posted on 2021-11-19, 20:06authored byJun Zhao, Qinhai Ma, Baoyue Zhang, Pengfei Guo, Zhe Wang, Yi Liu, Minsi Meng, Ailin Liu, Zifeng Yang, Guanhua Du
COVID-19
caused by a novel coronavirus (SARS-CoV-2) has been spreading
all over the world since the end of 2019, and no specific drug has
been developed yet. 3C-like protease (3CLpro) acts as an
important part of the replication of novel coronavirus and is a promising
target for the development of anticoronavirus drugs. In this paper,
eight machine learning models were constructed using naïve
Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular
descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular
fingerprints. The optimal models were selected according to the results
of 5-fold cross verification, test set verification, and external
test set verification. A total of 5766 natural compounds from the
internal natural product database were predicted, among which 369
chemical components were predicted to be active compounds by the optimal
models and the EstPGood values were more than 0.6, as predicted by
the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were
selected for further biological activity determination by the fluorescence
resonance energy transfer (FRET) method and cytopathic effect (CPE)
detection. The results indicated that (+)-shikonin, shikonin, scutellarein,
and 5,3′,4′-trihydroxyflavone showed certain activity
in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory
concentration (IC50) values ranging from 4.38 to 87.76
μM. In the CPE assay, 5,3′,4′-trihydroxyflavone
showed a certain antiviral effect with an IC50 value of
8.22 μM. The binding mechanism of 5,3′,4′-trihydroxyflavone
with SARS-CoV-2 3CLpro was further revealed through CDOCKER
analysis. In this study, 3CLpro prediction models were
constructed based on machine learning algorithms for the prediction
of active compounds, and the activity of potential inhibitors was
determined by the FRET method and CPE assay, which provide important
information for further discovery and development of antinovel coronavirus
drugs.