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In silico study to identify novel potential thiadiazole-based molecules as anti-Covid-19 candidates by hierarchical virtual screening and molecular dynamics simulations.

ABSTRACT: In the present study, a new category of 1,3,4-thiadiazoles was developed by submitting methyl 2-(4-hydroxy-3-methoxybenzylidene) hydrazine-1-carbodithioate to react with the appropriate hydrazonoyl halides in presence of few drops of diisopropyl ethyl amine. The chemical structures of the newly synthesized derivatives were inferred by means of their micro-analytical and spectral data. Utilizing combined molecular docking and molecular dynamics techniques, the binding affinities and features of the synthesized compounds were evaluated against four SARS-CoV-2 target enzymes, namely, main protease (M^pro), papain-like protease (PL^pro), RNA-dependent RNA polymerase (RdRp), and receptor-binding domain (RBD) of the spike protein. Compound 7 demonstrated promising binding affinities with the target enzymes M^pro, PL^pro, RdRp, and RBD with docking scores of -11.4, -9.4, -8.2, and -6.8 kcal/mol, respectively. In addition, compound 7 exhibited MM-GBSA//100 ns MD docking score of -35.9 kcal/mol against M^pro. Structural and energetic analyses revealed the stability of the 7-M^pro complex over 100 ns MD simulations. In addition, compound 7 obeyed Lipinski's rule of five, as it has acceptable absorption, distribution, and oral bioavailability inside the body. Therefore, compound 7 is considered as a promising starting point for designing potential therapeutic agents against Covid-19.

Supplementary information

The online version contains supplementary material available at 10.1007/s11224-022-01985-1.

SUBMITTER: Rashdan HRM

PROVIDER: S-EPMC9198413 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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In silico study to identify novel potential thiadiazole-based molecules as anti-Covid-19 candidates by hierarchical virtual screening and molecular dynamics simulations.

Rashdan Huda R M HRM Abdelmonsef Aboubakr H AH

Structural chemistry 20220615 5

In the present study, a new category of 1,3,4-thiadiazoles was developed by submitting methyl 2-(4-hydroxy-3-methoxybenzylidene) hydrazine-1-carbodithioate to react with the appropriate hydrazonoyl halides in presence of few drops of diisopropyl ethyl amine. The chemical structures of the newly synthesized derivatives were inferred by means of their micro-analytical and spectral data. Utilizing combined molecular docking and molecular dynamics techniques, the binding affinities and features of t ...[more]

PMID: 35729938

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Project description:Infectious agents such as SARS-CoV, MERS-CoV, and SARS-CoV-2 have emerged in recent years causing epidemics with high mortality rates. The quick development of novel therapeutic compounds is required in the fight against such pathogenic agents. Unfortunately, the traditional drug development methods are time-consuming and expensive. In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 Mpro. Compounds such as cinanserin, nelfinavir, baicalin, baicalein, candesartan cilexetil, chloroquine, dipyridamole, and hydroxychloroquine have the ability to prevent SARS-CoV-2 Mpro from facilitating COVID 19 infection; thus, they treat COVID 19. However, these drugs majorly act to reduce the symptoms of the disease. No anti-viral drug against COVID 19 virus infection has been discovered and approved. Therefore, this study sought to explore natural inhibitors of SARS-CoV-2 Mpro to develop a pharmacophore model for virtual screening of natural compounds in the ZINC database as potential candidates for SARS-CoV-2 Mpro inhibitors and as therapeutic molecules against COVID 19. This study undertook in silico methods to identify the best anti-viral candidates targeting SAR-CoV-2 Mpro from natural sources in the ZINC database. Initially, reported anti-SARS-CoV-2 Mpro molecules were integrated into designing a pharmacophore model utilizing PharmaGist. Later, the pharmacophore model was loaded into ZINCPHARMER and screened against the ZINC database to identify new probable drug candidates. The root means square deviation (RMSD) values of the potential drug candidates informed the selection of some of them, which were docked with SARS-CoV-2 Mpro to comprehend their interactions. From the molecular docking results, the top four candidates (ZINC000254823011, ZINC000072307130, ZINC000013627512, and ZINC000009418994) against SARS-CoV-2 Mpro, with binding energies ranging from -8.2 kcal/mol to -8.6 kcal/mol, were examined for their oral bioavailability and other pharmacokinetic properties. Consequently, ZINC000072307130 emerged as the only orally bioavailable drug candidate with desirable pharmacokinetic properties. This candidate drug was used to perform MD simulations, and the outcomes revealed that ZINC000072307130 formed a stable complex with the viral main protease. Consequently, ZINC000072307130 emerges as a potential anti-SARS-CoV-2 Mpro inhibitor for the production of new COVID 19 drugs.

Project description:(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.

Dataset Information

In silico study to identify novel potential thiadiazole-based molecules as anti-Covid-19 candidates by hierarchical virtual screening and molecular dynamics simulations.

Supplementary information

Publications

In silico study to identify novel potential thiadiazole-based molecules as anti-Covid-19 candidates by hierarchical virtual screening and molecular dynamics simulations.

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