RESUMO
To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski's and Veber's rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy <-7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.
RESUMO
Objectives: Despite great advancements in COVID-19 immunization, the development of therapeutic interventions is urgent to control the ongoing pandemic, especially infected patients. The spike protein (S1) of SARS-Cov-2 virus plays a major role in attachment to the host and further series of events. We aimed to identify natural bioactive compounds (NBC) that act as potential inhibitors of S1 by means of in silico assays. Methods: NBCs with proved biological in vitro activities were obtained from the ZINC database (https://zinc.docking.org) and analyzed through virtual screening and molecular docking to identify those with higher affinity to the S1. Machine learning models of principal component analysis (PCA), artificial neural networks (ANN), discriminant analysis by partial least squares (PLS-DA) and decision tree (DT) were used to validate the Results: Selected NBCs were submitted to drug-likeness analysis using the Lipinsk and Vebber's five rule. The prediction of pharmacokinetic parameters (i.e. absorption, metabolism, distribution, elimination) and toxicity (e.g. hepatotoxicity, cardiotoxicity, carcinogenicity, immunotoxicity) were performed (ADMET). The influence of the NBC’s stereoisomeric, tautomeric and protonation states at physiological pH on the pharmacodynamics, pharmacokinetics and toxicity analyses were also evaluated. Results: A total of 170,906 compounds were analyzed. Of these, only 36 showed greater affinity with the S1 (affinity energy <0.8 kcal/mol). The PCA and PLS-DA models were able to reproduce the results of the virtual screening and docking analyzes with an accuracy of 97.5%. Of these 36 CNBs, only 12 (33.33%) were drug-likeness. The ADMET analysis showed that the natural compound phaselol (7-[[(1R,4aS,6R,8aR)-6-hydroxy-2,5,5,8a-tetramethyl-1,4,4a,6,7,8-hexahydronaphthalen-1-yl]methoxy]chromen-2-one) was the most promising in inhibiting the SARS-COV-2 spike. Conclusions: Machine learning-based research is efficient for retrieving novel approaches to diseases’ treatment. We identified 12 compounds as possible inhibitors of S1;phaselol was the most promising candidate for treating COVID-19. In vitro, preclinical studies and clinical trials are now needed to confirm these findings.