Homology Modeling of Human Concentrative Nucleoside Transporters (hCNTs) and Validation by Virtual Screening and Experimental Testing to Identify Novel hCNT1 Inhibitors.
Homology Modeling of Human Concentrative Nucleoside Transporters (hCNTs) and Validation by Virtual Screening and Experimental Testing to Identify Novel hCNT1 Inhibitors.
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DOI：
10.4172/2169-0138.1000146
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发表时间：
2017-03-01
期刊：
影响因子：
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通讯作者：
Buolamwini, John K
中科院分区：
文献类型：
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作者：
Kumar Deokar, Hemant;Barch, Hilaire Playa;Buolamwini, John K
目的：核苷转运蛋白家族是肿瘤、病毒和心血管疾病的新靶点。由于膜蛋白的表达、分离和结晶的困难，缺乏关于任何哺乳动物和人类蛋白的结构信息。因此，本研究的目的是建立三个克隆的核苷浓度转运蛋白hCNT1，hCNT2和hCNT3的同源模型，并验证它们对发现急需的抑制剂和探针的筛选。最近报道的霍乱弧菌浓缩核苷转运蛋白（vcCNT）的晶体结构，与人CNT直向同源物具有令人满意的相似性，因此用作模板来构建所有三种hCNT的同源模型。薛定谔模型套件用于练习。同源性模型的外部验证是通过使用诱导拟合对接（IDF）方法和Glide对接程序在推定的结合位点对接一组最近报道的已知hCNT1核苷类抑制剂来进行的。然后，hCNT1同源性模型随后被用来进行360，000化合物库的虚拟筛选，并获得了172个化合物和生物学评价的hCNT 1，2和3的抑制效力和selectivity.RESULTS：良好质量的同源性模型获得所有三个hCNT的各种结构参数的询问所示，也通过已知抑制剂的对接外部验证。IDF对接结果显示，IDF分数和抑制活性之间有良好的相关性，特别是对hCNT1。从用hCNT1同源性模型通过虚拟筛选排名的前0.1%的化合物中，选择172种化合物并针对hCNT1、hCNT2和hCNT3进行测试，产生了14种新的hCNT1、hCNT2和hCNT3抑制剂（命中）（即，8%的成功率）。最具活性的化合物表现出9.05 μ M的IC50，这表明了大于25倍的效力比根皮苷的标准CNT抑制剂（IC50为250 μ M）。结论：我们成功地进行了同源建模和验证所有的人浓度核苷转运蛋白（hCNT 1，2和3）。使用hCNT1模型也获得了这些模型有希望用于虚拟筛选以鉴定有效和选择性抑制剂的概念验证。因此，我们确定了一种新的有效的hCNT1抑制剂，它比标准抑制剂根皮苷更有效，选择性更高。其他hCNT1命中也大多表现出选择性。这些同源性模型应该是有用的虚拟筛选，以确定新的hCNT抑制剂，以及优化的hCNT抑制剂。
OBJECTIVE: The nucleoside transporter family is an emerging target for cancer, viral and cardiovascular diseases. Due to the difficulty in the expression, isolation and crystallization of membrane proteins, there is a lack of structural information on any of the mammalian and for that matter the human proteins. Thus the objective of this study was to build homology models for the three cloned concentrative nucleoside transporters hCNT1, hCNT2 and hCNT3 and validate them for screening towards the discovery of much needed inhibitors and probes.METHODS: The recently reported crystal structure of the Vibrio cholerae concentrative nucleoside transporter (vcCNT), has satisfactory similarity to the human CNT orthologues and was thus used as a template to build homology models of all three hCNTs. The Schrodinger modeling suite was used for the exercise. External validation of the homology models was carried out by docking a set of recently reported known hCNT1 nucleoside class inhibitors at the putative binding site using induced fit docking (IDF) methodology with the Glide docking program. Then, the hCNT1 homology model was subsequently used to conduct a virtual screening of a 360,000 compound library, and 172 compounds were obtained and biologically evaluated for hCNT 1, 2 and 3 inhibitory potency and selectivity.RESULTS: Good quality homology models were obtained for all three hCNTs as indicated by interrogation for various structural parameters and also external validated by docking of known inhibitors. The IDF docking results showed good correlations between IDF scores and inhibitory activities; particularly for hCNT1. From the top 0.1% of compounds ranked by virtual screening with the hCNT1 homology model, 172 compounds selected and tested for against hCNT1, hCNT2 and hCNT3, yielded 14 new inhibitors (hits) of (i.e., 8% success rate). The most active compound exhibited an IC50 of 9.05 muM, which shows a greater than 25-fold higher potency than phlorizin the standard CNT inhibitor (IC50 of 250 muM).CONCLUSION: We successfully undertook homology modeling and validation for all human concentrative nucleoside transporters (hCNT 1, 2 and 3). The proof-of-concept that these models are promising for virtual screening to identify potent and selective inhibitors was also obtained using the hCNT1 model. Thus we identified a novel potent hCNT1 inhibitor that is more potent and more selective than the standard inhibitor phlorizin. The other hCNT1 hits also mostly exhibited selectivity. These homology models should be useful for virtual screening to identify novel hCNT inhibitors, as well as for optimization of hCNT inhibitors.