COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than
two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human
body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take
place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is
very important to understand human–spike protein–protein interactions
(PPIs) in order to predict new PPIs and consequently propose drugs for the human
proteins in order to fight the virus and its different mutated variants, with the
mutations occurring in the spike protein. This fact motivated us to develop a complete
pipeline where PPIs and drug–protein interactions can be predicted for
human–SARS-CoV-2 interactions. In this regard, initially interacting data sets
are collected from the literature, and noninteracting data sets are subsequently created
for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for
drug–protein interactions both interacting and noninteracting data sets are
considered from DrugBank and ChEMBL databases. Thereafter, a model based on a
sequence-based feature is used to code the protein sequences of human and spike proteins
using the well-known Moran autocorrelation technique, while the drugs are coded using
another well-known technique, viz., PaDEL descriptors, to predict new human–spike
PPIs and eventually new drug–protein interactions for the top 20 predicted human
proteins interacting with the original spike protein and its different mutated variants
like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random
forest as it is found to perform better than other predictors, providing an accuracy of
90.53% for human–spike PPI and 96.15% for drug–protein interactions.
Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide,
dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins
like ACACA, DST, DYNC1H1, etc.