The Human Leukocyte Antigen G (HLA-G) protein is an immune tolerogenic molecule with 7 isoforms. The change of expression level and some polymorphisms of the HLA-G gene are involved in various pathologies. Therefore, this study aimed to predict the most deleterious missense non-synonymous single nucleotide polymorphisms (nsSNPs) in HLA-G isoforms via in silico analyses and to examine structural and functional effects of the predicted nsSNPs on HLA-G isoforms. Out of 301 reported SNPs in dbSNP, 35 missense SNPs in isoform 1, 35 missense SNPs in isoform 5, 8 missense SNPs in all membrane-bound HLA-G isoforms and 8 missense SNPs in all soluble HLA-G isoforms were predicted as deleterious by all eight servers (SIFT, PROVEAN, PolyPhen-2, I-Mutant 3.0, SNPs&GO, PhD-SNP, SNAP2, and MUpro). The Structural and functional effects of the predicted nsSNPs on HLA-G isoforms were determined by MutPred2 and HOPE servers, respectively. Consurf analyses showed that the majority of the predicted nsSNPs occur in conserved sites. I-TASSER and Chimera were used for modeling of the predicted nsSNPs. rs182801644 and rs771111444 were related to creating functional patterns in 5′UTR. 5 SNPs in 3′UTR of the HLA-G gene were predicted to affect the miRNA target sites. Kaplan-Meier analysis showed the HLA-G deregulation can serve as a prognostic marker for some cancers. The implementation of in silico SNP prioritization methods provides a great framework for the recognition of functional SNPs. The results obtained from the current study would be called laboratory investigations.

中文翻译：

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预测人类HLA-G基因蛋白质同工型最有害的错义nsSNP，并对其结构和功能后果进行计算机评估。

人类白细胞抗原G（HLA-G）蛋白是具有7个亚型的免疫耐受分子。HLA-G基因的表达水平的变化和某些多态性涉及多种病理。因此，本研究旨在通过计算机分析预测HLA-G同工型中最有害的错义非同义单核苷酸多态性（nsSNPs），并检查预测的nsSNP对HLA-G同工型的结构和功能作用。在dbSNP中报告的301个SNP中，同工型1中有35个错义SNP，同工型5中有35个错义SNP，所有膜结合HLA-G同工型中有8个错义SNP，所有可溶性HLA-G同工型中有8个错义SNP。所有八个服务器（SIFT，PROVEAN，PolyPhen-2，I-Mutant 3.0，SNPs＆GO，PhD-SNP，SNAP2和MUpro）。预测的nsSNP对HLA-G亚型的结构和功能影响分别由MutPred2和HOPE服务器确定。Consurf分析表明，大多数预测的nsSNPs均位于保守位点。I-TASSER和Chimera用于预测nsSNP的建模。rs182801644和rs771111444与在5'UTR中创建功能模式有关。预测HLA-G基因3'UTR中的5个SNP会影响miRNA靶位点。Kaplan-Meier分析显示HLA-G失调可以作为某些癌症的预后指标。计算机SNP优先级排序方法的实现为功能性SNP的识别提供了一个很好的框架。从当前研究获得的结果将被称为实验室研究。Consurf分析表明，大多数预测的nsSNPs均位于保守位点。I-TASSER和Chimera用于预测nsSNP的建模。rs182801644和rs771111444与在5'UTR中创建功能模式有关。预测HLA-G基因3'UTR中的5个SNP会影响miRNA靶位点。Kaplan-Meier分析表明，HLA-G失调可以作为某些癌症的预后指标。计算机SNP优先级排序方法的实现为功能性SNP的识别提供了一个很好的框架。从当前研究获得的结果将被称为实验室研究。Consurf分析表明，大多数预测的nsSNPs均位于保守位点。I-TASSER和Chimera用于预测nsSNP的建模。rs182801644和rs771111444与在5'UTR中创建功能模式有关。预测HLA-G基因3'UTR中的5个SNP会影响miRNA靶位点。Kaplan-Meier分析显示HLA-G失调可以作为某些癌症的预后指标。计算机SNP优先级排序方法的实现为功能性SNP的识别提供了一个很好的框架。从当前研究获得的结果将被称为实验室研究。rs182801644和rs771111444与在5'UTR中创建功能模式有关。预测HLA-G基因3'UTR中的5个SNP会影响miRNA靶位点。Kaplan-Meier分析显示HLA-G失调可以作为某些癌症的预后指标。计算机SNP优先级排序方法的实现为功能性SNP的识别提供了一个很好的框架。从当前研究获得的结果将被称为实验室研究。rs182801644和rs771111444与在5'UTR中创建功能模式有关。预测HLA-G基因3'UTR中的5个SNP会影响miRNA靶位点。Kaplan-Meier分析表明，HLA-G失调可以作为某些癌症的预后指标。计算机SNP优先级排序方法的实现为功能性SNP的识别提供了一个很好的框架。从当前研究获得的结果将被称为实验室研究。