Cancer Genetics & Molecular Biology Laboratory, Department of Bioinformatics, Science Campus, Alagappa University, Karaikudi, Tamil Nadu, India.
Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education (Deemed to Be University), Chennai, Tamil Nadu, India.
Biomed Res Int. 2022 Jun 28;2022:3338549. doi: 10.1155/2022/3338549. eCollection 2022.
Cyclooxygenase-2 (COX-2) is a key enzyme involved in overexpression in several human cancerous diseases including breast cancer. By performing efficient virtual screening in a series of active molecules or compounds from the Maybridge, NCI (National Cancer Institute), and Enamine databases, potential identification of COX-2 inhibitors could lead to new prognostic strategies in the treatment of breast cancer. Based on a 50% structural similitude, compounds were chosen as the inductive model of COX-2 inhibitions from these databases. Selected compounds were filtered and tested with Lipinski's rule of five followed by absorption, distribution, metabolism, and excretion (ADME) properties. Subsequently, molecular docking was performed to achieve accuracy in screening and also to find an interactive mechanism between hit compounds with their respective binding sites. Simultaneously, molecular simulations of top-scored compounds were selected and coded such as Maybridge_55417, NCI_30552, and Enamine_62410. Chosen compounds were analyzed and interpreted with COX-2 affinity. Results endorsed that hydrophobic affinity and optimum hydrogen bonds were the forces driven in the interactive mechanism of hits compounds with COX-2 and can be used as efficient alternative therapeutic agents targeting deleterious breast cancer. With these findings, compounds identified may prevent the action of the COX-2 enzyme and thereby diminish the incidence of breast cancer.
环氧化酶-2(COX-2)是一种关键酶,在包括乳腺癌在内的几种人类癌症中过度表达。通过在 Maybridge、NCI(美国国立癌症研究所)和 Enamine 数据库中的一系列活性分子或化合物中进行有效的虚拟筛选,可以潜在地识别 COX-2 抑制剂,从而为乳腺癌的治疗提供新的预后策略。基于 50%的结构相似性,从这些数据库中选择化合物作为 COX-2 抑制的诱导模型。选择的化合物经过 Lipinski 的五规则过滤和测试,然后进行吸收、分布、代谢和排泄(ADME)特性测试。随后,进行分子对接以实现筛选的准确性,并找到命中化合物与其各自结合位点之间的相互作用机制。同时,选择并编码了 top-scored 化合物的分子模拟,例如 Maybridge_55417、NCI_30552 和 Enamine_62410。选择的化合物与 COX-2 的亲和力进行了分析和解释。结果表明,疏水性亲和力和最佳氢键是命中化合物与 COX-2 相互作用机制中的驱动力,可作为针对有害乳腺癌的有效替代治疗药物。有了这些发现,鉴定出的化合物可能会阻止 COX-2 酶的作用,从而减少乳腺癌的发生。