Doss C George Priya, Nagasundaram N
Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India,
Cell Biochem Biophys. 2014 Nov;70(2):939-56. doi: 10.1007/s12013-014-0002-9.
Fanconi anemia (FA) is an autosomal recessive human disease characterized by genomic instability and a marked increase in cancer risk. The importance of FANCD1 gene is manifested by the fact that deleterious amino acid substitutions were found to confer susceptibility to hereditary breast and ovarian cancers. Attaining experimental knowledge about the possible disease-associated substitutions is laborious and time consuming. The recent introduction of genome variation analyzing in silico tools have the capability to identify the deleterious variants in an efficient manner. In this study, we conducted in silico variation analysis of deleterious non-synonymous SNPs at both functional and structural level in the breast cancer and FA susceptibility gene BRCA2/FANCD1. To identify and characterize deleterious mutations in this study, five in silico tools based on two different prediction methods namely pathogenicity prediction (SIFT, PolyPhen, and PANTHER), and protein stability prediction (I-Mutant 2.0 and MuStab) were analyzed. Based on the deleterious scores that overlap in these in silico approaches, and the availability of three-dimensional structures, structure analysis was carried out with the major mutations that occurred in the native protein coded by FANCD1/BRCA2 gene. In this work, we report the results of the first molecular dynamics (MD) simulation study performed to analyze the structural level changes in time scale level with respect to the native and mutated protein complexes (G25R, W31C, W31R in FANCD1/BRCA2-PALB2, and F1524V, V1532F in FANCD1/BRCA2-RAD51). Analysis of the MD trajectories indicated that predicted deleterious variants alter the structural behavior of BRCA2-PALB2 and BRCA2-RAD51 protein complexes. In addition, statistical analysis was employed to test the significance of these in silico tool predictions. Based on these predictions, we conclude that the identification of disease-related SNPs by in silico methods, in combination with MD approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. The methods reviewed here generated a considerable amount of valuable data, but also the need for further validation.
范可尼贫血（FA）是一种常染色体隐性人类疾病，其特征为基因组不稳定以及癌症风险显著增加。FANCD1基因的重要性体现在以下事实上：已发现有害的氨基酸替代会使人易患遗传性乳腺癌和卵巢癌。获取有关可能与疾病相关的替代的实验知识既费力又耗时。最近引入的基因组变异计算机分析工具能够以高效的方式识别有害变异。在本研究中，我们对乳腺癌和FA易感基因BRCA2/FANCD1中有害非同义单核苷酸多态性（SNP）在功能和结构水平上进行了计算机变异分析。为了在本研究中识别和表征有害突变，我们分析了基于两种不同预测方法的五种计算机工具，即致病性预测（SIFT、PolyPhen和PANTHER）以及蛋白质稳定性预测（I-Mutant 2.0和MuStab）。基于这些计算机方法中重叠的有害分数以及三维结构的可用性，我们对由FANCD1/BRCA2基因编码的天然蛋白质中发生的主要突变进行了结构分析。在这项工作中，我们报告了首次进行的分子动力学（MD）模拟研究的结果，该研究旨在分析天然和突变蛋白质复合物（FANCD1/BRCA2-PALB2中的G25R、W31C、W31R以及FANCD1/BRCA2-RAD51中的F1524V、V1532F）在时间尺度水平上的结构水平变化。MD轨迹分析表明，预测的有害变异会改变BRCA2-PALB2和BRCA2-RAD51蛋白质复合物的结构行为。此外，我们采用统计分析来检验这些计算机工具预测的显著性。基于这些预测，我们得出结论，通过计算机方法结合MD方法识别与疾病相关的SNP有潜力创建用于疾病诊断、预后和治疗的个性化工具。这里回顾的方法产生了大量有价值的数据，但也需要进一步验证。
