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基于计算机的 GD2 研究进展:小儿神经母细胞瘤的潜在靶点

In Silico Insights on GD2 : A Potential Target for Pediatric Neuroblastoma.

机构信息

In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India.

Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India.

出版信息

Curr Top Med Chem. 2019;19(30):2766-2781. doi: 10.2174/1568026619666191112115333.

Abstract

BACKGROUND

Originating from the abnormal growth of neuroblasts, pediatric neuroblastoma affects the age group below 15 years. It is an aggressive heterogenous cancer with a high morbidity rate. Biological marker GD2 synthesised by the GD2 gene acts as a powerful predictor of neuroblastoma cells. GD2 gangliosides are sialic acid-containing glycosphingolipids. Differential expression during brain development governs the function of the GD2. The present study explains the interaction of the GD2 with its established inhibitors and discovers the compound having a high binding affinity against the target protein. Technically, during the development of new compounds through docking studies, the best drug among all pre-exist inhibitors was filtered. Hence in reference to the best docked compound, the study proceeded further.

METHODOLOGY

The In silico approach provides a platform to determine and establish potential inhibitor against GD2 in Pediatric neuroblastoma. The 3D structure of GD2 protein was modelled by homology base fold methods using Smith-Watermans' Local alignment. A total of 18 established potent compounds were subjected to molecular docking and Etoposide (CID: 36462) manifested the highest affinity. The similarity search presented 336 compounds similar to Etoposide.

RESULTS

Through virtual screening, the compound having PubChem ID 10254934 showed a better affinity towards GD2 than the established inhibitor. The comparative profiling of the two compounds based on various interactions such as H-bond interaction, aromatic interactions, electrostatic interactions and ADMET profiling and toxicity studies were performed using various computational tools.

CONCLUSION

The docking separated the virtual screened drug (PubChemID: 10254934) from the established inhibitor with a better re-rank score of -136.33. The toxicity profile of the virtual screened drug was also lesser (less lethal) than the established drug. The virtual screened drug was observed to be bioavailable as it does not cross the blood-brain barrier. Conclusively, the virtual screened compound obtained in the present investigation is better than the established inhibitor and can be further augmented by In vitro analysis, pharmacodynamics and pharmacokinetic studies.

摘要

背景

起源于神经母细胞的异常生长,小儿神经母细胞瘤影响 15 岁以下的年龄组。它是一种具有高发病率的侵袭性异质性癌症。由 GD2 基因合成的生物标志物 GD2 作为神经母细胞瘤细胞的强大预测因子。GD2 神经节苷脂是含有唾液酸的糖脂。在大脑发育过程中的差异表达控制 GD2 的功能。本研究解释了 GD2 与其已建立的抑制剂的相互作用,并发现了一种对靶蛋白具有高结合亲和力的化合物。从技术上讲,在通过对接研究开发新化合物的过程中,从所有现有抑制剂中筛选出最佳药物。因此,参考最佳对接化合物,进一步进行了研究。

方法

计算方法提供了一个平台,用于确定和建立针对小儿神经母细胞瘤中 GD2 的潜在抑制剂。使用 Smith-Watermans 局部比对同源折叠方法对 GD2 蛋白的 3D 结构进行建模。总共对 18 种已建立的有效化合物进行了分子对接,依托泊苷(CID:36462)表现出最高的亲和力。相似性搜索显示了 336 种与依托泊苷相似的化合物。

结果

通过虚拟筛选,具有 PubChem ID 10254934 的化合物对 GD2 的亲和力优于已建立的抑制剂。基于各种相互作用,如氢键相互作用、芳香相互作用、静电相互作用和 ADMET 分析和毒性研究,对两种化合物进行了比较分析,并使用各种计算工具进行了分析。

结论

对接将虚拟筛选药物(PubChemID:10254934)与具有更好重排分数-136.33 的已建立抑制剂分开。虚拟筛选药物的毒性谱也较小(较少致命)比已建立的药物。观察到虚拟筛选药物是可生物利用的,因为它不会穿过血脑屏障。总之,本研究中获得的虚拟筛选化合物优于已建立的抑制剂,可以通过体外分析、药效学和药代动力学研究进一步增强。

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