Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer

Non-small cell lung cancer (NSCLC) is a type of lung cancer associated with translocation of the EML4 and ALK genes on the short arm of chromosome 2. This leads to the development of an aberrant protein kinase with a deregulated catalytic domain, the cdALK+. Currently, different ALK inhibitors (iALKs) have been proposed to treat ALK+ NSCLC patients. However, the recent resistance to iALKs stimulates the exploration of new iALKs for NSCLC. Here, we describe an in silico approach to finding FDA-approved drugs that can be used by pharmacological repositioning as iALK. We used homology modelling to obtain a structural model of cdALK+ protein and then performed molecular docking and molecular dynamics of the complex cdALK+-iALKs to generate the pharmacophore model. The pharmacophore was used to identify potential iALKs from FDA-approved drugs library by ligand-based virtual screening. Four pharmacophores with different atomistic characteristics were generated, resulting in six drugs that satisfied the proposed atomistic positions and coupled at the ATP-binding site. Mitoxantrone, riboflavin and abacavir exhibit the best interaction energies with 228.29, 165.40 and 133.48 kjoul/mol respectively. In addition, the special literature proposed these drugs for other types of diseases due to pharmacological repositioning. This study proposes FDA-approved drugs with ALK inhibitory characteristics. Moreover, we identified pharmacophores sites that can be tested with other pharmacological libraries.

Methods
Was used YASARA™ software to perform the simulations between the interactions of the cdALK+ with ATP and its known inhibitors: crizotinib, ceritinib, brigatinib, alectinib, lorlatinib.  The force fields used in the molecular dynamics, was AMBER14.

非小细胞肺癌（NSCLC）是一类与2号染色体短臂上EML4与ALK基因易位相关的肺癌亚型。该基因易位会导致产生一种催化结构域失调的异常蛋白激酶，即cdALK+。目前，多种ALK抑制剂（iALKs）已被应用于ALK+非小细胞肺癌患者的临床治疗。然而，当前ALK抑制剂耐药性问题逐渐凸显，推动了新型ALK抑制剂的研发探索。本研究介绍了一种计算机辅助（in silico）筛选方法，用于从FDA批准的药物库中筛选可通过药物重定位用作ALK抑制剂的候选药物。本研究采用同源建模法构建cdALK+蛋白的三维结构模型，随后通过对cdALK+-iALK复合物进行分子对接与分子动力学模拟，生成药效团模型。在基于配体的虚拟筛选中，该药效团被用于从FDA批准药物库中筛选潜在ALK抑制剂。研究共构建了四种具有不同原子特征的药效团，最终筛选出6种符合预设原子位点要求且可结合于ATP结合口袋的药物。米托蒽醌、核黄素与阿巴卡韦的相互作用能最优，分别为228.29、165.40与133.48千焦/摩尔。此外，已有研究表明上述药物可通过药物重定位用于其他疾病的治疗。本研究筛选出了具有ALK抑制活性的FDA批准药物，同时确定了可用于其他药物库筛选的药效团位点。

方法
本研究采用YASARA™软件，模拟cdALK+与ATP及其已知ALK抑制剂（克唑替尼、色瑞替尼、布加替尼、阿来替尼、洛拉替尼）之间的相互作用。本研究分子动力学模拟所采用的力场为AMBER14。