An In-silico Analysis of OGT gene association with diabetes mellitus

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Ayodele, Brenda Udosen, Olugbenga O. Oluwagbemi, Elijah K. Oladipo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3068800/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2024 Read the published version in BMC Research Notes → Version 1 posted 4 You are reading this latest preprint version Abstract O-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two main proteins; the O-linked N-acetylglucosamine transferase (OGT) catalyzing the addition, and the glycoside hydrolase OGA (O-GlcNAcase) catalyzing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins. This process is necessary for various important cellular functions. The O-linked N-acetylglucosamine transferase (OGT) gene is responsible for the production of 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). In this study, we retrieved a total of 159 SNPs with clinical significance from the SNPs database and 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 modeling 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, and 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 the treatment of diabetes mellitus. Single nucleotide polymorphism (SNPs) O-linked N-Acetylglucosamine transferase (OGT) 3 and 4 Figures Figure 1 Figure 2 1. Introduction The human O-linked N-acetylglucosamine transferase (OGT) gene is ~ 43 kb long. Located at the Xq13.1 genomic locus, it is alternatively spliced to generate nucleocytoplasmic (nc), mitochondrial (m), and short (s) isoforms. These isoforms are distinguished by the varying number of tetratricopeptide repeats (TPRs) in their N-terminal domains. The full-length human nucleocytoplasmic OGT isoform (~ 110 kDa) contains 13 TPRs, while mitochondrial OGT (~ 103 kDa) and short OGT (~ 75 kDa) contain 9 and 3 TPRs, respectively [ 1 , 2 , 3 ]. The OGT gene encodes the OGT protein. Protein O-GlcNAc transferase (OGT) is responsible for the addition of the GlcNAc moiety to the threonine and serine residues of cytoplasmic and nuclear proteins. Because it is involved in cell signaling, glucose homeostasis in the liver, and the regulation of the clock genes' circadian oscillation, its absence has been found to be lethal in mice [ 4 , 5 ]. Torres and Hart discovered it about 30 years ago [ 6 ], and it has been found to be linked to x-linked intellectual disability and insulin resistance in muscle and adipocyte cells when mutated [ 7 , 8 ]. Its contribution to glucose metabolism via the Hexosamine Biosynthesis Pathway directly links it to diabetes mellitus [ 9 , 10 ]. Diabetes mellitus (DM) is a metabolic disorder that comes in two forms: T1DM and T2DM. T1DM is caused by the defective secretion of insulin, while T2DM is caused by a defect in insulin action [ 11 ]. Diabetes is caused by a variety of factors, including but not limited to lifestyle, genetics, and diet. Diabetes is estimated to kill 6.7 million people worldwide in 2021, with 537 million adults living with the disease, a figure that is expected to rise to 783 million by 2045 [ 12 ]. Non-synonymous single nucleotide polymorphisms (nsSNPs) are protein amino acid substitutions [ 13 ]. As a result, the goal of this study is to identify disease-causing and deleterious SNPs within the OGT gene, as well as druggable targets, in order to discover therapeutic drugs for diabetes mellitus via this gene. 2. Materials and methods 2.1. Data retrieval for single nucleotide polymorphisms The OGT variants and SNPs were retrieved from the National Centre for Biotechnology Information's (NCBI) dbSNPs server [ 14 ]. The SNPs were chosen based on their clinical significance as reported by ClinVar [ 15 ]. 2.2 Investigating the functional effects of coding nsSNPs The deleterious potential of the OGT nsSNPs was assessed using four major tools: Predictor of Human Deleterious Single Nucleotide Polymorphism (PhD-SNP) [ 13 ], SNPs&Go [ 16 ], PROVEAN v1.1[ 17 ], and Polymorphism Phenotyping v2 (Polyphen2) [ 18 ]. SNPs&GO is an algorithm that predicts deleterious nsSNPs based on protein functional annotation. PHD-SNP is an online tool for predicting point mutations in protein sequences and determining the impact of these mutations [ 19 ], the program predicts how the single point amino acid change will cause disease. PROVEAN predicts changes in a protein's biological functions caused by single amino acid substitutions and a score of less than − 2.5 is predicted to be harmful. 2.3 Analysis of protein stability of predicted OGT nsSNPs The i-Stable 2.0 server, which includes tools such as iPTREE-STAB, I-Mutant 2.0, and MUpro, was used to predict the structure-function relationship of the SNPs [ 20 ]. The i-Mutant tool calculates the Gibbs free energy for the wild type protein and subtracts it from the mutant form to estimate the free energy changes. The predicted values of all OGT mutant types may alter protein stability with associated free energy. Positive DDG values indicate that the mutated proteins are highly stable, whereas negative scores indicate that they are less stable [ 21 ]. 2.4 Analysis of the evolutionary conservation of amino acids The Consurf program is used to investigate the evolutionary conservation of OGT amino acids. It uses a Bayesian method to determine the conserved amino acids for the identification of the structural and functional residues in the conserved regions [ 22 ]. Prediction of the amino acids are into variable (range between 1 and 4), intermediate (range between 5 and 6), and conserved (range between 7 and 9) based on their scores and color indications [ 23 ]. 2.5 Protein modelling and molecular docking Using the protein sequence retrieved from the UniProt database, we used the ROBETTA homology modelling tool to predict the 3D structure of the OGT apo-protein [ 24 ]. The predicted structure was viewed using the Schrodinger Maestro v11.1 workspace and validated using the Verify-3D and ERRAT programs available in the SAVES server [ 25 ]. Schrodinger-Maestro v11.1's Protein Preparation Wizard module was used to preprocess, optimize, and minimize the crystal structure of OGT. While keeping the pH at 7, structural water molecules were kept to ensure protein stability, while redundant water molecules were removed to facilitate protein-ligand binding. Hydrogens were also added to fill in the gaps and to mediate hydrogen bridges and electrostatic forces [ 26 ]. We used the SiteMap feature of the Schrodinger Maestro software to identify potential binding pockets on the OGT protein [ 27 ]. The generation of receptor grids was expedient in order to limit ligand docking to only the identified binding pockets [ 28 ]. The grid box had dimensions of x = -32.724, y = 51.454, and z = 83.332. The PubChem database was used to retrieve the 2D structure of OSMI-1, a small molecule inhibitor of OGT [ 29 ]. The OSMI-1 was prepared and converted to its 3D geometry prior to molecular docking using the LigPrep module of Maestro v.11.1 [ 30 ]. 3. Results 3.1 nsSNPs obtained from the dbSNPs database The discovery of disease-causing nsSNPs are useful in the development of candidate drug therapy because they are biological markers involved in disease occurrence or progression [ 31 , 32 ]. The NCBI server yielded 159 nsSNPs [ 33 ] (Supplementary table 1). According to ClinVar, the retrieval favored only SNPs with clinical significance [ 15 ]. 3.2 Identification of damaging nsSNPs in OGT We used four (4) tools to predict the potential deleteriousness of 25 nsSNPs, with at least three (3) of the four (4) tools predicting a negative effect (Table a). PROVEAN predicted seven (7) nsSNPs to be harmful, and using the PolyPhen-2 tool, all seven (7) nsSNPs were predicted to be probably harmful, with scores ranging from 0.932 to 1.000. SNPs&GO and PhD-SNP both predicted diseased SNPs. The total number of deleterious SNPs was reduced to 7 based on their detrimental effect across all four tools used (Table b). Table a: Damaging nsSNPs from OGT S/N rs ID AA Change/position PROVEAN PhD-SNPs SNPs&GO PolyPhen2 1. rs766646613 R627C − 4.677 Deleterious Disease RI-2 Neutral 0.999 probably damaging 2. rs131705060 R117C -4.194 Deleterious Disease RI-1 Disease RI-1 0.932 probably damaging 3. rs204042438 P879L -9.041 Deleterious Disease RI-6 Neutral 0.942 probably damaging 4. rs204039392 P685Q -7.872 Deleterious Neutral Disease RI-4 0.994 probably damaging 5. rs204042400 R867C -5.399 Deleterious Disease RI- 1 Neutral 0.994 probably damaging 6. rs204034593 A380V -3.790 Deleterious Neutral Disease RI-4 0.938 probably damaging 7. rs766646613 R627C − 4.677 Deleterious Disease RI-2 Neutral 0.999 probably damaging 8. rs2040347448 M401T -4.727 Deleterious Disease RI-2 Neutral 0.998 probably damaging 9. rs2040347668 C417Y -8.596 Deleterious Neutral Disease RI-1 0.989 probably damaging 10. rs2040350890 D481G -4.340 Deleterious Disease 1 Disease RI-2 benign 11. rs2040368778 H611N -5.952 Deleterious Disease 2 Neutral 0.55 probably damaging 12. rs2040387073 P657L -9.335 Deleterious Neutral Disease RI-6 0.924 probably damaging 13. rs2040191136 Y112S -7.489 Deleterious Neutral Disease RI-7 0.973 probably damaging 14. rs2040329106 Y228H -2.680 Deleterious Disease 5 Disease RI-0 0.997 probably damaging 15. rs2040334939 R250C -6.093 Deleterious Disease 3 Disease RI-3 1.000 probably damaging 16. rs2040341169 G341V -7.294 Deleterious Disease RI-3 Disease RI-3 0.991 probably damaging 17. rs2040345810 L367F -3.717 Deleterious Disease 1 Disease RI-1 0.999 probably damaging 18. rs2040190682 R102G -4.116 Deleterious Neutral Disease RI-1 0.930 probably damaging 19. rs2040334968 R250L -5.405 Deleterious Disease 2 Disease RI-0 1.000 probably damaging 20. rs772525369 R899C -6.682 Deleterious Disease RI-0 Disease RI-2 1.000 probably damaging 21. rs1114167891 R284P -4.060 Deleterious Disease 6 Disease RI-6 0.951 probably damaging 22. rs1556046834 G103R -5.717 Deleterious Disease 5 Disease RI-6 0.993 probably damaging 23. rs1602152230 N648Y -7.605 Deleterious Disease 6 Disease RI-0 0.998 probably damaging 24. rs2040405196 C845S -7.654 Deleterious Disease 3 Disease RI-3 0.930 probably damaging 25. rs200109331 N196K -4.599 Deleterious Disease 5 Disease RI-5 1.000 probably damaging Table b: Predicted deleterious nsSNPs across the four tools S/N rs ID AA Change/Position PROVEAN PhD-SNPs SNPs&GO PolyPhen2 1. rs2040329106 Y228H -2.680 Deleterious Disease 5 Disease RI-0 0.997 PROBABLY DAMAGING 2. rs2040334939 R250C -6.093 Deleterious Disease 3 Disease RI-3 1.000 PROBABLY DAMAGING 3. rs2040341169 G341V -7.294 Deleterious Disease RI-3 Disease RI-3 0.991 PROBABLY DAMAGING 4. rs2040345810 L367F -3.717 Deleterious Disease 1 Disease RI-1 0.999 PROBABLY DAMAGING 5. rs2040405196 C845S -7.654 Deleterious Disease 3 Disease RI-3 0.930 probably damaging 6. rs1556046834 G103R -5.717 Deleterious Disease 5 Disease RI-6 0.993 PROBABLY DAMAGING 7. rs200109331 N196K -4.599 Deleterious Disease 5 Disease RI-5 1.000 PROBABLY DAMAGING 3.3 Protein stability profile prediction for nsSNPs in OGT The iStable 2.0 tool was used to predict protein stability [ 34 ]. All seven predicted highly deleterious SNPs were also predicted to reduce OGT protein stability. The results of MUpro SVM, MUpro MM, I-Mutant 2.0, and iPTREE-STAB are shown in Table c. Table c: nsSNPs stability profiling S/N SNPs AA Change I-Mutant2.0 SEQ MUpro_SVM MUpro_NN iPTREE-STAB 1. rs2040329106 Y228H Decrease Decrease Decrease Decrease 2. rs2040334939 R250C Decrease Increase Increase Decrease 3. rs2040341169 G341V Decrease Decrease Increase Decrease 4. rs2040345810 L367F Decrease Decrease Decrease Decrease 5. rs2040405196 C845S Decrease Decrease Decrease Decrease 6. rs1556046834 G103R Decrease Increase Increase Decrease 7. rs200109331 N196K Decrease Decrease Decrease Decrease 3.4 Conservation prediction of damaging nsSNPs in OGT Consurf predicted that Y228H, C845S, and L367F would be buried and conserved, whereas G103R, N196K, R250C, and G341V would be exposed and conserved (Table d). Table d: ConSurf result output S/N Amino acid change Pos Seq Score Color Confidence interval Confidence interval colors B/e Function Msa data Residue variety 1. G103R 103 G -0.148 6 -0.417, 0.041 7,5 e 122/150 G,N,A,V 2. N196K 196 N -0.805 9 -0.861,-0.777 9,9 e f 127/150 N,Y,S 3. Y228H 228 Y -0.218 6 -0.417,-0.080 7,5 b 126/150 Y,L,H,F 4. R250C 250 R -0.104 6 -0.350, 0.041 7,5 e 143/150 K,R,E,S,T,N,H,Q,A 5. C845S 845 C -0.077 9 -0.272, 0.041 9,9 b s 147/150 Q,H,Y,T,E,S,K,R 6. G341V 341 G -0.703 9 -0.799,-0.660 9,8 e f 147/150 S,G,C,N 7. L367F 367 L -0.78 9 -0.849,-0.752 9,9 b s 146/150 I,Y,L 3.5 OGT structural characterization of wild and mutant types in comparison ERRAT and Verify-3D were used to validate the protein structure (Fig. 1 ). According to the Verify-3D results, 94.39% of the residues have an average 3D-ID score of 0.2. (Fig. 2 a). The Ramachandran plot, which is available in PROCHECK, was used to assess the quality of the 3D protein structure (Fig. 2 b). According to the plot, 91.3%, 8.0%, 0.3%, and 0.3% of the residues are in the favored, allowed, generously allowed, and disallowed regions, respectively (Fig. 2 c). This confirms the protein structure's high quality. ERRAT also demonstrated an overall quality factor of 98.7161 (Fig. 2 d), implying that the results obtained from the aforementioned tools indicated that our modeled protein is of high quality and can be used for further investigation. 3.6 OGT Mutant type as a potential drug target The Glide module of the Schrödinger Maestro Suite was used to investigate the protein-ligand binding affinity of OSMI-1 and the OGT protein. OSMI-1 interacted well with the active site residues of OGT (supplementary Fig. 1), and the docking scores for each interaction are shown in table e. These predictions can be validated using additional downstream analysis. Table e: Molecular docking results of mutant type OGT against OSMI-1 S/N AMINO ACID CHANGE DOCKING SCORES INTERACTING RESIDUES 1. G103R -4.646 GLU649, LYS534, ARG338, and ASN621 2. N196K -5.183 LYS644, GLY645, and ASN648 3. Y228H -5.069 LYS534, ASN621, ALA646, and TYR642 4. R250C -4.775 HIS508, LYS852, THR932, PHE878, LYS908, HIS568, and LYS644 5. C845S -4.571 GLU649 6. G341V -5.145 ASN567, SER594, and LYS644 7. L367F -5.563 GLU649, ALA646, TYR642, and LYS534 4. Discussion Based on similarity and homology data, Consurf calculates the evolutionary profile of proteins and the effects of amino acid substitutions [ 35 ]. Because the nsSNPs were found in a conserved region, a change in the amino acid sequence in those regions will affect the structural and functional profile of the OGT protein. Only the mutation points in G103R, Y228H, R250C, C845S, G341V, N196K, and L367F were found to be harmful across all four tools used, out of the 25 deleterious nsSNPs identified. They also significantly reduced the OGT protein's stability, as predicted by the i-Stable 2.0 web tool. The current study's strength lies in the use of various algorithms to obtain precise prediction results for the identified nsSNPs, which could be used as druggable reference points for the discovery of drugs to treat diabetes mellitus. 5. Conclusions The OGT protein has been linked to the progression of diabetes mellitus because it catalyzes the addition of the o-GlcNAc sugar moiety on nucleocytoplasmic proteins, a substrate of the hexosamine biosynthesis pathway, increasing the amount of intracellular glucose content. In this study, 159 OGT nsSNPs in coding regions were chosen, and structural analysis of the chosen 7 nsSNPs predicted a negative impact on protein function and stability. Overall, the findings indicated that nsSNPs could be used in drug development for diabetes mellitus. 6. Declarations (i) Authors’ contributions Segun Fatumo, Oyekanmi Nash, and Opeyemi Soremekun conceptualized the study and supervised the project. Abigail O. Ayodele, Brenda Udosen, and Opeyemi Soremekun led the main analyses. Abigail O. Ayodele wrote the first draft of the manuscript. All authors reviewed the first draft and provided critical feedback. All authors read and approved the final manuscript. (ii) Funding This work didn’t receive any funding. (iii) Competing interests The authors declare no conflict of interest. (iv) Availability of data and materials 1. PolyPhen2; http://genetics.bwh.harvard.edu/pph2/ 2. SNPs&Go; https://snps-and-go.biocomp.unibo.it/snps-and-go/ 3. PhD-SNP; https://snps.biofold.org/phd-snp/phd-snp.html 4. PROVEAN; https://bio.tools/provean 5. SNPs database; https://www.ncbi.nlm.nih.gov/snp/ 6. Consurf; https://consurf.tau.ac.il/consurf_index.php 7. ROBETTA; https://robetta.bakerlab.org/ 8. ClinVar; https://www.ncbi.nlm.nih.gov/clinvar/ 9. ERRAT; https://www.doe-mbi.ucla.edu/errat/ 10. Verify3D; https://www.doe-mbi.ucla.edu/verify3d/ 11. SAVES; https://saves.mbi.ucla.edu/ (v) Acknowledgements The National Institutes of Health Common Fund to the H3ABioNet Project grant number (5U24HG006941-09). Segun Fatumo is an international intermediate fellow funded by the Wellcome Trust grant (220740/Z/20/Z) at the MRC/UVRI and LSHTM. (vi) Ethics approval and consent to participate All authors approved and consented to participate the final manuscript. (vii) Consent for publication Not Applicable. References Hanover, J. A., Yu, S., Lubas, W. B., Shin, S.-H., Ragano-Caracciola, M., Kochran, J., & Love, D. C. (2003). Mitochondrial and nucleocytoplasmic isoforms of O-linked GlcNAc transferase encoded by a single mammalian gene. Archives of Biochemistry and Biophysics, 409(2), 287–297. Love, D. C., Kochran, J., Cathey, R. L., Shin, S.-H., & Hanover, J. A. (2003). Mitochondrial and nucleocytoplasmic targeting of O-linked GlcNAc transferase. Journal of Cell Science, 116(4), 647–654. Kanwal et al. (2015, December 2). 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Udosen, B., Soremekun, O., Ekenna, C., Idowu Omotuyi, O., Chikowore, T., Nashiru, O., & Fatumo, S. (2021). In-silico analysis reveals druggable single nucleotide polymorphisms in angiotensin 1 converting enzyme involved in the onset of blood pressure. BMC Research Notes, 14(1), 457. https://doi.org/10.1186/s13104-021-05879-z Aloyuni, S. A. (2021). In silico prediction of deleterious single nucleotide polymorphism in human AKR1C3 gene and identification of potent inhibitors using molecular docking approach. Journal of King Saud University - Science, 33(6), 101514. doi:10.1016/j.jksus.2021.101514 Supplementary information Supplementary Figure and Supplementary Table are not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2024 Read the published version in BMC Research Notes → Version 1 posted Editorial decision: Major revision 05 Jul, 2023 Editor assigned by journal 05 Jul, 2023 Submission checks completed at journal 01 Jul, 2023 First submitted to journal 15 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3068800","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":214803288,"identity":"dc95993f-77d9-432c-a7cd-c632ad4690a3","order_by":0,"name":"Abigail O. Ayodele","email":"","orcid":"","institution":"NABDA/FMST","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"O.","lastName":"Ayodele","suffix":""},{"id":214803289,"identity":"5422908f-7baa-492e-bc5e-7279419d903b","order_by":1,"name":"Brenda Udosen","email":"","orcid":"","institution":"University of Leicester","correspondingAuthor":false,"prefix":"","firstName":"Brenda","middleName":"","lastName":"Udosen","suffix":""},{"id":214803290,"identity":"48751dd7-7617-4758-93de-ef8bd0d3a59b","order_by":2,"name":"Olugbenga O. Oluwagbemi","email":"","orcid":"","institution":"Sol Plaatje University","correspondingAuthor":false,"prefix":"","firstName":"Olugbenga","middleName":"O.","lastName":"Oluwagbemi","suffix":""},{"id":214803291,"identity":"b092185e-a851-4ec5-af8d-2fb762e3c650","order_by":3,"name":"Elijah K. Oladipo","email":"","orcid":"","institution":"Adeleke University","correspondingAuthor":false,"prefix":"","firstName":"Elijah","middleName":"K.","lastName":"Oladipo","suffix":""},{"id":214803292,"identity":"94696ebc-acbf-4363-9da7-77106c53b30f","order_by":4,"name":"Idowu Omotuyi","email":"","orcid":"","institution":"Afe Babalola University","correspondingAuthor":false,"prefix":"","firstName":"Idowu","middleName":"","lastName":"Omotuyi","suffix":""},{"id":214803293,"identity":"785348e4-eb44-4a5b-b769-8e4d1c4601f5","order_by":5,"name":"Itunuoluwa Isewon","email":"","orcid":"","institution":"Covenant University","correspondingAuthor":false,"prefix":"","firstName":"Itunuoluwa","middleName":"","lastName":"Isewon","suffix":""},{"id":214803294,"identity":"a8258c7e-3338-428a-adfe-cc8bd0f61534","order_by":6,"name":"Oyekanmi Nash","email":"","orcid":"","institution":"NABDA/FMST","correspondingAuthor":false,"prefix":"","firstName":"Oyekanmi","middleName":"","lastName":"Nash","suffix":""},{"id":214803295,"identity":"582606a3-5b91-41d5-94e6-7150886fcbe9","order_by":7,"name":"Opeyemi Soremekun","email":"","orcid":"","institution":"MRC/UVRI and London School of Hygiene and Tropical Medicine London (LSHTM)","correspondingAuthor":false,"prefix":"","firstName":"Opeyemi","middleName":"","lastName":"Soremekun","suffix":""},{"id":214803296,"identity":"3e2785c7-52dd-4e9e-86e8-ad0ba7f37621","order_by":8,"name":"Segun Fatumo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2RsUrEQBCG/7Bgmq3lZMF7hQkBUe4kr5LlQAtzciBIqpDKbfYBDnwKObAOLGiTWgJXeGKrcGVAC7MRQYSNlhb7FTsMw8fO8AMezz9kXPZl/7PbLpB+TcilUNWXGLx7gyUh5b8q+KYw/iclVA9iARonozP5PKXiPNmtgm0LEztv0fWlWIIiPZqv4ozMBRcp29MwBy4FTXYiON6DTrkVGVVSixQCMFOn8vhiFUp65ZAKq7C3QaUJ76wiewXErLJjf3EuRnXGJpxopuvXVaTJSL2WV0eaTt3nq/unNc/pWKn5zabNC6muZ6Zp80lUOjfrsviRQVAOpGIJN0NTj8fj8QAfpNJOBF4710sAAAAASUVORK5CYII=","orcid":"","institution":"NABDA/FMST","correspondingAuthor":true,"prefix":"","firstName":"Segun","middleName":"","lastName":"Fatumo","suffix":""}],"badges":[],"createdAt":"2023-06-15 16:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3068800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3068800/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13104-024-06744-5","type":"published","date":"2024-03-27T15:01:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39600144,"identity":"adcf7ea2-8186-4604-98e4-4c24ab502ff7","added_by":"auto","created_at":"2023-07-05 20:49:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":267989,"visible":true,"origin":"","legend":"\u003cp\u003eThe Hexosamine Biosynthesis pathway promotes protein O-GlcNAcylation by supplying the O-GlcNAc moiety for addition and removal on nuclear and cytoplasmic proteins. (Kanwal et al., 2015)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3068800/v1/8c8ab46a46063832d9b110ee.jpeg"},{"id":39600145,"identity":"316ac9a4-6537-4d09-96ce-b238bc42c12f","added_by":"auto","created_at":"2023-07-05 20:49:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1065736,"visible":true,"origin":"","legend":"\u003cp\u003ea;\u003cstrong\u003e \u003c/strong\u003eVerify the 3D plot for the modeled protein b; Ramachandran plot showing the majority of the modeled protein's residues in the favored region c; The Ramachandran plot statistics provide values for the residues d; the ERRAT overall quality factor is 98.716\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3068800/v1/e5f42b29bcb827d7c6fcadb0.jpg"},{"id":53869558,"identity":"ddd3cbb0-f9ed-40bd-aeda-1cbb5c7505cd","added_by":"auto","created_at":"2024-04-01 15:10:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3068800/v1/f9430a07-907a-47ac-b161-2000de7ed4cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An In-silico Analysis of OGT gene association with diabetes mellitus","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human O-linked N-acetylglucosamine transferase (OGT) gene is ~\u0026thinsp;43 kb long. Located at the Xq13.1 genomic locus, it is alternatively spliced to generate nucleocytoplasmic (nc), mitochondrial (m), and short (s) isoforms. These isoforms are distinguished by the varying number of tetratricopeptide repeats (TPRs) in their N-terminal domains. The full-length human nucleocytoplasmic OGT isoform (~\u0026thinsp;110 kDa) contains 13 TPRs, while mitochondrial OGT (~\u0026thinsp;103 kDa) and short OGT (~\u0026thinsp;75 kDa) contain 9 and 3 TPRs, respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The OGT gene encodes the OGT protein.\u003c/p\u003e \u003cp\u003eProtein O-GlcNAc transferase (OGT) is responsible for the addition of the GlcNAc moiety to the threonine and serine residues of cytoplasmic and nuclear proteins. Because it is involved in cell signaling, glucose homeostasis in the liver, and the regulation of the clock genes' circadian oscillation, its absence has been found to be lethal in mice [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Torres and Hart discovered it about 30 years ago [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and it has been found to be linked to x-linked intellectual disability and insulin resistance in muscle and adipocyte cells when mutated [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Its contribution to glucose metabolism via the Hexosamine Biosynthesis Pathway directly links it to diabetes mellitus [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiabetes mellitus (DM) is a metabolic disorder that comes in two forms: T1DM and T2DM. T1DM is caused by the defective secretion of insulin, while T2DM is caused by a defect in insulin action [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Diabetes is caused by a variety of factors, including but not limited to lifestyle, genetics, and diet. Diabetes is estimated to kill 6.7\u0026nbsp;million people worldwide in 2021, with 537\u0026nbsp;million adults living with the disease, a figure that is expected to rise to 783\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-synonymous single nucleotide polymorphisms (nsSNPs) are protein amino acid substitutions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a result, the goal of this study is to identify disease-causing and deleterious SNPs within the OGT gene, as well as druggable targets, in order to discover therapeutic drugs for diabetes mellitus via this gene.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data retrieval for single nucleotide polymorphisms\u003c/h2\u003e \u003cp\u003eThe OGT variants and SNPs were retrieved from the National Centre for Biotechnology Information's (NCBI) dbSNPs server [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The SNPs were chosen based on their clinical significance as reported by ClinVar [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Investigating the functional effects of coding nsSNPs\u003c/h2\u003e \u003cp\u003eThe deleterious potential of the OGT nsSNPs was assessed using four major tools: Predictor of Human Deleterious Single Nucleotide Polymorphism (PhD-SNP) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], SNPs\u0026amp;Go [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], PROVEAN v1.1[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and Polymorphism Phenotyping v2 (Polyphen2) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. SNPs\u0026amp;GO is an algorithm that predicts deleterious nsSNPs based on protein functional annotation. PHD-SNP is an online tool for predicting point mutations in protein sequences and determining the impact of these mutations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the program predicts how the single point amino acid change will cause disease. PROVEAN predicts changes in a protein's biological functions caused by single amino acid substitutions and a score of less than \u0026minus;\u0026thinsp;2.5 is predicted to be harmful.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of protein stability of predicted OGT nsSNPs\u003c/h2\u003e \u003cp\u003eThe i-Stable 2.0 server, which includes tools such as iPTREE-STAB, I-Mutant 2.0, and MUpro, was used to predict the structure-function relationship of the SNPs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The i-Mutant tool calculates the Gibbs free energy for the wild type protein and subtracts it from the mutant form to estimate the free energy changes. The predicted values of all OGT mutant types may alter protein stability with associated free energy. Positive DDG values indicate that the mutated proteins are highly stable, whereas negative scores indicate that they are less stable [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of the evolutionary conservation of amino acids\u003c/h2\u003e \u003cp\u003eThe Consurf program is used to investigate the evolutionary conservation of OGT amino acids. It uses a Bayesian method to determine the conserved amino acids for the identification of the structural and functional residues in the conserved regions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Prediction of the amino acids are into variable (range between 1 and 4), intermediate (range between 5 and 6), and conserved (range between 7 and 9) based on their scores and color indications [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein modelling and molecular docking\u003c/h2\u003e \u003cp\u003eUsing the protein sequence retrieved from the UniProt database, we used the ROBETTA homology modelling tool to predict the 3D structure of the OGT apo-protein [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The predicted structure was viewed using the Schrodinger Maestro v11.1 workspace and validated using the Verify-3D and ERRAT programs available in the SAVES server [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Schrodinger-Maestro v11.1's Protein Preparation Wizard module was used to preprocess, optimize, and minimize the crystal structure of OGT. While keeping the pH at 7, structural water molecules were kept to ensure protein stability, while redundant water molecules were removed to facilitate protein-ligand binding. Hydrogens were also added to fill in the gaps and to mediate hydrogen bridges and electrostatic forces [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We used the SiteMap feature of the Schrodinger Maestro software to identify potential binding pockets on the OGT protein [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The generation of receptor grids was expedient in order to limit ligand docking to only the identified binding pockets [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The grid box had dimensions of x = -32.724, y\u0026thinsp;=\u0026thinsp;51.454, and z\u0026thinsp;=\u0026thinsp;83.332. The PubChem database was used to retrieve the 2D structure of OSMI-1, a small molecule inhibitor of OGT [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The OSMI-1 was prepared and converted to its 3D geometry prior to molecular docking using the LigPrep module of Maestro v.11.1 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 nsSNPs obtained from the dbSNPs database\u003c/h2\u003e\n\u003cp\u003eThe discovery of disease-causing nsSNPs are useful in the development of candidate drug therapy because they are biological markers involved in disease occurrence or progression [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The NCBI server yielded 159 nsSNPs [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] (Supplementary table 1). According to ClinVar, the retrieval favored only SNPs with clinical significance [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Identification of damaging nsSNPs in OGT\u003c/h2\u003e\n\u003cp\u003eWe used four (4) tools to predict the potential deleteriousness of 25 nsSNPs, with at least three (3) of the four (4) tools predicting a negative effect (Table a). PROVEAN predicted seven (7) nsSNPs to be harmful, and using the PolyPhen-2 tool, all seven (7) nsSNPs were predicted to be probably harmful, with scores ranging from 0.932 to 1.000. SNPs\u0026amp;GO and PhD-SNP both predicted diseased SNPs. The total number of deleterious SNPs was reduced to 7 based on their detrimental effect across all four tools used (Table b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable a: Damaging nsSNPs from OGT\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS/N\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ers ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAA Change/position\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePROVEAN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhD-SNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSNPs\u0026amp;GO\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePolyPhen2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers766646613\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR627C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;4.677 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers131705060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR117C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.194 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.932 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers204042438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP879L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.041 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.942 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers204039392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP685Q\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.872 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.994 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers204042400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR867C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.399 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI- 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.994 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers204034593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA380V\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.790 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.938 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers766646613\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR627C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;4.677 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040347448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM401T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.727 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.998 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040347668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC417Y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.596 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.989 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040350890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD481G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.340 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebenign\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040368778\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH611N\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.952 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.55 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040387073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP657L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.335 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040191136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY112S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.489 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040329106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY228H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.680 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040334939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR250C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.093 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040341169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG341V\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.294 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040345810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL367F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.717 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040190682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR102G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.116 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeutral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.930 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040334968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR250L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.405 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers772525369\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR899C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.682 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1114167891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR284P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.060 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.951 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1556046834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG103R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.717 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.993 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1602152230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN648Y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.605 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.998 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040405196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC845S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.654 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.930 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers200109331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN196K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.599 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable b: Predicted deleterious nsSNPs across the four tools\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS/N\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ers ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAA Change/Position\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePROVEAN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePhD-SNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSNPs\u0026amp;GO\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePolyPhen2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040329106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY228H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.680 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997 PROBABLY DAMAGING\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040334939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR250C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.093 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 PROBABLY DAMAGING\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040341169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG341V\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.294 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991 PROBABLY DAMAGING\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040345810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL367F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.717 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999 PROBABLY DAMAGING\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040405196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC845S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.654 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.930 probably damaging\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1556046834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG103R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.717 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.993 PROBABLY DAMAGING\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers200109331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN196K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.599 Deleterious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisease RI-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 PROBABLY DAMAGING\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Protein stability profile prediction for nsSNPs in OGT\u003c/h2\u003e\n\u003cp\u003eThe iStable 2.0 tool was used to predict protein stability [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. All seven predicted highly deleterious SNPs were also predicted to reduce OGT protein stability. The results of MUpro SVM, MUpro MM, I-Mutant 2.0, and iPTREE-STAB are shown in Table c.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable c: nsSNPs stability profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS/N\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAA Change\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eI-Mutant2.0 SEQ\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMUpro_SVM\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMUpro_NN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eiPTREE-STAB\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040329106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY228H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040334939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR250C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040341169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG341V\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040345810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL367F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2040405196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC845S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1556046834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG103R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers200109331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN196K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDecrease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Conservation prediction of damaging nsSNPs in OGT\u003c/h2\u003e\n\u003cp\u003eConsurf predicted that Y228H, C845S, and L367F would be buried and conserved, whereas G103R, N196K, R250C, and G341V would be exposed and conserved (Table d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable d: ConSurf result output\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabd\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS/N\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAmino acid change\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePos\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSeq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScore\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eColor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConfidence interval\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConfidence interval colors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB/e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFunction\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMsa data\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidue variety\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG103R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.417, 0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG,N,A,V\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN196K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.805\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.861,-0.777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN,Y,S\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY228H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.417,-0.080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY,L,H,F\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR250C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.350, 0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eK,R,E,S,T,N,H,Q,A\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC845S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.272, 0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ,H,Y,T,E,S,K,R\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG341V\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.799,-0.660\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ee\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS,G,C,N\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL367F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.849,-0.752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9,9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003es\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146/150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI,Y,L\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 OGT structural characterization of wild and mutant types in comparison\u003c/h2\u003e\n\u003cp\u003eERRAT and Verify-3D were used to validate the protein structure (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the Verify-3D results, 94.39% of the residues have an average 3D-ID score of 0.2. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The Ramachandran plot, which is available in PROCHECK, was used to assess the quality of the 3D protein structure (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). According to the plot, 91.3%, 8.0%, 0.3%, and 0.3% of the residues are in the favored, allowed, generously allowed, and disallowed regions, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). This confirms the protein structure's high quality. ERRAT also demonstrated an overall quality factor of 98.7161 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed), implying that the results obtained from the aforementioned tools indicated that our modeled protein is of high quality and can be used for further investigation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6 OGT Mutant type as a potential drug target\u003c/h2\u003e\n\u003cp\u003eThe Glide module of the Schr\u0026ouml;dinger Maestro Suite was used to investigate the protein-ligand binding affinity of OSMI-1 and the OGT protein. OSMI-1 interacted well with the active site residues of OGT (supplementary Fig.\u0026nbsp;1), and the docking scores for each interaction are shown in table e. These predictions can be validated using additional downstream analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable e: Molecular docking results of mutant type OGT against OSMI-1\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabe\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS/N\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAMINO ACID CHANGE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDOCKING SCORES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eINTERACTING RESIDUES\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG103R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLU649, LYS534, ARG338, and ASN621\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN196K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYS644, GLY645, and ASN648\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY228H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYS534, ASN621, ALA646, and TYR642\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR250C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHIS508, LYS852, THR932, PHE878, LYS908, HIS568, and LYS644\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC845S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLU649\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG341V\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASN567, SER594, and LYS644\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL367F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLU649, ALA646, TYR642, and LYS534\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBased on similarity and homology data, Consurf calculates the evolutionary profile of proteins and the effects of amino acid substitutions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Because the nsSNPs were found in a conserved region, a change in the amino acid sequence in those regions will affect the structural and functional profile of the OGT protein. Only the mutation points in G103R, Y228H, R250C, C845S, G341V, N196K, and L367F were found to be harmful across all four tools used, out of the 25 deleterious nsSNPs identified. They also significantly reduced the OGT protein's stability, as predicted by the i-Stable 2.0 web tool. The current study's strength lies in the use of various algorithms to obtain precise prediction results for the identified nsSNPs, which could be used as druggable reference points for the discovery of drugs to treat diabetes mellitus.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe OGT protein has been linked to the progression of diabetes mellitus because it catalyzes the addition of the o-GlcNAc sugar moiety on nucleocytoplasmic proteins, a substrate of the hexosamine biosynthesis pathway, increasing the amount of intracellular glucose content. In this study, 159 OGT nsSNPs in coding regions were chosen, and structural analysis of the chosen 7 nsSNPs predicted a negative impact on protein function and stability. Overall, the findings indicated that nsSNPs could be used in drug development for diabetes mellitus.\u003c/p\u003e"},{"header":"6. Declarations","content":"\u003cp\u003e\u003cstrong\u003e(i) Authors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSegun Fatumo, Oyekanmi Nash, and Opeyemi Soremekun conceptualized the study and supervised the project. Abigail O. Ayodele, Brenda Udosen, and Opeyemi Soremekun led the main analyses. Abigail O. Ayodele wrote the first draft of the manuscript. All authors reviewed the first draft and provided critical feedback. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(ii) Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work didn\u0026rsquo;t receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iii) Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(iv) Availability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. PolyPhen2; http://genetics.bwh.harvard.edu/pph2/\u003c/p\u003e\n\u003cp\u003e2. SNPs\u0026amp;Go; https://snps-and-go.biocomp.unibo.it/snps-and-go/\u003c/p\u003e\n\u003cp\u003e3. PhD-SNP; https://snps.biofold.org/phd-snp/phd-snp.html\u003c/p\u003e\n\u003cp\u003e4. PROVEAN; https://bio.tools/provean\u003c/p\u003e\n\u003cp\u003e5. SNPs database; https://www.ncbi.nlm.nih.gov/snp/\u003c/p\u003e\n\u003cp\u003e6. Consurf; https://consurf.tau.ac.il/consurf_index.php\u003c/p\u003e\n\u003cp\u003e7. ROBETTA; https://robetta.bakerlab.org/\u003c/p\u003e\n\u003cp\u003e8. ClinVar; https://www.ncbi.nlm.nih.gov/clinvar/\u003c/p\u003e\n\u003cp\u003e9. ERRAT; https://www.doe-mbi.ucla.edu/errat/\u003c/p\u003e\n\u003cp\u003e10. Verify3D; https://www.doe-mbi.ucla.edu/verify3d/\u003c/p\u003e\n\u003cp\u003e11. SAVES; https://saves.mbi.ucla.edu/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(v) Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Institutes of Health Common Fund to the H3ABioNet Project grant number (5U24HG006941-09). Segun Fatumo is an international intermediate fellow funded by the Wellcome Trust grant (220740/Z/20/Z) at the MRC/UVRI and LSHTM. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(vi) Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved and consented to participate the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(vii) Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHanover, J. A., Yu, S., Lubas, W. B., Shin, S.-H., Ragano-Caracciola, M., Kochran, J., \u0026amp; Love, D. C. (2003). Mitochondrial and nucleocytoplasmic isoforms of O-linked GlcNAc transferase encoded by a single mammalian gene. Archives of Biochemistry and Biophysics, 409(2), 287\u0026ndash;297.\u003c/li\u003e\n\u003cli\u003eLove, D. C., Kochran, J., Cathey, R. L., Shin, S.-H., \u0026amp; Hanover, J. A. (2003). 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Journal of Computer-Aided Molecular Design, 30(5), 401\u0026ndash;412.\u003c/li\u003e\n\u003cli\u003eAshkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., \u0026amp; Ben-Tal, N. (2016). ConSurf 2016: An improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Research, 44(W1), W344\u0026ndash;W350.\u003c/li\u003e\n\u003cli\u003eAshkenazy, H., Erez, E., Martz, E., Pupko, T., \u0026amp; Ben-Tal, N. (2010). ConSurf 2010: Calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Research, 38(suppl_2), W529\u0026ndash;W533.\u003c/li\u003e\n\u003cli\u003eWang, Y., Wang, Q., Huang, H., Huang, W., Chen, Y., McGarvey, P. B., Wu, C. H., Arighi, C. N., \u0026amp; Consortium, on behalf of the U. (2021). A crowdsourcing open platform for literature curation in UniProt. PLOS Biology, 19(12), e3001464. https://doi.org/10.1371/journal.pbio.3001464\u003c/li\u003e\n\u003cli\u003eSAVESv6.0\u0026mdash;Structure Validation Server. (n.d.). Retrieved July 26, 2022, from https://saves.mbi.ucla.edu/\u003c/li\u003e\n\u003cli\u003eMadhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., \u0026amp; Sherman, W. (2013). Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221\u0026ndash;234.\u003c/li\u003e\n\u003cli\u003eHalgren, T. A. (2009). Identifying and characterizing binding sites and assessing druggability. Journal of Chemical Information and Modeling, 49(2), 377\u0026ndash;389.\u003c/li\u003e\n\u003cli\u003eAdebesin, A. O., Ayodele, A. O., Omotoso, O., Akinnusi, P. A., \u0026amp; Olubode, S. O. (2022). Computational evaluation of bioactive compounds from Vitis vinifera as a novel \u0026beta;-catenin inhibitor for cancer treatment. Bulletin of the National Research Centre, 46(1), 1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eKim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., \u0026amp; Bryant, S. H. (2016). PubChem Substance and Compound databases. Nucleic Acids Research, 44(Database issue), D1202. https://doi.org/10.1093/nar/gkv951\u003c/li\u003e\n\u003cli\u003eWilson, J., Nampoothiri, M., \u0026amp; Satarker, S. (2021). In silico screening of existing FDA approved drugs for spermine synthase inhibition as a therapeutic approach in Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s \u0026amp; Dementia, 17, e058496.\u003c/li\u003e\n\u003cli\u003eKaur, S., Ali, A., Ahmad, U., Siahbalaei, Y., Pandey, A. K., \u0026amp; Singh, B. (2019). Role of single nucleotide polymorphisms (SNPs) in common migraine. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 55(1), 47. https://doi.org/10.1186/s41983-019-0093-8\u003c/li\u003e\n\u003cli\u003eSoremekun, O. S., Ezenwa, C., Isewon, I., Soliman, M., Idowu, O., Nashiru, O., \u0026amp; Fatumo, S. (2020). Computational and drug target analysis of functional single nucleotide polymorphisms associated with Haemoglobin Subunit Beta (HBB) gene. Computers in Biology and Medicine, 125, 104018. https://doi.org/10.1016/j.compbiomed.2020.104018\u003c/li\u003e\n\u003cli\u003eSmigielski, E. M., Sirotkin, K., Ward, M., \u0026amp; Sherry, S. T. (2000). dbSNP: A database of single nucleotide polymorphisms. Nucleic Acids Research, 28(1), 352\u0026ndash;355.\u003c/li\u003e\n\u003cli\u003eUdosen, B., Soremekun, O., Ekenna, C., Idowu Omotuyi, O., Chikowore, T., Nashiru, O., \u0026amp; Fatumo, S. (2021). In-silico analysis reveals druggable single nucleotide polymorphisms in angiotensin 1 converting enzyme involved in the onset of blood pressure. BMC Research Notes, 14(1), 457. https://doi.org/10.1186/s13104-021-05879-z\u003c/li\u003e\n\u003cli\u003eAloyuni, S. A. (2021). In silico prediction of deleterious single nucleotide polymorphism in human AKR1C3 gene and identification of potent inhibitors using molecular docking approach. Journal of King Saud University - Science, 33(6), 101514. doi:10.1016/j.jksus.2021.101514\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary information","content":"\u003cp\u003eSupplementary Figure and Supplementary Table are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Single nucleotide polymorphism (SNPs), O-linked N-Acetylglucosamine transferase (OGT), 3, and 4","lastPublishedDoi":"10.21203/rs.3.rs-3068800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3068800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eO-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two main proteins; the O-linked N-acetylglucosamine transferase (OGT) catalyzing the addition, and the glycoside hydrolase OGA (O-GlcNAcase) catalyzing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins. This process is necessary for various important cellular functions. The O-linked N-acetylglucosamine transferase (OGT) gene is responsible for the production of 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). In this study, we retrieved a total of 159 SNPs with clinical significance from the SNPs database and 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\u0026amp;Go, PROVEAN, and PolyPhen2). Proceeding with this, we used ROBETTA, a homology modeling tool, to model the proteins with these point mutations and carried out a structural bioinformatics method \u0026ndash; molecular docking \u0026ndash; 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, and as a result, very good binding affinities and interactions were observed between this ligand and the active site residues within 4\u0026Aring; of OGT. We conclude that these mutation points may be used for further downstream analysis as drug targets for the treatment of diabetes mellitus.\u003c/p\u003e","manuscriptTitle":"An In-silico Analysis of OGT gene association with diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-05 20:49:07","doi":"10.21203/rs.3.rs-3068800/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-05T13:19:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-05T10:09:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-01T05:28:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2023-06-15T15:58:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2321cc8-975d-44aa-94f7-e1222f0a5b59","owner":[],"postedDate":"July 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-01T15:04:41+00:00","versionOfRecord":{"articleIdentity":"rs-3068800","link":"https://doi.org/10.1186/s13104-024-06744-5","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2024-03-27 15:01:08","publishedOnDateReadable":"March 27th, 2024"},"versionCreatedAt":"2023-07-05 20:49:07","video":"","vorDoi":"10.1186/s13104-024-06744-5","vorDoiUrl":"https://doi.org/10.1186/s13104-024-06744-5","workflowStages":[]},"version":"v1","identity":"rs-3068800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3068800","identity":"rs-3068800","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}