Jamali Zeinab, Zargar Mahsa, Modarressi Mohammad Hossein
Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Mol Biol Res Commun. 2025;14(1):47-58. doi: 10.22099/mbrc.2024.49991.1977.
Testis specific gene antigen 10 (TSGA10) is a protein which has roles in spermatogenesis and cancers so that deletion or mutation in the gene resulted in non-obstructive infertility and aberrant expression of this protein, was detected in solid tumors and leukemia. Despite the crucial roles of TSGA10 in tumorigenesis and infertility, yet it is not obvious how various nsSNPs of its gene impress the structure and function of the TSGA10. Therefore, it is worthwhile to investigate the potential highly deleterious nsSNPs by several in-silico tools before launching costly experimental approaches. In the current study, we employed several different machine learning algorithms in a two-step screening procedure to analyze single nucleotide substitutions of gene. Prediction tools were included SIFT, PROVEAN, PolyPhen-2, SNAP2, SNPs & GO, PhD-SNP for the first step and the second step included predictive tools such as I-mutant 3.0, MUpro, SNPeffect 4.0 (LIMBO, WALTZ, TANGO, FoldX), MutationTaster and CADD. Also, the 3D models of significantly damaging variants were built by Phyre2. The results elucidated 15 amino acid alterations as the most deleterious ones. Among these S563P, E578K, Q580P, R638L, R638C, R638G, R638S, L648R, R649C, R649H were located in a domain which is approved to has interaction with the HIF1-A protein and D62Y, R105G, D106V and D111Y were located on phosphodiesterase domain. In sum, these predicted mutations significantly influence the function of TSGA10 and they could be used for precise study of this protein in infertility and cancer experimental investigations.
睾丸特异性基因抗原10（TSGA10）是一种在精子发生和癌症中发挥作用的蛋白质，该基因的缺失或突变会导致非梗阻性不育，并且在实体瘤和白血病中检测到该蛋白的异常表达。尽管TSGA10在肿瘤发生和不育中起着关键作用，但其基因的各种非同义单核苷酸多态性（nsSNPs）如何影响TSGA10的结构和功能尚不清楚。因此，在开展代价高昂的实验方法之前，利用多种计算机模拟工具研究潜在的高度有害nsSNPs是值得的。在本研究中，我们采用了几种不同的机器学习算法，通过两步筛选程序来分析该基因的单核苷酸替换。第一步的预测工具包括SIFT、PROVEAN、PolyPhen-2、SNAP2、SNPs & GO、PhD-SNP，第二步包括预测工具，如I-mutant 3.0、MUpro、SNPeffect 4.0（LIMBO、WALTZ、TANGO、FoldX）、MutationTaster和CADD。此外，通过Phyre2构建了显著有害变体的三维模型。结果阐明了15个氨基酸改变是最有害的。其中，S563P、E578K、Q580P、R638L、R638C、R638G、R638S、L648R、R649C、R649H位于一个已被证实与HIF1-A蛋白相互作用的结构域中，而D62Y、R105G、D106V和D111Y位于磷酸二酯酶结构域上。总之，这些预测的突变显著影响TSGA10的功能，它们可用于在不育和癌症实验研究中对该蛋白进行精确研究。
