Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km28 Cairo-Ismailia Road, Ahmed Orabi District, Cairo, Egypt.
Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km28 Cairo-Ismailia Road, Ahmed Orabi District, Cairo, Egypt; Chemistry Department, University of Washington, Seattle, WA, USA.
Comput Biol Med. 2022 Jul;146:105526. doi: 10.1016/j.compbiomed.2022.105526. Epub 2022 Apr 20.
Cysteine-based mesenchymal-epithelial transition (c-Met) is a receptor tyrosine kinase that plays a definitive role during cancer progression and was identified as a possible target for anti-angiogenesis drugs. In the present study, different protocols of computer-based drug design were performed. Construction of predictive pharmacophore model using HypoGen algorithm resulted in a validated model of four features of positive ionizable, hydrogen bond acceptor, hydrophobic, and ring aromatic features with a correlation coefficient of 0.87, a configuration cost of 14.95, and a cost difference of 357.92. The model revealed a promising predictive power and had >90% probability of representing true correlation with the activity data. The model was established using Fisher's validation test at the 95% confidence level and test set prediction (r = 0.96), furthermore, the model was validated by mapping of set of compounds undergoing clinical trials as class Ⅱ c-met inhibitors. The generated valid pharmacophore model was then anticipated for virtual screening of three data bases. Moreover, scaffold hopping using replace fragments protocol was implemented. Hits generated were filtered according to Lipinski's rule; 510 selected hits were anatomized and subjected to molecular docking studies into the crystal structure of c-Met kinase. The good correlation between docking scores and ligand pharmacophore mapping fit values provided a reliable foundation for designing new potentially active candidates that may target c-Met kinase. Eventually, eight hits were selected as potential leads. Subsequently, seven (Hits) have displayed a higher dock score and demonstrated key residue interactions with stable molecular dynamics simulation. Therefore, these c-Met kinase inhibitors may further serve as new chemical spaces in designing new compounds.
半胱氨酸基质金属上皮转化因子（c-Met）是一种受体酪氨酸激酶，在癌症进展中起着决定性作用，被确定为抗血管生成药物的可能靶点。在本研究中，进行了不同的基于计算机的药物设计方案。使用 HypoGen 算法构建预测药效团模型，得到了一个经过验证的具有正可离子化、氢键受体、疏水和亲脂环芳香四个特征的模型，相关系数为 0.87，构象成本为 14.95，成本差异为 357.92。该模型显示出有希望的预测能力，有超过 90%的概率代表与活性数据的真实相关性。该模型是使用 Fisher 验证测试在 95%置信水平和测试集预测（r=0.96）建立的，此外，通过对正在进行临床试验的化合物集进行映射作为 II 类 c-Met 抑制剂，对模型进行了验证。然后，预期生成的有效药效团模型可以对三个数据库进行虚拟筛选。此外，还实施了替换片段协议的支架跳跃。根据 Lipinski 规则过滤生成的命中；对 510 个选定的命中进行解剖，并将其进行分子对接研究，以进入 c-Met 激酶的晶体结构。对接评分与配体药效团映射拟合值之间的良好相关性为设计可能靶向 c-Met 激酶的新潜在活性化合物提供了可靠的基础。最终，选择了 8 个命中作为潜在的先导化合物。随后，有 7 个（命中）显示出更高的对接评分，并在稳定的分子动力学模拟中表现出关键残基相互作用。因此，这些 c-Met 激酶抑制剂可能进一步作为设计新化合物的新化学空间。
