is a
strategy to quickly find medicines already used to treatment to another
pathology. In this work, we used the PLpro as molecular target for it
being responsible for cleaving other viral proteins and interfering with
the immune system. In the Brazilian Pharmacopeia is described many
different Active Pharmaceutical Ingredients (API) used in Brazil and the
world. Using the in silico techniques of virtual screening, the top 44
API pass through Toxicity prediction, where 36 API prove to not be
mutagenic. Using molecular weight, distance to the protein, and
literature information 19 API go through prediction of chemical
interactions, we determine the top 6 APIs with the best chance of
interacting with the PLpro. With this result, we determine new possible
API that will be tested in vitro to determine its ability to inhibit
SARS-CoV-2’s PLpro, and could be readily made available to the infected
populous.