Xu Guanhong, Chen Yue, Shen Kun, Wang Xiuzhen, Li Fei, He Yan
School of Pharmacy, Nanjing Medical University, Nanjing 210029, China.
Department of Internal Neurology, Nanjing Children's Hospital Affiliated to Nanjing Medical University, Nanjing 210008, China.
Int J Mol Sci. 2014 May 14;15(5):8553-69. doi: 10.3390/ijms15058553.
Neuronal nitric oxide synthase (nNOS) plays an important role in neurotransmission and smooth muscle relaxation. Selective inhibition of nNOS over its other isozymes is highly desirable for the treatment of neurodegenerative diseases to avoid undesirable effects. In this study, we present a workflow for the identification and prioritization of compounds as potentially selective human nNOS inhibitors. Three-dimensional pharmacophore models were constructed based on a set of known nNOS inhibitors. The pharmacophore models were evaluated by Pareto surface and CoMFA (Comparative Molecular Field Analysis) analyses. The best pharmacophore model, which included 7 pharmacophore features, was used as a search query in the SPECS database (SPECS®, Delft, The Netherlands). The hit compounds were further filtered by scoring and docking. Ten hits were identified as potential selective nNOS inhibitors.
神经元型一氧化氮合酶(nNOS)在神经传递和平滑肌舒张中起重要作用。相对于其他同工酶,选择性抑制nNOS对于治疗神经退行性疾病非常必要,以避免不良影响。在本研究中,我们提出了一种用于鉴定和优先排序潜在选择性人nNOS抑制剂化合物的工作流程。基于一组已知的nNOS抑制剂构建了三维药效团模型。通过帕累托表面和比较分子场分析(CoMFA)对药效团模型进行评估。包含7个药效团特征的最佳药效团模型被用作荷兰代尔夫特的SPECS数据库(SPECS®)中的搜索查询。通过评分和对接对命中的化合物进行进一步筛选。鉴定出10种命中化合物为潜在的选择性nNOS抑制剂。