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Decoding of novel missense TSC2 gene variants using in-silico methods.
BMC Medical Genetics Pub Date : 2019-10-26 , DOI: 10.1186/s12881-019-0891-y
Shruthi Sudarshan 1 , Manoj Kumar 2 , Punit Kaur 2 , Atin Kumar 3 , Sethuraman G 4 , Savita Sapra 1 , Sheffali Gulati 5 , Neerja Gupta 1 , Madhulika Kabra 1 , Madhumita Roy Chowdhury 1
Affiliation  

BACKGROUND Mutations in TSC1 or TSC2 gene cause tuberous sclerosis complex (TSC), an autosomal dominant disorder characterized by the formation of non-malignant hamartomas in multiple vital organs. TSC1 and TSC2 gene products form TSC heterodimer that senses specific cell growth conditions to control mTORC1 signalling. METHODS In the present study 98 TSC patients were tested for variants in TSC1 and TSC2 genes and 14 novel missense variations were identified. The pathogenecity of these novel variations was determined by applying different bioinformatics tools involving computer aided protein modeling. RESULTS Protein modelling could be done only for ten variants which were within the functional part of the protein. Homology modeling is the most reliable method for structure prediction of a protein. Since no sequence homology structure was available for the tuberin protein, three dimensional structure was modeled by a combination of homology modeling and the predictive fold recognition and threading method using Phyre2 threading server. The best template structures for model building of the TSC1 interacting domain, tuberin domain and GAP domain are the crystal structures of clathrin adaptor core protein, Rap1GAP catalytic domain and Ser/Thr kinase Tor protein respectively. CONCLUSIONS In this study, an attempt has been made to assess the impact of each novel missense variant based on their TSC1-TSC2 hydrophobic interactions and its effect on protein function.

中文翻译:

使用计算机模拟方法对新型错义TSC2基因变体进行解码。

背景技术TSC1或TSC2基因的突变会导致结节性硬化症(TSC),这是一种常染色体显性遗传疾病,其特征是在多个重要器官中形成了非恶性错构瘤。TSC1和TSC2基因产物形成TSC异源二聚体,该异源二聚体感测特定的细胞生长条件以控制mTORC1信号传导。方法在本研究中,对98例TSC患者的TSC1和TSC2基因变异进行了测试,并鉴定了14种新的错义变异。这些新颖变异的致病性是通过应用涉及计算机辅助蛋白质建模的不同生物信息学工具来确定的。结果只能对蛋白质功能部分内的十个变体进行蛋白质建模。同源性建模是预测蛋白质结构的最可靠方法。由于该蛋白没有序列同源性结构,因此通过同源性建模以及使用Phyre2穿线服务器的预测性折叠识别和穿线方法的组合,可以对三维结构进行建模。用于建立TSC1相互作用域,tuberin域和GAP域的模型的最佳模板结构分别是网格蛋白衔接子核心蛋白,Rap1GAP催化域和Ser / Thr激酶Tor蛋白的晶体结构。结论在这项研究中,已尝试基于其TSC1-TSC2疏水相互作用及其对蛋白质功能的影响,评估每种新型错义变体的影响。通过同源性建模以及使用Phyre2穿线服务器的预测性折痕识别和穿线方法,对三维结构进行建模。用于建立TSC1相互作用域,tuberin域和GAP域的模型的最佳模板结构分别是网格蛋白衔接子核心蛋白,Rap1GAP催化域和Ser / Thr激酶Tor蛋白的晶体结构。结论在这项研究中,已尝试基于其TSC1-TSC2疏水相互作用及其对蛋白质功能的影响,评估每种新型错义变体的影响。通过同源性建模以及使用Phyre2穿线服务器的预测性折痕识别和穿线方法,对三维结构进行建模。用于建立TSC1相互作用域,tuberin域和GAP域的模型的最佳模板结构分别是网格蛋白衔接子核心蛋白,Rap1GAP催化域和Ser / Thr激酶Tor蛋白的晶体结构。结论在这项研究中,已尝试基于其TSC1-TSC2疏水相互作用及其对蛋白质功能的影响,评估每种新型错义变体的影响。
更新日期:2019-10-26
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