Molecular phylogeny, Sequence-based drug design, Docking built virtual screening, dynamics simulations, and ADMET properties of thiazolino 2-pyridone amide derivatives as an inhibitor of Chlamydia trachomatis and SARS-CoV-2 protein

Q3 Biochemistry, Genetics and Molecular Biology
E. Edache, A. Uzairu, P. Mamza, G. Shallangwa
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Abstract

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.
沙眼衣原体和SARS-CoV-2蛋白抑制剂噻唑啉2-吡啶酮酰胺衍生物的分子系统发育、基于序列的药物设计、对接构建虚拟筛选、动力学模拟和ADMET特性研究
新发疾病的传播以及使用经典方法的昂贵成本和时间损失,特别是在当前世界受到SARS-CoV-2和沙眼衣原体疾病困扰的情况下,寻找另一种方法来发明新药非常重要。这就是为什么我们使用计算方法来预测噻唑啉- 2-吡啶酮酰胺衍生物的蛋白质-配体相互作用。高通量虚拟筛选需要对现有数据集进行广泛梳理,希望找到可能的匹配,以筛选能够抑制SARS-CoV-2和沙眼衣原体疾病的新分子。本研究通过QSAR分析、系统发育分析、同源性建模、对接模拟、分子动力学(MD)模拟和ADMET筛选等多种策略,筛选出46个噻唑啉-2-吡啶酮酰胺衍生物,并对其进行规划。二维QSAR研究发现,这些化合物与生物活性表现出令人满意的联系。从那时起,使用系统发育分析和同源性建模对选定的受体进行建模,然后使用SAVES和PROSA服务器对其进行评估,表明模型蛋白与实验结果的最佳正确性。此外,对接模拟研究了46种噻唑啉-2-吡啶酮酰胺衍生物与同源蛋白的相互作用。此外,MD模拟结合MM/GBSA验证了所选复合体系在1000 ps以上的稳定性。基于这些发现,选择了两种化合物作为可能的抑制剂。在ADMET下发现了预期的噻唑啉-2-吡啶酮酰胺的口服生物利用度和毒性。因此,这些发现可以用来开发具有必要作用的新分子。
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来源期刊
Turkish Computational and Theoretical Chemistry
Turkish Computational and Theoretical Chemistry Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
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