Prashant Kurkute, Sumit Sonawane, Kumar Pratyush, Bhushan Dravyakar, Azim Ansari, Pradip Bawane, Yogeeta O Agrawal, Mahendra Khairnar, Mohd Usman Mohd Siddique
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Top-scoring molecules and reference molecules were further subjected to MD simulations, PCA analysis, DCCM assay, binding free energies calculations, and in-silico ADME calculations.</p><p><strong>Results: </strong>From a preliminary HTVS study, six molecules were identified based on their docking scores: ZINC37289024, ZINC89755080, ZINC20993115, ZINC72445968, ZINC28247630, and ZINC16638515, with the docking score of -10.186, -09.229, -09.045, -09.021, -08.920 and -08.821, respectively. In subsequent MD simulations studies, the protein backbone RMSD values were observed to be 1.978, 1.8178, 2.2309, 1.7933, 1.8837, 1.9461, and 1.8711 Å, respectively. Similarly, the protein backbone RMSF values were 0.9511, 1.0172, 1.2023, 1.0591, 1.0029, 1.9755, and 0.9200 Å, respectively. PCA, DCCM, and MMGBSA analysis indicated that these complexes were quite stable throughout the 100 ns MD simulations. In-silico ADME predictions of identified top six hits suggested that these top six hits possess favorable drug-like properties, supporting their potential as the lead candidates for therapeutic development.</p><p><strong>Conclusion: </strong>A multiscale molecular modelling approach was employed, and six top-scoring hits were identified as promising TTBK1 inhibitors. Analysis of the in-silico data suggested that ZINC37289024 would be the most promising clinical candidate for AD. 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引用次数: 0
摘要
导读:tau微管蛋白激酶1 (TTBK1)酶对tau蛋白的过度磷酸化与几种神经退行性疾病的发病机制有关。基于对TTBK1在复杂抑制剂(pdb id 4BTK)中共晶结构的全面文献综述和可用性,我们设计了一种多尺度计算方法来识别ZINC13化学文库中的新命中点。方法:利用ZINC13数据库(包含13,195,609个分子)对TTBK1蛋白(PDB id 4BTK)进行高通量虚拟筛选(HTVS)。得分最高的分子和参考分子进一步进行MD模拟、PCA分析、DCCM分析、结合自由能计算和硅ADME计算。结果:通过HTVS初步研究,根据对接分数鉴定出6个分子:ZINC37289024、ZINC89755080、ZINC20993115、ZINC72445968、ZINC28247630和ZINC16638515,对接分数分别为-10.186、-09.229、-09.045、-09.021、-08.920和-08.821。在随后的MD模拟研究中,蛋白质骨架RMSD值分别为1.978、1.8178、2.2309、1.7933、1.8837、1.9461和1.8711 Å。同样,蛋白质骨架RMSF值分别为0.9511、1.0172、1.2023、1.0591、1.0029、1.9755和0.9200 Å。PCA、DCCM和MMGBSA分析表明,这些配合物在100 ns MD模拟中相当稳定。对确定的前6个热门药物的计算机ADME预测表明,这6个热门药物具有良好的药物样特性,支持它们作为治疗开发的主要候选药物的潜力。结论:采用多尺度分子建模方法,确定了6个得分最高的TTBK1抑制剂。计算机数据分析表明,ZINC37289024将是最有希望的阿尔茨海默病临床候选药物。然而,需要进一步的体外和体内实验数据来验证这些结果。
A Multiscale Computational Study for the Identification of Novel Inhibitors Targeting Tau-Tubulin Kinase 1 (TTBK1) in Alzheimer's Disease.
Introduction: Excessive phosphorylation of tau protein by the tau-tubulin kinase 1 (TTBK1) enzyme is implicated in the pathogenesis of several neurodegenerative diseases. Based on a comprehensive literature review and availability of the co-crystal structure of TTBK1 in complex inhibitor (pdb id 4BTK), we designed a multiscale computational approach to identify novel hits from the ZINC13 chemical library.
Methods: The High-Throughput Virtual Screening (HTVS) of the ZINC13 database (containing 13,195,609 molecules) was carried out against TTBK1 protein (PDB id 4BTK). Top-scoring molecules and reference molecules were further subjected to MD simulations, PCA analysis, DCCM assay, binding free energies calculations, and in-silico ADME calculations.
Results: From a preliminary HTVS study, six molecules were identified based on their docking scores: ZINC37289024, ZINC89755080, ZINC20993115, ZINC72445968, ZINC28247630, and ZINC16638515, with the docking score of -10.186, -09.229, -09.045, -09.021, -08.920 and -08.821, respectively. In subsequent MD simulations studies, the protein backbone RMSD values were observed to be 1.978, 1.8178, 2.2309, 1.7933, 1.8837, 1.9461, and 1.8711 Å, respectively. Similarly, the protein backbone RMSF values were 0.9511, 1.0172, 1.2023, 1.0591, 1.0029, 1.9755, and 0.9200 Å, respectively. PCA, DCCM, and MMGBSA analysis indicated that these complexes were quite stable throughout the 100 ns MD simulations. In-silico ADME predictions of identified top six hits suggested that these top six hits possess favorable drug-like properties, supporting their potential as the lead candidates for therapeutic development.
Conclusion: A multiscale molecular modelling approach was employed, and six top-scoring hits were identified as promising TTBK1 inhibitors. Analysis of the in-silico data suggested that ZINC37289024 would be the most promising clinical candidate for AD. However, further in-vitro and in-vivo experimental data would be needed for validation of these results.