The propagation of emerging diseases and the expensive cost and time lost by using the classic methods, especially in the current scenario with the world being plagued by SARS-CoV-2 and Chlamydia trachomatis diseases, make finding another way to invent new medication very important. That's why we used computational approaches to predict protein-ligand interactions of thiazolino 2-pyridone amide derivatives. The high-throughput virtual screening requires extensive combing through existing datasets in the hope of finding possible matches to screen for new molecules able to inhibit SARS-CoV-2 and Chlamydia trachomatis diseases. In this study, 46 thiazolino-2-pyridone amide derivatives were chosen for planning the powerful inhibitors by utilizing various strategies: QSAR analysis, phylogenetic analysis, homology modeling, docking simulation, molecular dynamics (MD) simulation, as well as ADMET Screening. The 2D QSAR investigation uncovers that these compounds show a satisfactory connection with bioactivity. From that point onward, phylogenetic analysis and homology modeling were used to model the selected receptors, which were then evaluated using both the SAVES and PROSA servers, indicating the best correctness of the modeled protein with the experimental results. Additionally, a docking simulation investigation was carried out to comprehend the 46 thiazolino-2-pyridone amide derivatives' interactions with homologous proteins. Additionally, MD simulations coupled with MM/GBSA verified the chosen complex systems' stability over 1000 ps. Two compounds were chosen as possible inhibitors based on these findings. The expected thiazolino-2-pyridone amide's oral bioavailability and toxicity have been discovered under the ADMET. Thus, these discoveries can be leveraged to develop novel molecules with the necessary action.
SARS-CoV-2 Chlamydia trachomatis Thiazolino 2-pyridone amide Docking MD simulations ADMET and MM/GBSA calculation
Primary Language | English |
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Subjects | Chemical Engineering |
Journal Section | Research Article |
Authors | |
Early Pub Date | May 26, 2023 |
Publication Date | January 15, 2024 |
Submission Date | October 28, 2022 |
Published in Issue | Year 2024 Volume: 8 Issue: 1 |
Journal Full Title: Turkish Computational and Theoretical Chemistry
Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)