School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
College of Health Sciences, Department of Biomedical Sciences, Qatar University, Doha, Qatar.
Metab Brain Dis. 2018 Dec;33(6):1823-1834. doi: 10.1007/s11011-018-0285-4. Epub 2018 Jul 13.
Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the mutations in survival motor neuron 1 gene (SMN1). The molecular pathology of missense mutations in SMN1 is not thoroughly investigated so far. Therefore, we collected all missense mutations in the SMN1 protein, using all possible search terms, from three databases (PubMed, PMC and Google Scholar). All missense mutations were subjected to in silico pathogenicity, conservation, and stability analysis tools. We used statistical analysis as a QC measure for validating the specificity and accuracy of these tools. PolyPhen-2 demonstrated the highest specificity and accuracy. While PolyPhen-1 showed the highest sensitivity; overall, PolyPhen2 showed better measures in comparison to other in silico tools. Three mutations (D44V, Y272C, and Y277C) were identified as the most pathogenic and destabilizing. Further, we compared the physiochemical properties of the native and the mutant amino acids and observed loss of H-bonds and aromatic stacking upon the cysteine to tyrosine substitution, which led to the loss of aromatic rings and may reduce protein stability. The three mutations were further subjected to Molecular Dynamics Simulation (MDS) analysis using GROMACS to understand the structural changes. The Y272C and Y277C mutants exhibited maximum deviation pattern from the native protein as compared to D44V mutant. Further MDS analysis predicted changes in the stability that may have been contributed due to the loss of hydrogen bonds as observed in intramolecular hydrogen bond analysis and physiochemical analysis. A loss of function/structural impact was found to be severe in the case of Y272C and Y277C mutants in comparison to D44V mutation. Correlating the results from in silico predictions, physiochemical analysis, and MDS, we were able to observe a loss of stability in all the three mutants. This combinatorial approach could serve as a platform for variant interpretation and drug design for spinal muscular dystrophy resulting from missense mutations.
脊髓性肌萎缩症（SMA）是一种由生存运动神经元 1 基因（SMN1）突变引起的神经肌肉疾病。目前，SMN1 错义突变的分子病理学尚未得到深入研究。因此，我们使用所有可能的搜索词，从三个数据库（PubMed、PMC 和 Google Scholar）中收集了 SMN1 蛋白中的所有错义突变。对所有错义突变进行了计算机致病性、保守性和稳定性分析工具的分析。我们使用统计分析作为验证这些工具特异性和准确性的 QC 措施。PolyPhen-2 表现出最高的特异性和准确性。而 PolyPhen-1 表现出最高的敏感性；总体而言，与其他计算机工具相比，PolyPhen2 表现出更好的指标。三个突变（D44V、Y272C 和 Y277C）被确定为最具致病性和最不稳定的突变。此外，我们比较了天然和突变氨基酸的物理化学性质，并观察到半胱氨酸向酪氨酸取代后氢键和芳环堆积的丧失，导致芳香环的丢失，并可能降低蛋白质稳定性。这三个突变进一步用 GROMACS 进行了分子动力学模拟（MDS）分析，以了解结构变化。与 D44V 突变体相比，Y272C 和 Y277C 突变体表现出与天然蛋白最大的偏差模式。进一步的 MDS 分析预测了稳定性的变化，这可能是由于在分子内氢键分析和物理化学分析中观察到的氢键丧失所导致的。与 D44V 突变相比，Y272C 和 Y277C 突变体的功能丧失/结构影响被发现更为严重。通过对计算机预测、物理化学分析和 MDS 的结果进行关联，我们能够观察到所有三个突变体的稳定性丧失。这种组合方法可以作为脊髓性肌萎缩症的变体解释和药物设计的平台，这些变体是由错义突变引起的。
