In Silico approach for identification, prediction of AMPD1 gene nsSNPs associated with Myoadenylate Deaminase deficiency
Mohamed, A., Hamza, A., Fadl, H., Albkrye, A., Ahmed, H., Abubaker, H., Elbager, S. (2023). In Silico approach for identification, prediction of AMPD1 gene nsSNPs associated with Myoadenylate Deaminase deficiency. EKB Journal Management System, 9(1), 17-32. doi: 10.21608/jbaar.2023.284727
Abrar Mohamed Gamar Mohamed; Abdelrahman Hamza Abdelmoneim Hamza; Hiba Awadelkareem Osman Fadl; Afra Mohamed Suliman Albkrye; Hadeel Abdelsamea Mohamed Ahmed; Hazem Abdo Mohamed Abubaker; Sahar Gamal Elbager. "In Silico approach for identification, prediction of AMPD1 gene nsSNPs associated with Myoadenylate Deaminase deficiency". EKB Journal Management System, 9, 1, 2023, 17-32. doi: 10.21608/jbaar.2023.284727
Mohamed, A., Hamza, A., Fadl, H., Albkrye, A., Ahmed, H., Abubaker, H., Elbager, S. (2023). 'In Silico approach for identification, prediction of AMPD1 gene nsSNPs associated with Myoadenylate Deaminase deficiency', EKB Journal Management System, 9(1), pp. 17-32. doi: 10.21608/jbaar.2023.284727
Mohamed, A., Hamza, A., Fadl, H., Albkrye, A., Ahmed, H., Abubaker, H., Elbager, S. In Silico approach for identification, prediction of AMPD1 gene nsSNPs associated with Myoadenylate Deaminase deficiency. EKB Journal Management System, 2023; 9(1): 17-32. doi: 10.21608/jbaar.2023.284727

In Silico approach for identification, prediction of AMPD1 gene nsSNPs associated with Myoadenylate Deaminase deficiency

Journal of Bioscience and Applied Research
Volume 9, Issue 1, March 2023, Page 17-32  XML PDF (1.03 MB)
Document Type: Original Article
DOI: 10.21608/jbaar.2023.284727
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Authors
Abrar Mohamed Gamar Mohamed email 1; Abdelrahman Hamza Abdelmoneim Hamza2; Hiba Awadelkareem Osman Fadlorcid 3; Afra Mohamed Suliman Albkrye4; Hadeel Abdelsamea Mohamed Ahmed5; Hazem Abdo Mohamed Abubaker6; Sahar Gamal Elbager7
1Faculty of Medicine, Al-Zaiem Al-Azhari University, Khartoum, Sudan/Clinical Immunology Resident, Sudan Medical Specialization Board, Khartoum, Sudan
2Clinical Immunology Resident, Sudan Medical Specialization Board, Khartoum, Sudan/
3Hematology Department, Faculty of Medical Laboratory Sciences,Al-Neelain University and Head of Medical Laboratory Department,Sudanese Medical Research Association(SMRA),Khartoum-Sudan.
4Department of Molecular Biology and Bioinformatics, Faculty of Veterinary Medicine, Bahri University, Khartoum, Sudan
5Department of Biotechnology, Faculty of Science and Technology, Omdurman Islamic University, Khartoum, Sudan
6Department of Clinical Medicine, Faculty of Veterinary Medicine, University of Khartoum, Khartoum, Sudan
7Department of Haematology, Faculty of Medical Laboratory Sciences, University of Medical Sciences and Technology, Khartoum, Sudan/Department of Pathology, South Egypt Cancer Institute, Assiut University, Assiut, Egypt
Abstract
Background: Myoadenylate deaminase deficiency is an autosomal recessive metabolic myopathy caused by mutations in the Adenosine monophosphate deaminase 1 gene. Adenosine monophosphate deaminase 1 gene deficiency is one of the most common causes of exercise-induced myopathy. In this study, non-synonymous single nucleotide polymorphism was analyzed for its functional and structural impact which is deleterious to Adenosine monophosphate deaminase 1 protein. Methods: The data on human Adenosine monophosphate deaminase 1gene was retrieved from the NCBI database on 9 JUNE 2021 and then analyzed using different bioinformatics prediction algorithms, namely: SIFT, PolyPhen-2, PROVEAN, SNAP2, PANTHER, SNPs and GO, PMut, and I-Mutant to detect the deleterious nsSNPs and its association with diseases. In addition, a Consurf web server was used to detect the functional SNPs in the conserved region. Chimera, Project Hope, and MutPred2 software were used to visualize and analyze the effect of nsSNPs on the functions and structure of the AMPD1 protein. Finally, both the STRING database and KEGG were used for the prediction of protein-protein interaction.  Results: A total of 6178 SNPs were reported in the human AMPD1 gene. In this study 583 missense nsSNPs were selected for investigation and only 72 nsSNPs were shortlisted and computationally evaluated for their impact on AMPD1 protein. From all servers that were used collectively (K320I, R421W, R458C, R458H, R51C, R757L, R761H, and G246S) nsSNPs were predicted as deleterious, associated with disease, highly conserved, and decrease effective stability of AMPD1 protein. In addition, the AMPD1 protein was predicted to have strong interactions with ten proteins involved in various ranges of biological processes.
Conclusion: The present study undertakes a systematic bioinformatics approach to identify functionally important nsSNPs in the human AMPD1 gene to understand how these mutations affect the protein function and structure and hence promote a myoadenylate deaminase deficiency.
Keywords
AMPD1 Gene; Myoadenylate Deaminase Deficiency; Myopathy; Bioinformatics; nsSNPs; & In Silico
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