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Functional Analysis of Single Nucleotide Polymorphism in ZUFSP Protein and Implication in Pathogenesis

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Abstract

Researches have revealed that functional non-synonymous Single Nucleotide Polymorphism (nsSNPs) present in the Zinc-finger with UFM1-Specific Peptidase domain protein (ZUFSP) may be involved in genetic instability and carcinogenesis. For the first time, we employed in-silico approach using predictive tools to identify and validate potential nsSNPs that could be pathogenic. Our result revealed that 8 nsSNPs (rs 112738382, rs 140094037, rs 201652589, rs 201847265, rs 202076827, rs 373634906, rs 375114528, rs 772591104) are pathogenic after being subjected to rigorous filtering process. The structural impact of the nsSNPs on ZUFSP structure indicated that the nsSNPs affect the stability of the protein by lowering ZUFSP protein stability. Furthermore, conservation analysis showed that rs 201652589, rs 140094037, rs 201847265, and rs 772591104 were highly conserved. Interestingly, the protein–protein affinity between ZUFSP and Ubiquitin was altered rs 201652589, rs 140094037, rs 201847265, and rs 772591104 had a binding affinity of − 0.46, − 0.83, − 1.62, and − 1.12 kcal/mol respectively. Our study has been able to identify potential nsSNPs that could be used as genetic biomarkers for some diseases arising as a result of aberration in the ZUFSP structure, however, being a predictive study, the identified nsSNPs need to be experimentally investigated.

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References

  1. Baker RT, Tobias JW, Varshavsky A (1992) Ubiquitin-specific proteases of Saccharomyces cerevisiae. Cloning of UBP2 and UBP3, and functional analysis of the UBP gene family. J Biol Chem 267:23364–23375

    Article  CAS  Google Scholar 

  2. Matsui SI, Sandberg AA, Negoro S et al (1982) Isopeptidase: a novel eukaryotic enzyme that cleaves isopeptide bonds. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.79.5.1535

    Article  PubMed  Google Scholar 

  3. Poondla N, Chandrasekaran AP, Kim KS, Ramakrishna S (2019) Deubiquitinating enzymes as cancer biomarkers: new therapeutic opportunities? BMB Rep. https://doi.org/10.5483/BMBRep.2019.52.3.048

    Article  PubMed  PubMed Central  Google Scholar 

  4. Poondla N, Chandrasekaran AP, Kim KS, Ramakrishna S (2019) Deubiquitinating enzymes as cancer biomarkers: new therapeutic opportunities? BMB Rep 52:181–189

    Article  CAS  Google Scholar 

  5. Mevissen TET, Komander D (2017) Mechanisms of deubiquitinase specificity and regulation. Annu Rev Biochem. https://doi.org/10.1146/annurev-biochem-061516-044916

    Article  PubMed  Google Scholar 

  6. Nijman SMB, Luna-Vargas MPA, Velds A et al (2005) A genomic and functional inventory of deubiquitinating enzymes. Cell. https://doi.org/10.1016/j.cell.2005.11.007

    Article  PubMed  Google Scholar 

  7. Kwasna D, Abdul Rehman SA, Natarajan J et al (2018) Discovery and characterization of ZUFSP/ZUP1, a distinct deubiquitinase class important for genome stability. Mol Cell. https://doi.org/10.1016/j.molcel.2018.02.023

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hanpude P, Bhattacharya S, Dey AK, Maiti TK (2015) Deubiquitinating enzymes in cellular signaling and disease regulation. IUBMB Life. https://doi.org/10.1002/iub.1402

    Article  PubMed  Google Scholar 

  9. D’Arcy P, Wang X, Linder S (2015) Deubiquitinase inhibition as a cancer therapeutic strategy. Pharmacol Ther. https://doi.org/10.1016/j.pharmthera.2014.11.002

    Article  PubMed  Google Scholar 

  10. Jemal A, Bray F, Ferlay J (1999) Global cancer statistics: 2011. CA Cancer J Clin. https://doi.org/10.3322/caac.20107.Available

    Article  Google Scholar 

  11. Landegren U, Nilsson M, Kwok PY (1998) Reading bits of genetic information: methods for single-nucleotide polymorphism analysis. Genome Res 8:769–776

    Article  CAS  Google Scholar 

  12. Ke X, Taylor MS, Cardon LR (2008) Singleton SNPs in the human genome and implications for genome-wide association studies. Eur J Hum Genet 16:506–515. https://doi.org/10.1038/sj.ejhg.5201987

    Article  CAS  PubMed  Google Scholar 

  13. Shastry BS (2002) SNP alleles in human disease and evolution. J Hum Genet 47:561–566

    Article  CAS  Google Scholar 

  14. Dakal TC, Kala D, Dhiman G et al (2017) Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene. Sci Rep. https://doi.org/10.1038/s41598-017-06575-4

    Article  PubMed  PubMed Central  Google Scholar 

  15. Sheryl ST et al (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29(1):308–311

  16. Abdelraheem NE, El-tayeb GM, Osman LO, Abedlrhman SA (2016) A comprehensive in silico analysis of the functional and structural impact of non-synonymous single nucleotide polymorphisms in the human KRAS gene. Am J Bioinform Res 6:32–55. https://doi.org/10.5923/j.bioinformatics.20160602.02

    Article  Google Scholar 

  17. Vaser R, Adusumalli S, Leng SN et al (2016) SIFT missense predictions for genomes. Nat Protoc 11:1–9. https://doi.org/10.1038/nprot.2015.123

    Article  CAS  PubMed  Google Scholar 

  18. Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249

    Article  CAS  Google Scholar 

  19. Hecht M, Bromberg Y, Rost B (2015) Better prediction of functional effects for sequence variants. BMC Genom. https://doi.org/10.1186/1471-2164-16-S8-S1

    Article  Google Scholar 

  20. Bromberg Y, Overton J, Vaisse C et al (2009) In silico mutagenesis: a case study of the melanocortin 4 receptor. FASEB J 23:3059–3069. https://doi.org/10.1096/fj.08-127530

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yachdav G, Hecht M, Pasmanik-Chor M et al (2014) HeatMapViewer: interactive display of 2D data in biology. F1000Research. https://doi.org/10.12688/f1000research.3-48.v1

    Article  PubMed  PubMed Central  Google Scholar 

  22. Choi Y, Sims GE, Murphy S et al (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS ONE 7:e46688. https://doi.org/10.1371/journal.pone.0046688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Capriotti E, Fariselli P, Rossi I, Casadio R (2008) A three-state prediction of single point mutations on protein stability changes. BMC Bioinform 9(Suppl 2):S6. https://doi.org/10.1186/1471-2105-9-S2-S6

    Article  CAS  Google Scholar 

  24. Emidio C, Fariselli P (2017) PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Res 45(W1): W247–W252

  25. Ferrer-Costa C, Gelpí JL, Zamakola L, Parraga I, De La Cruz X, Orozco M (2005) PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics 21:3176–3178

    Article  CAS  Google Scholar 

  26. Calabrese R, Capriotti E, Fariselli P et al (2009) Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat 30:1237–1244. https://doi.org/10.1002/humu.21047

    Article  CAS  PubMed  Google Scholar 

  27. Emidio C, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33(2):W306–W310. https://doi.org/10.1093/nar/gki375.

  28. Cheng J, Randall A, Baldi P (2006) Prediction of protein stability changes for single-site mutations using support vector machines. Prot Struct Funct Genet 62:1125–1132. https://doi.org/10.1002/prot.20810

    Article  CAS  Google Scholar 

  29. Liang-Tsung Huang, Gromiha MM, Ho S (2007) iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations. Bioinformatics 23(10):1292–1293. https://doi.org/10.1093/bioinformatics/btm100

  30. Emidio C, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33(2):W306–W310. https://doi.org/10.1093/nar/gki375.

    Article  CAS  Google Scholar 

  31. Witvliet DK, et al. (2016) ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity. Bioinformatics 32(10):1589–1591. https://doi.org/10.1093/bioinformatics/btw031

    Article  CAS  PubMed  Google Scholar 

  32. Pejaver V, Hsu WL, Xin F et al (2014) The structural and functional signatures of proteins that undergo multiple events of post-translational modification. Prot Sci 23:1077–1093. https://doi.org/10.1002/pro.2494

    Article  CAS  Google Scholar 

  33. Petersen B, Petersen T. N., Andersen P., et al (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol, 9:51, 1-10. https://doi.org/10.1186/1472-6807-9-51

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rodrigues CHM, Myung Y, Pires DEV, Ascher DB (2019) MCSM-PPI2: predicting the effects of mutations on protein-protein interactions. Nucleic Acids Res 47:W338–W344. https://doi.org/10.1093/nar/gkz383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. https://doi.org/10.1002/jcc.20290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general Amber force field. J Comput Chem 25:1157–1174. https://doi.org/10.1002/jcc.20035

    Article  CAS  PubMed  Google Scholar 

  37. Berendsen HJC, Postma JPM, Van Gunsteren WF et al (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. https://doi.org/10.1063/1.448118

    Article  CAS  Google Scholar 

  38. Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341. https://doi.org/10.1016/0021-9991(77)90098-5

    Article  CAS  Google Scholar 

  39. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9:3084–3095. https://doi.org/10.1021/ct400341p

    Article  CAS  PubMed  Google Scholar 

  40. Soremekun OS, Soliman MES (2019) From genomic variation to protein aberration: mutational analysis of single nucleotide polymorphism present in ULBP6 gene and implication in immune response. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2019.103354

    Article  PubMed  Google Scholar 

  41. Nailwal M, Chauhan JB (2017) In silico analysis of non-synonymous single nucleotide polymorphisms in human DAZL gene associated with male infertility. Syst Biol Reprod Med. https://doi.org/10.1080/19396368.2017.1305466

    Article  PubMed  Google Scholar 

  42. Suresh PS, Venkatesh T, Rajan T (2012) Single nucleotide polymorphisms in genes that are common targets of luteotropin and luteolysin in primate corpus luteum: computational exploration. Gene 511:353–357. https://doi.org/10.1016/j.gene.2012.09.076

    Article  CAS  PubMed  Google Scholar 

  43. Brender JR, Zhang Y (2015) Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles. PLOS Comput Biol 11:e1004494. https://doi.org/10.1371/journal.pcbi.1004494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang LL, Li Y, Zhou SF (2009) A bioinformatics approach for the phenotype prediction of nonsynonymous single nucleotide polymorphisms in human cytochromes P450E. Drug Metab Dispos 37:977–991. https://doi.org/10.1124/dmd.108.026047

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors appreciate the financial and infrastructural support of College of Health Sciences, UKZN and also acknowledge the Centre for High Performance Computing (CHPC, www.chpc.ac.za), Cape Town for provision of computational resource.

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This research was not funded by any organization for the submitted work.

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Authors and Affiliations

  1. Department of Medical Biochemistry, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Howard Campus, Durban, 4000, South Africa

    Mary B. Ajadi & Hezekiel M. Kumalo

  2. Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, 4001, South Africa

    Opeyemi S. Soremekun, Adeniyi T. Adewumi & Mahmoud E. S. Soliman

  3. Chemical Pathology Department, Faculty of Basic Medical Sciences, College of Health Sciences, Ladoke Akintola University of Technology, PMB 4400, Osogbo, Nigeria

    Mary B. Ajadi

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Correspondence to Mahmoud E. S. Soliman.

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Ajadi, M.B., Soremekun, O.S., Adewumi, A.T. et al. Functional Analysis of Single Nucleotide Polymorphism in ZUFSP Protein and Implication in Pathogenesis. Protein J 40, 28–40 (2021). https://doi.org/10.1007/s10930-021-09962-z

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