اخبار و اعلانات

 آمار

تعداد نشریات24
تعداد شماره‌ها912
تعداد مقالات8,158
تعداد مشاهده مقاله15,838,242
تعداد دریافت فایل اصل مقاله13,333,306
Behbahani, Mandana, Rabiei, Parisa, Mohabatkar, Hassan. (1400). Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2. , 10(4), 171-178. doi: 10.22099/mbrc.2021.42187.1692
Mandana Behbahani; Parisa Rabiei; Hassan Mohabatkar. "Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2". , 10, 4, 1400, 171-178. doi: 10.22099/mbrc.2021.42187.1692
Behbahani, Mandana, Rabiei, Parisa, Mohabatkar, Hassan. (1400). 'Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2', , 10(4), pp. 171-178. doi: 10.22099/mbrc.2021.42187.1692
Behbahani, Mandana, Rabiei, Parisa, Mohabatkar, Hassan. Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2. , 1400; 10(4): 171-178. doi: 10.22099/mbrc.2021.42187.1692

Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2

Molecular Biology Research Communications
دوره 10، شماره 4، 2021، صفحه 171-178    اصل مقاله (282.33 K)
نوع مقاله: Original article
شناسه دیجیتال (DOI): 10.22099/mbrc.2021.42187.1692
نویسندگان
Mandana Behbahani؛ Parisa Rabiei؛ Hassan Mohabatkar*
Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
چکیده
SARS-CoV-2 is a member of β-genus of the coronavirus subfamily, alongside the virus that causes SARS (Severe Acute Respiratory Syndrome). As implied by their names, SARS-CoV-2 and SARS-CoV genome sequences have close kinship (about 79% genomic sequence similarity). In the current research, sequence-based physiochemical properties of RNA polymerase and membrane glycoprotein of SARS-CoV-2 and SARS-CoV were compared. In addition, impacts of substitution mutations on stability and glycosylation patterns of these proteins were studied. In comparison of physiochemical features of membrane and RNA polymerase proteins, only instability index of membrane protein was difference between SARS-CoV and SARS-CoV-2. Mutation analysis showed increase in stability of RNA polymerase and decrease in stability of membrane protein in SARS-CoV-2. Glycosylation pattern analysis showed glycosylation enhancement in both membrane and RNA polymerase proteins of SARS-CoV-2 in comparison to SARS-CoV. In conclusion, more glycosylation and stability of SARS-CoV-2 RNA polymerase could be one of the reasons of high pathogenicity property and host immune system evasion of SARS-CoV-2. 
کلیدواژه‌ها
COVID-19؛ Betacoronavirus؛ MEME motifs؛ Glycosylation؛ Substitution mutation
مراجع
  1. Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, Xing F, Liu J, Yip CC-Y, Poon RW-S, Tsoi HW, Fai Lo SK, Chan KH, Poon VKM, Chan WM, Ip JD, Cai JP, Cheng VCC, Chen H, Hui CKM, Yuen KY. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 2020;395:514-523.
  2. Delgado J, Radusky LG, Cianferoni D, Serrano L. FoldX 5.0: working with RNA, small molecules and a new graphical interface. Bioinformatics 2019;35:4168-4169.
  3. Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh C-L, Abiona O, Graham BS, McLellan JS. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 2020;367:1260-1263.
  4. Astuti I, Ysarafil. Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response. Diabetes Metab Syndr 2020;14:407-412.
  5. Schoeman D, Fielding BC. Coronavirus envelope protein: current knowledge. Virol J 2019; 16:1-22.
  6. Wang Y, Liu L. The membrane protein of severe acute respiratory syndrome coronavirus functions as a novel cytosolic pathogen-associated molecular pattern to promote beta interferon induction via a Toll-like-receptor-related TRAF3-independent mechanism. mBio 2016;7:e01872.
  7. Gao Y, Yan L, Huang Y, Liu F, Zhao Y, Cao L, Wang T, Sun Q, Ming Z, Zhang L, Ge J, Zheng L, Zhang Y, Wang H, Zhu Y, Zhu C, Hu T, Hua T, Zhang B, Yang X, Li J, Yang H, Liu Z, Xu W, Guddat LW, Wang Q, Lou Z, Rao Z. Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science 2020;368:779-782.
  8. Zhu W, Chen CZ, Gorshkov K, Xu M, Lo DC, Zheng W. RNA-Dependent RNA Polymerase as a Target for COVID-19 Drug Discovery. SLAS Discov 2020;25:1141-1151.
  9. Gorbalenya AE, Pringle FM, Zeddam JL, Luke BT, Cameron CE, Kalmakoff J, Hanzlik TN, Gordon KH, Ward VK. The palm subdomain-based active site is internally permuted in viral RNA-dependent RNA polymerases of an ancient lineage. J Mol Biol 2002;324:47-62.
  10. Singh P, Tripathi MK, Yasir M, Khare R, Shrivastava R. In silico identification of promising inhibitor against RNA-dependent RNA polymerase target of SARS-CoV-2. Mol Biol Res Commun 2021;10:131-140.
  11. Gordon D, Jang MG, Bouhaddou M, Xu J, Obernier K, O'Meara MJ, Guo JZ, Swaney DL, Tummino TA, Huettenhain R, Kaake RM, Richards AL, Tutuncuoglu B, Foussard H, Batra J, Haas K, Modak M, Kim M, Haas P, Polacco BJ, Braberg H, Fabius JM, Eckardt M, Soucheray M, Bennet MJ, Cakir M, McGregor MJ, Li Q, Naing ZZC, Zhou Y, Peng S, Kirby IT, Melnyk JE, Chorba JS, Lou K, Dai SA, Shen W, Shi Y, Zhang Z, Hernandez IB, Memon D, Armenta CH, Mathy CJP, Perica T, Pilla KB, Ganesan SJ, Saltzberg DJ, Ramachandran R, Lin Y, Wankowicz SA, Bohn M, Sharp PP, Trenker R, Young JM, Cavero DA, Hiatt J, Roth TL, Rathore U, Subramanian A, Noack J, Hubert M, Roesch F, Vallet T, Meyer B, White KM, Miorin L, Rosenberg OS, Verba KA, Agard D, Ott M, Emerman M, Ruggero D, Sastre AG, Jura N, Zastrow MV, Taunton J, Ashworth A, Schwartz O, Vignuzzi M, d'Enfert C, Mukherjee S, Jacobson M, Malik HS, Fujimori DG, Ideker T, Craik CS, Floor S, Fraser JS, Gross J, Sali A, Kortemme T, Beltrao P, Shokat K, Shoichet BK, Krogan NJ. A SARS-CoV-2-human protein-protein interaction map reveals drug targets and potential drug-repurposing. BioRXiv 2020.
  12. Bailey TL, Williams N, Misleh C, Li WW. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 2006;34:W369-W373.
  13. Gasteiger E, Hoogland C, Gattiker A, Duvaud S'E, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. The Proteomics Protocols Handbook: Springer 2005;571-607.
  14. Zhu W, Chen CZ, Gorshkov K, Xu M, Lo DC, Zheng W. RNA-dependent rNA polymerase as a target for COVID-19 drug discovery. SLAS Discov 2020;25:1141-1151.
  15. Ghaffari M, Behbahani M, Mohabatkar H. In silico comparison of Iranian HIV-1 envelop glycoprotein with five nearby countries. Mol Biol Res Commun 2016;5:114-121.
  16. Thakur CJ, Saini S, Notra A, Chauhan B, Arya S, Gupta R, Thakur J, Kumar V. Deciphering the functional role of hypothetical proteins from Chloroflexus aurantiacs J-10-f1 using bioinformatics approach. Mol Biol Res Commun 2020;9:129-139.
  17. Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005;33:W306-W310.
  18. Yadegari F, Majidzadeh K. In silico analysis for determining the deleterious nonsynonymous single nucleotide polymorphisms of BRCA genes. Mol Biol Res Commun 2019;8:141-150.
  19. Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res 2012;40:W452-W457.
  20. Shivani N, Smiline-Girija AS, Paramasivam A, Vijayashree-Priyadharsini J. Computational approach towards identification of pathogenic missense mutations in AMELX gene and their possible association with amelogenesis imperfecta. Mol Biol Res Commun 2020;9:63-69.
  21. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812-3814.
  22. Yadegari F, Farahmand L, Esmaeili R, Samadi T, Majidzadeh K. Functional investigation of the BRCA1 Val1714Gly and Asp1733Gly variants by computational tools and yeast transcription activation assay. Mol Biol Res Commun 2019;8:113-118.
  23. Lin B, Qing X, Liao J, Zhuo K. Role of protein glycosylation in host-pathogen interaction. Cells 2020;9:1022.
  24. Bhat AH, Maity S, Giri K, Ambatipudi K. Protein glycosylation: Sweet or bitter for bacterial pathogens? Crit Rev Microbiol 2019;45:82-102.
  25. Hamby SE, Hirst JD. Prediction of glycosylation sites using random forests. BMC Bioinformatics 2008;9:500.
  26. Guruprasad K, Reddy BB, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng 1990;4:155-161.
  27. Gorbalenya AE, Pringle FM, Zeddam JL, Luke BT, Cameron CE, Kalmakoff J, Hanzlik TN, Gordon KHJ, Ward VK. The palm subdomain-based active site is internally permuted in viral rNA-dependent rNA polymerases of an ancient lineage. J Mol Biol 2002;324:47-62.
  28. Hasan S. Analysis of COVID-19 M protein for possible clues regarding virion stability, longevity and spreading 2020. https://doi.org/10.31219/osf.io/e7jkc.
  29. Sal-Man N, Shai Y. Arginine mutations within a transmembrane domain of Tar, an Escherichia coli aspartate receptor, can drive homodimer dissociation and heterodimer association in vivo. Biochem J 2005;385:29-36.
  30. Shoichet BK, Baase WA, Kuroki R, Matthews BW. A relationship between protein stability and protein function. Proc Natl Acad Sci USA 1995;92:452-456.
  31. Matthews BW. Structural and genetic analysis of protein stability. Annu Rev Biochem 1993;62:139-160.
  32. Vigerust DJ, Shepherd VL. Virus glycosylation: role in virulence and immune interactions. Trends Microbiol 2007;15:211-218.
آمار
تعداد مشاهده مقاله: 2,165
تعداد دریافت فایل اصل مقاله: 2,378


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب