Niu Miaomiao, Wang Fengzhen, Li Fang, Dong Yaru, Gu Yueqing
Department of Biomedical Engineering, School of Life Science and Technology, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
J Taiwan Inst Chem Eng. 2015 Apr;49:19-26. doi: 10.1016/j.jtice.2014.11.028. Epub 2014 Dec 31.
Inhibitors of aminopeptidase N (APN) have been thought as potential drugs for the treatment of tumor angiogenesis, invasion and metastasis and a considerable number of APN inhibitors have been reported recently. To clarify the essential structure-activity relationship for the APN inhibitors as well as identify new potent leads against APN, pharmacophore models were established using structure- and common feature-based approaches and validated with a database of active and inactive compounds. These validated pharmacophores were then used in database screening for novel virtual leads. The hit compounds were further subjected to molecular docking studies to refine the retrieved hits. Finally, six structurally diverse compounds that showed strong interactions with the key amino acids and the zinc ion were selected for biological evaluation, where two hits showed more than 70% inhibition against APN at 60 μM concentration. The evaluation results show the potential of our screening approach in identifying APN inhibitors.
氨肽酶N(APN)抑制剂被认为是治疗肿瘤血管生成、侵袭和转移的潜在药物,最近已有相当数量的APN抑制剂被报道。为阐明APN抑制剂的基本构效关系,并鉴定针对APN的新的有效先导化合物,采用基于结构和共同特征的方法建立了药效团模型,并用活性和非活性化合物数据库进行了验证。然后将这些经过验证的药效团用于数据库筛选,以寻找新的虚拟先导化合物。对命中的化合物进一步进行分子对接研究,以优化检索到的命中结果。最后,选择了六种与关键氨基酸和锌离子表现出强烈相互作用的结构多样的化合物进行生物学评价,其中两种命中化合物在60μM浓度下对APN的抑制率超过70%。评价结果显示了我们的筛选方法在鉴定APN抑制剂方面的潜力。