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首页> 外文期刊>Frontiers in Chemistry >Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2
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Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2

机译：将配体和基于目标驱动的基于目标驱动的虚拟筛选方法与体外人体细胞系模型和时间分辨荧光共振能量转移测定法识别针对Bcl-2的新型麦芽化合物

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Antiapoptotic members of BCL-2 family proteins are one of the overexpressed proteins in cancer cells that are oncogenic targets that raise hopes for new therapeutic discoveries. Here, we have used multi-step screening and filtering approaches that combine structure and ligand-based drug design to identify new, effective BCL-2 inhibitors from small molecules database Specs SC. Compounds are first filtered based on binary “cancer-QSAR” model and common 26 toxicity QSAR models. Non-toxic compounds are considered for target-driven studies and here we have applied two different approaches to select hit compounds for further in vitro human cell line studies. In the first approach, a forward molecular docking and filtering approach is used to rank compounds based on their docking scores and only top-ranked a few molecules are selected for further long (100-ns) molecular simulations (MD) and in vitro tests. Docking algorithms though can be promising in predicting binding poses, they can be less prone to precisely predict ranking of compounds leading to decrease in the success rate of in silico studies. Hence, in the second approach, top-docking poses of each compound filtered through QSAR studies are subjected to initially short (1ns) MD simulations and their binding energies are calculated via MM/GBSA method. Then, the compounds are ranked based on their MM/GBSA energy values to select hit molecules for further long MD simulations and in vitro studies. Additionally, we have applied text-mining approaches to identify molecules that contain indol phased as many of the approved drugs contain indol derivatives. Around 2700 compounds are filtered based on “cancer-QSAR” model and are then docked into BCL-2 protein. Short MD simulations are performed for the top-docking poses for each compound in complex with BCL-2. The complexes are again ranked based their MM/GBSA values to select hit molecules for further long MD simulations and in vitro studies. In total, seven molecules are subjected to biological activity tests in various human cancer cell lines. Inhibitory concentrations are evaluated, and biological activities and apoptotic potentials were assessed by cell culture studies. Four molecules are found to be limiting the proliferation capacity of cancer cells while increasing the apoptotic cell fractions.
机译：Bcl-2家族蛋白的抗曝光成员是癌细胞中的过表达蛋白之一，是致癌目标，其提高对新的治疗发现的希望。在这里，我们使用了组合结构和配体的药物设计的多步筛选和过滤方法来识别来自小分子数据库SC的新的有效的Bcl-2抑制剂。基于二元“癌症 - QSAR”模型和共同的26个毒性QSAR模型，首先过滤化合物。无毒化合物被认为是针对目标驱动的研究，并且在这里我们施加了两种不同的方法来选择麦片化合物以进一步体外的人体细胞系研究。在第一种方法中，前向分子对接和过滤方法用于基于它们的对接分数对化合物进行排序，并且仅为进一步的长（100-ns）分子模拟（MD）和体外测试仅选择少量分子。停靠算法虽然可以在预测结合姿势方面有望，但它们可以不太容易预测化合物的排名，导致硅研究中的成功率降低。因此，在第二种方法中，通过QSAR研究过滤的每种化合物的顶部对接姿势初始短（1ns）MD模拟，并且它们的结合能量通过MM / GBSA方法计算。然后，将化合物基于其MM / GBSA能量值来排序，以选择进一步的LONG MD模拟和体外研究的命中分子。此外，我们已经应用了文本挖掘方法，以鉴定含有吲哚醇等许多批准药物含有吲哚衍生物的分子的分子。基于“癌症-QSAR”模型过滤约2700种化合物，然后将其停靠在Bcl-2蛋白中。对与BCL-2复合物中的每个化合物的顶部对接姿势进行短MD模拟。复合物再次被评为其MM / GBSA值，以选择击中分子以进一步的MD MD模拟和体外研究。总共七种分子在各种人类癌细胞系中进行生物活性试验。评价抑制浓度，通过细胞培养研究评估生物活性和凋亡电位。发现四种分子限制癌细胞的增殖能力，同时增加凋亡细胞级分。

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