Tutumlu Gurbet, Dogan Berna, Avsar Timucin, Orhan Muge Didem, Calis Seyma, Durdagi Serdar
Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey.
Department of Medical Biology, Bahcesehir University, School of Medicine, Istanbul, Turkey.
Front Chem. 2020 Apr 9;8:167. doi: 10.3389/fchem.2020.00167. eCollection 2020.
Antiapoptotic members of -/-2 (BCL-2) family proteins are one of the overexpressed proteins in cancer cells that are oncogenic targets. As such, targeting of BCL-2 family proteins raises hopes for new therapeutic discoveries. Thus, we used multistep screening and filtering approaches that combine structure and ligand-based drug design to identify new, effective BCL-2 inhibitors from a small molecule database (Specs SC), which includes more than 210,000 compounds. This database is first filtered based on binary " model constructed with 886 training and 167 test set compounds and common 26 toxicity quantitative structure-activity relationships (QSAR) models. Predicted non-toxic compounds are considered for target-driven studies. Here, we applied two different approaches to filter and select hit compounds for further biological assays and human cell line experiments. In the first approach, a molecular docking and filtering approach is used to rank compounds based on their docking scores and only a few top-ranked molecules are selected for further long (100-ns) molecular dynamics (MD) simulations and tests. While docking algorithms are promising in predicting binding poses, they can be less prone to precisely predict ranking of compounds leading to decrease in the success rate of studies. Hence, in the second approach, top-docking poses of each compound filtered through QSAR studies are subjected to initially short (1 ns) MD simulations and their binding energies are calculated via molecular mechanics generalized Born surface area (MM/GBSA) method. Then, the compounds are ranked based on their average MM/GBSA energy values to select hit molecules for further long MD simulations and studies. Additionally, we have applied text-mining approaches to identify molecules that contain "" phrase as many of the approved drugs contain indole and indol derivatives. Around 2700 compounds are filtered based on " model and are then docked into BCL-2. Short MD simulations are performed for the top-docking poses for each compound in complex with BCL-2. The complexes are again ranked based on their MM/GBSA values to select hit molecules for further long MD simulations and studies. In total, seven molecules are subjected to biological activity tests in various human cancer cell lines as well as Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assay. Inhibitory concentrations are evaluated, and biological activities and apoptotic potentials are assessed by cell culture studies. Four molecules are found to be limiting the proliferation capacity of cancer cells while increasing the apoptotic cell fractions.
-/-2(BCL-2)家族蛋白的抗凋亡成员是癌细胞中过表达的蛋白之一,是致癌靶点。因此,靶向BCL-2家族蛋白为新的治疗发现带来了希望。于是,我们采用了多步筛选和过滤方法,将基于结构和配体的药物设计相结合,从一个包含超过210,000种化合物的小分子数据库(Specs SC)中识别新的、有效的BCL-2抑制剂。该数据库首先基于用886种训练和167种测试集化合物构建的二元“模型”以及26种常见的毒性定量构效关系(QSAR)模型进行过滤。预测无毒的化合物用于目标驱动的研究。在这里,我们应用了两种不同的方法来过滤和选择命中化合物,以进行进一步的生物学测定和人类细胞系实验。在第一种方法中,使用分子对接和过滤方法根据化合物的对接分数对其进行排名,仅选择少数排名靠前的分子进行进一步的长时间(100纳秒)分子动力学(MD)模拟和测试。虽然对接算法在预测结合姿势方面很有前景,但它们可能不太容易精确预测化合物的排名,从而导致研究成功率降低。因此,在第二种方法中,通过QSAR研究过滤的每种化合物的顶级对接姿势先进行短时间(1纳秒)的MD模拟,并通过分子力学广义玻恩表面积(MM/GBSA)方法计算其结合能。然后,根据化合物的平均MM/GBSA能量值对其进行排名,以选择命中分子进行进一步的长时间MD模拟和研究。此外,我们应用了文本挖掘方法来识别包含“”短语的分子,因为许多已批准的药物都含有吲哚和吲哚衍生物。基于“模型”过滤了大约2700种化合物,然后将其对接至BCL-2。对每种与BCL-2复合的化合物的顶级对接姿势进行短时间MD模拟。再次根据复合物的MM/GBSA值对其进行排名,以选择命中分子进行进一步的长时间MD模拟和研究。总共对七种分子在各种人类癌细胞系以及时间分辨荧光共振能量转移(TR-FRET)测定中进行了生物活性测试。评估抑制浓度,并通过细胞培养研究评估生物活性和凋亡潜力。发现有四种分子在限制癌细胞增殖能力的同时增加了凋亡细胞比例。