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.

非小细胞肺癌（Non-small cell lung cancer, NSCLC）是一类与2号染色体短臂上EML4基因与间变性淋巴瘤激酶（Anaplastic Lymphoma Kinase, ALK）基因易位相关的肺癌。该易位会导致产生带有调控异常催化结构域的异常蛋白激酶，即cdALK+。目前，多款ALK抑制剂（iALKs）已被提议用于治疗ALK阳性非小细胞肺癌患者。然而，当前出现的ALK抑制剂耐药性推动了针对非小细胞肺癌的新型ALK抑制剂的研发探索。本研究提出了一种计算机模拟（in silico）方法，用于从美国食品药品监督管理局（Food and Drug Administration, FDA）批准的药物库中筛选可通过药物重定位（pharmacological repositioning）用作ALK抑制剂的候选化合物。我们通过同源建模（homology modelling）构建了cdALK+蛋白的结构模型，随后对cdALK+-iALKs复合物开展分子对接（molecular docking）与分子动力学（molecular dynamics）模拟，以构建药效团模型（pharmacophore model）。该药效团模型被用于通过基于配体的虚拟筛选（ligand-based virtual screening），从FDA批准的药物库中筛选潜在的ALK抑制剂。我们共构建了4个具备不同原子特征的药效团，最终筛选得到6种可匹配预设原子位点并结合于ATP结合位点（ATP-binding site）的药物。米托蒽醌（Mitoxantrone）、核黄素（riboflavin）与阿巴卡韦（abacavir）展现出最优的相互作用能，分别为228.29、165.40及133.48千焦每摩尔（kJ/mol）。此外，已有相关研究基于药物重定位策略，将这三种药物应用于其他疾病的治疗。本研究筛选得到了具备ALK抑制活性的FDA批准药物。此外，本研究还确定了可用于其他药物库筛选的药效团位点。