Department of Pharmacoinformatics and National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, India.
Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, India.
J Comput Biol. 2021 Jan;28(1):43-59. doi: 10.1089/cmb.2019.0332. Epub 2020 Mar 25.
Dihydrofolate reductase (DHFR) is a well-known enzyme of the folate metabolic pathway and it is a validated drug target for leishmaniasis. However, only a few leads are reported against DHFR (DHFR), and thus, there is a need to identify new inhibitors. In this article, pharmacoinformatic tools such as molecular docking, virtual screening, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, and molecular dynamics (MD) simulations were utilized to identify potential DHFR inhibitors. Initially, a natural DHFR substrate (dihydrofolate), a classical DHFR inhibitor (methotrexate), and a potent DHFR inhibitor, that is, "5-(3-(octyloxy)benzyl)pyrimidine-2,4-diamine" (LEAD) were docked in the active site of the DHFR and MD simulated to understand the binding mode characteristics of the substrates/inhibitors in the DHFR. The shape of the LEAD molecule was used as a query for shape-based virtual screening, while the three-dimensional structure of DHFR was utilized for docking-based virtual screening. In silico ADMET factors were also considered during virtual screening. These two screening processes yielded 25 suitable hits, which were further validated for their selectivity toward DHFR using molecular docking and prime molecular mechanics/generalized born surface area analysis in the human DHFR (DHFR). Best six hits, which were selective and energetically favorable for the DHFR, were chosen for MD simulations. The MD analysis showed that four of the hits exhibited very good binding affinity for DHFR with respect to DHFR, and two hits were found to be more selective than the reported potent DHFR inhibitor. The present study thus identifies hits that can be further designed and modified as potent DHFR inhibitors.
二氢叶酸还原酶(DHFR)是叶酸代谢途径中一种众所周知的酶,也是利什曼病的有效药物靶点。然而,针对 DHFR(DHFR)仅有少数报道的先导化合物,因此,需要识别新的抑制剂。在本文中,利用药理学工具,如分子对接、虚拟筛选、吸收、分布、代谢、排泄和毒性(ADMET)分析和分子动力学(MD)模拟,来识别潜在的 DHFR 抑制剂。最初,将天然的 DHFR 底物(二氢叶酸)、经典的 DHFR 抑制剂(甲氨蝶呤)和一种有效的 DHFR 抑制剂,即“5-(3-(辛氧基)苄基)嘧啶-2,4-二胺”(LEAD)对接进 DHFR 的活性部位,并进行 MD 模拟,以了解底物/抑制剂在 DHFR 中的结合模式特征。将 LEAD 分子的形状用作基于形状的虚拟筛选的查询,而 DHFR 的三维结构则用于基于对接的虚拟筛选。在虚拟筛选过程中还考虑了计算机辅助药物设计(ADMET)因素。这两种筛选过程共产生了 25 个合适的候选物,并用分子对接和基于 prime 的分子力学/广义 Born 表面面积分析(在人源 DHFR(DHFR)中)进一步验证了它们对 DHFR 的选择性。选择了对 DHFR 具有选择性和能量优势的最佳的六个候选物进行 MD 模拟。MD 分析表明,四个候选物与 DHFR 结合具有非常好的亲和力,并且有两个候选物比报道的有效的 DHFR 抑制剂更具选择性。因此,本研究确定了可进一步设计和修饰为有效的 DHFR 抑制剂的候选物。