Department of Medical Parasitology and Mycology, Faculty of Medicine, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Clinical Biochemistry Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, Iran
Anticancer Agents Med Chem. 2021;21(8):987-1018. doi: 10.2174/1871520620666200721134010.
Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.
In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.
The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q), noncross- validated correlation coefficient (r) and predicted correlation coefficient (r) of the training and test set molecules, respectively.
The CoMFA (q, 0.670; r, 0.930; r, 0.968), CoMFA-RF (q, 0.694; r, 0.926; r, 0.927), CoMSIA (q, 0.834; r, 0.956; r, 0.958) and HQSAR models (q, 0.854; r, 0.900; r, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.
These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.
组蛋白赖氨酸去甲基酶 1（LSD1）是一种有前途的治疗癌症的药物，它在基因表达的表观遗传调控中起着至关重要的作用。用小分子抑制 LSD1 已成为治疗癌症的重要机制。
本研究采用分子模拟研究（如 CoMFA、CoMFA-RF、CoMSIA 和 HQSAR）、分子对接和分子动力学（MD）模拟对一些反式环丙胺衍生物作为 LSD1 抑制剂进行了研究。
通过 SYBYL-X2.1.1 程序，对一系列反式环丙胺衍生物作为数据集进行 QSAR 模型研究。分子对接和 MD 模拟分别由 MOE 软件和 SYBYL 程序进行。通过评估训练集和测试集分子的交叉验证相关系数（q）、非交叉验证相关系数（r）和预测相关系数（r），对这些 LSD1 抑制剂生成模型的内部和外部预测性能进行了验证。
CoMFA（q，0.670；r，0.930；r，0.968）、CoMFA-RF（q，0.694；r，0.926；r，0.927）、CoMSIA（q，0.834；r，0.956；r，0.958）和 HQSAR 模型（q，0.854；r，0.900；r，0.728）对训练集和测试集的 LSD1 抑制均有显著发现。
这些 QSAR 模型被发现具有较好的预测能力，且非常稳健。所有模型的等高线图均已生成，通过分子对接研究和分子动力学模拟证明，疏水性、静电和氢键场在这些模型中对于提高结合亲和力和确定结构-活性关系至关重要。这些理论结果可能有助于设计新的具有增强活性的强 LSD1 抑制剂，以治疗癌症。
