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Type-2 Diabetes Mellitus and Glucagon-Like Peptide-1 Receptor toward Predicting Possible Association

Computational Molecular Bioscience 2023年第3期13卷 48-62页

作者： Nabaa Kamal Alshafei Intisar Hassan Saeed Mona Abdelrahman Mohamed Khaier Department of Biochemistry College of Veterinary Medicine University of Bahri Khartoum Sudan Department of Physiology College of Veterinary Medicine University of Bahri Khartoum Sudan Department of Molecular Biology and Bioinformatics College of Veterinary Medicine University of Bahri Khartoum Sudan

Aim: This study aimed to investigate the effect of non-synonymous snps (nssnps) of the Glucagon-like peptide-1 Receptor (GLP-1R) gene in protein function and structure using different computational software. Introduction: The GLP1R gene provides the necessary instruction for the synthesis of the insulin hormones which is needed for glucose catabolism. Polymorphisms in this gene are associated with diabetes. The protein is an important drug target for the treatment of type-2 diabetes and stroke. Material and Methods: Different nssnps and protein-related sequences were obtained from NCBI and ExPASY database. Gene associations and interactions were predicted using GeneMANIA software. Deleterious and damaging effects of nssnps were analyzed using SIFT, Provean, and Polyphen-2. The association of the nssnps with the disease was predicted using snps & GO software. Protein stability was investigated using I-Mutant and MUpro software. The structural and functional impact of point mutations was predicted using Project Hope software. Project Hope analyzes the mutations according to their size, charge, hydrophobicity, and conservancy. Results: The GLP1R gene was found to have an association with 20 other different genes. Among the most important ones is the GCG (glucagon) gene which is also a trans membrane protein. Overall 7229 variants were seen, and the missense variants or nssnps (146) were selected for further analysis. The total number of nssnps obtained in this study was 146. After being subjected to SIFT software (27 Deleterious and 119 Tolerated) were predicted. Analysis with Provean showed that (20 deleterious and 7 neutral). Analysis using Polyphen-2 revealed 17 probably damaging, 2 possibly damaging and 1 benign nssnps. Using two additional software snps & GO and PHD-snps showed that 14 and 17 nssnps had a disease effect, respectively. Project Hope software predicts the effect of the 14 nssnps on the protein function due to differences in charge, size, hydrophobici

关键词： Glucagon-Like Peptide-1 Receptor Single Nucleotide Polymorphism Insilico Analysis non synonymous snp SIFT Polyphen-2 GeneMANIA

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