Materials and Process Simulation Center (MC139-74), California Institute of Technology, 1200 E. California Blvd., Pasadena, California 91125, USA.
J Chem Inf Model. 2011 Dec 27;51(12):3262-74. doi: 10.1021/ci200435b. Epub 2011 Nov 16.
Histamine receptors (HRs) are excellent drug targets for the treatment of diseases, such as schizophrenia, psychosis, depression, migraine, allergies, asthma, ulcers, and hypertension. Among them, the human H(3) histamine receptor (hH(3)HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer's disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity. However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity and activity for such treatments, it would be useful to have the three-dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H(1), H(2), H(3), and H(4)) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol, sampling ∼35 million combinations of helix packings to predict the 10 most stable packings for each of the four subtypes. Then we used these 10 best protein structures with the DarwinDock Monte Carlo protocol to sample ∼50 000 × 10(20) poses to predict the optimum ligand-protein structures for various agonists and antagonists. We find that E206(5.46) contributes most in binding H(3) selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/S5.43 in both of hH(3)HR and hH(4)HR are involved in H(3)/ H(4) subtype selectivity. In addition, we find that M378(6.55) in hH(3)HR provides additional hydrophobic interactions different from hH(4)HR (the corresponding amino acid of T323(6.55) in hH(4)HR) to provide additional subtype bias. From these studies, we developed a pharmacophore model based on our predictions for known hH(3)HR selective antagonists in clinical study [ABT-239 1, GSK-189,254 2, PF-3654746 3, and BF2.649 (tiprolisant) 4] that suggests critical selectivity directing elements are: the basic proton interacting with D114(3.32), the spacer, the aromatic ring substituted with the hydrophilic or lipophilic groups interacting with lipophilic pockets in transmembranes (TMs) 3-5-6 and the aliphatic ring located in TMs 2-3-7. These 3D structures for all four HRs should help guide the rational design of novel drugs for the subtype selective antagonists and agonists with reduced side effects.
组胺受体(HRs)是治疗精神分裂症、精神病、抑郁症、偏头痛、过敏、哮喘、溃疡和高血压等疾病的优秀药物靶点。其中,人 H(3)组胺受体(hH(3)HR)拮抗剂已被提议用于特定的治疗应用,包括治疗阿尔茨海默病、注意缺陷多动障碍(ADHD)、癫痫和肥胖症。然而,许多这些候选药物通过与其他组胺受体亚型的交叉反应而引起不良的副作用。为了开发针对这些治疗方法的更高选择性和活性,了解所有四种 HR 亚型(H(1)、H(2)、H(3)和 H(4))的三维结构将非常有用。我们使用 GEnSEMBLE(膜双层环境中的 GPCR 整体结构)蒙特卡罗协议报告了这四种 HR 亚型的预测结构,该协议对螺旋包装的组合进行了约 3500 万次采样,以预测每种亚型的 10 种最稳定的包装。然后,我们使用这些 10 种最佳蛋白质结构和 DarwinDock 蒙特卡罗协议对各种激动剂和拮抗剂进行了约 50000×10(20)次构象采样,以预测最佳配体-蛋白质结构。我们发现 E206(5.46)在与实验突变研究一致的情况下,对 H(3)选择性激动剂(5、6、7)的结合贡献最大。我们还发现 hH(3)HR 和 hH(4)HR 中的保守 E5.46/S5.43 都参与了 H(3)/H(4)亚型选择性。此外,我们发现 hH(3)HR 中的 M378(6.55)提供了不同于 hH(4)HR(hH(4)HR 中的相应氨基酸为 T323(6.55))的额外疏水性相互作用,以提供额外的亚型偏倚。从这些研究中,我们根据临床研究中已知的 hH(3)HR 选择性拮抗剂[ABT-239 1、GSK-189、254 2、PF-3654746 3 和 BF2.649(tiprolisant)4]建立了一个基于我们预测的药效团模型,该模型表明关键的选择性定向元素为:与 D114(3.32)相互作用的质子、间隔物、与跨膜(TMs)3-5-6 中疏脂口袋相互作用的取代有亲水或亲脂性基团的芳环以及位于 TMs 2-3-7 中的脂环。这四种 HR 的所有这些 3D 结构都应该有助于指导新型药物的合理设计,以开发具有减少副作用的新型亚型选择性拮抗剂和激动剂。