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Ayodele AO, Udosen B, Oluwagbemi OO, Oladipo EK, Omotuyi I, Isewon I, Nash O, Soremekun O, Fatumo S. An in-silico analysis of OGT gene association with diabetes mellitus. BMC Res Notes 2024; 17:89. [PMID: 38539217 PMCID: PMC10976716 DOI: 10.1186/s13104-024-06744-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 03/08/2024] [Indexed: 04/01/2024] Open
Abstract
O-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two primary proteins: the O-linked N-acetylglucosamine transferase (OGT) catalysing the addition, and the glycoside hydrolase OGA (O-GlcNAcase) catalysing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins. This process is necessary for various critical cellular functions. The O-linked N-acetylglucosamine transferase (OGT) gene produces the OGT protein. Several studies have shown the overexpression of this protein to have biological implications in metabolic diseases like cancer and diabetes mellitus (DM). This study retrieved 159 SNPs with clinical significance from the SNPs database. We probed the functional effects, stability profile, and evolutionary conservation of these to determine their fit for this research. We then identified 7 SNPs (G103R, N196K, Y228H, R250C, G341V, L367F, and C845S) with predicted deleterious effects across the four tools used (PhD-SNPs, SNPs&Go, PROVEAN, and PolyPhen2). Proceeding with this, we used ROBETTA, a homology modelling tool, to model the proteins with these point mutations and carried out a structural bioinformatics method- molecular docking- using the Glide model of the Schrodinger Maestro suite. We used a previously reported inhibitor of OGT, OSMI-1, as the ligand for these mutated protein models. As a result, very good binding affinities and interactions were observed between this ligand and the active site residues within 4Å of OGT. We conclude that these mutation points may be used for further downstream analysis as drug targets for treating diabetes mellitus.
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Affiliation(s)
- Abigail O Ayodele
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Brenda Udosen
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
| | - Olugbenga O Oluwagbemi
- Department of Computer Science and Information Technology, Faculty of Natural and Applied Sciences, Sol Plaatje University, 8301, Kimberley, South Africa
- Department of Mathematical Sciences, Stellenbosch University, 7602, Stellenbosch, South Africa
| | - Elijah K Oladipo
- Laboratory of Molecular Biology, Immunology and Bioinformatics, Department of Microbiology, Adeleke University, 232104, Ede, Nigeria
- Genomics Unit, Helix Biogen Institute, 210214, Ogbomoso, Nigeria
| | - Idowu Omotuyi
- Institute for Drug Research and Development, S.E. Bogoro Center, Afe Babalola University, Ado Ekiti, Nigeria
- Molecular Biology and Molecular Simulation Center (Mols&Sims), Ado Ekiti, Nigeria
| | - Itunuoluwa Isewon
- Computer and Information Sciences Department, Covenant University, Ota, Ogun State, Nigeria
| | - Oyekanmi Nash
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- MRC/UVRI and London School of Hygiene and Tropical Medicine London (LSHTM) Uganda Research Unit, Entebbe, Uganda
| | - Segun Fatumo
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda.
- MRC/UVRI and London School of Hygiene and Tropical Medicine London (LSHTM) Uganda Research Unit, Entebbe, Uganda.
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Mayanja R, Kintu C, Diabate O, Soremekun O, Oluwagbemi OO, Wele M, Kalyesubula R, Jjingo D, Chikowore T, Fatumo S. Molecular Dynamic Simulation Reveals Structure Differences in APOL1 Variants and Implication in Pathogenesis of Chronic Kidney Disease. Genes (Basel) 2022; 13:1460. [PMID: 36011371 PMCID: PMC9408642 DOI: 10.3390/genes13081460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/28/2022] [Accepted: 08/08/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND According to observational studies, two polymorphisms in the apolipoprotein L1 (APOL1) gene have been linked to an increased risk of chronic kidney disease (CKD) in Africans. One polymorphism involves the substitution of two amino-acid residues (S342G and I384M; known as G1), while the other involves the deletion of two amino-acid residues in a row (N388 and Y389; termed G2). Despite the strong link between APOL1 polymorphisms and kidney disease, the molecular mechanisms via which these APOL1 mutations influence the onset and progression of CKD remain unknown. METHODS To predict the active site and allosteric site on the APOL1 protein, we used the Computed Atlas of Surface Topography of Proteins (CASTp) and the Protein Allosteric Sites Server (PASSer). Using an extended molecular dynamics simulation, we investigated the characteristic structural perturbations in the 3D structures of APOL1 variants. RESULTS According to CASTp's active site characterization, the topmost predicted site had a surface area of 964.892 Å2 and a pocket volume of 900.792 Å3. For the top three allosteric pockets, the allostery probability was 52.44%, 46.30%, and 38.50%, respectively. The systems reached equilibrium in about 125 ns. From 0-100 ns, there was also significant structural instability. When compared to G1 and G2, the wildtype protein (G0) had overall high stability throughout the simulation. The root-mean-square fluctuation (RMSF) of wildtype and variant protein backbone Cα fluctuations revealed that the Cα of the variants had a large structural fluctuation when compared to the wildtype. CONCLUSION Using a combination of different computational techniques, we identified binding sites within the APOL1 protein that could be an attractive site for potential inhibitors of APOL1. Furthermore, the G1 and G2 mutations reduced the structural stability of APOL1.
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Affiliation(s)
- Richard Mayanja
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe 31405, Uganda
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala 10101, Uganda
| | - Christopher Kintu
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe 31405, Uganda
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala 10101, Uganda
| | - Oudou Diabate
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe 31405, Uganda
- African Center of Excellence in Bioinformatics (ACE-B), University of Science, Technique and Technologies of Bamako (USTTB), Bamako 3206, Mali
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe 31405, Uganda
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Durban 4041, South Africa
| | | | - Mamadou Wele
- African Center of Excellence in Bioinformatics (ACE-B), University of Science, Technique and Technologies of Bamako (USTTB), Bamako 3206, Mali
| | - Robert Kalyesubula
- Department of Internal Medicine and Department of Physiology, Makerere University, Kampala 10101, Uganda
| | - Daudi Jjingo
- African Center of Excellence in Bioinformatics (ACE-B), Makerere University, Kampala 10101, Uganda
| | - Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2050, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe 31405, Uganda
- Department of Non-Communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
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