Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Computational approaches for molecular characterization and structure-based functional elucidation of a hypothetical protein from Mycobacterium tuberculosis

  • Abu Saim Mohammad, Saikat (Department of Biochemistry and Molecular Biology, Life Science Faculty, Bangabandhu Sheikh Mujibur Rahman Science and Technology University)
  • Received : 2023.01.04
  • Accepted : 2023.05.04
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

Keywords

References

  1. Aitken ML, Limaye A, Pottinger P, Whimbey E, Goss CH, Tonelli MR, et al. Respiratory outbreak of Mycobacterium abscessus subspecies massiliense in a lung transplant and cystic fibrosis center. Am J Respir Crit Care Med 2012;185:231-232. https://doi.org/10.1164/ajrccm.185.2.231
  2. Pawlik A, Garnier G, Orgeur M, Tong P, Lohan A, Le Chevalier F, et al. Identification and characterization of the genetic changes responsible for the characteristic smooth-to-rough morphotype alterations of clinically persistent Mycobacterium abscessus. Mol Microbiol 2013;90:612-629. https://doi.org/10.1111/mmi.12387
  3. Bryant JM, Grogono DM, Greaves D, Foweraker J, Roddick I, Inns T, et al. Whole-genome sequencing to identify transmission of Mycobacterium abscessus between patients with cystic fibrosis: a retrospective cohort study. Lancet 2013;381:1551-1560. https://doi.org/10.1016/S0140-6736(13)60632-7
  4. Gordon SV, Bottai D, Simeone R, Stinear TP, Brosch R. Pathogenicity in the tubercle bacillus: molecular and evolutionary determinants. Bioessays 2009;31:378-388. https://doi.org/10.1002/bies.200800191
  5. Springer B, Stockman L, Teschner K, Roberts GD, Bottger EC. Two-laboratory collaborative study on identification of mycobacteria: molecular versus phenotypic methods. J Clin Microbiol 1996;34:296-303. https://doi.org/10.1128/jcm.34.2.296-303.1996
  6. Stinear TP, Seemann T, Harrison PF, Jenkin GA, Davies JK, Johnson PD, et al. Insights from the complete genome sequence of Mycobacterium marinum on the evolution of Mycobacterium tuberculosis. Genome Res 2008;18:729-741.  https://doi.org/10.1101/gr.075069.107
  7. Kaur D, Guerin ME, Skovierova H, Brennan PJ, Jackson M. Chapter 2: Biogenesis of the cell wall and other glycoconjugates of Mycobacterium tuberculosis. Adv Appl Microbiol 2009;69:23-78. https://doi.org/10.1016/S0065-2164(09)69002-X
  8. Smith I. Mycobacterium tuberculosis pathogenesis and molecular determinants of virulence. Clin Microbiol Rev 2003;16:463-496. https://doi.org/10.1128/CMR.16.3.463-496.2003
  9. Shang S, Gibbs S, Henao-Tamayo M, Shanley CA, McDonnell G, Duarte RS, et al. Increased virulence of an epidemic strain of Mycobacterium massiliense in mice. PLoS One 2011;6:e24726.
  10. Koch A, Mizrahi V. Mycobacterium tuberculosis. Trends Microbiol 2018;26:555-556. https://doi.org/10.1016/j.tim.2018.02.012
  11. Gutierrez MC, Brisse S, Brosch R, Fabre M, Omais B, Marmiesse M, et al. Ancient origin and gene mosaicism of the progenitor of Mycobacterium tuberculosis. PLoS Pathog 2005;1:e5.
  12. Sambou T, Dinadayala P, Stadthagen G, Barilone N, Bordat Y, Constant P, et al. Capsular glucan and intracellular glycogen of Mycobacterium tuberculosis: biosynthesis and impact on the persistence in mice. Mol Microbiol 2008;70:762-774. https://doi.org/10.1111/j.1365-2958.2008.06445.x
  13. Sani M, Houben EN, Geurtsen J, Pierson J, de Punder K, van Zon M, et al. Direct visualization by cryo-EM of the mycobacterial capsular layer: a labile structure containing ESX-1-secreted proteins. PLoS Pathog 2010;6:e1000794.
  14. Dhanuka R, Singh JP. Protein function prediction using functional inter-relationship. Comput Biol Chem 2021;95:107593.
  15. Gabaldon T, Huynen MA. Prediction of protein function and pathways in the genome era. Cell Mol Life Sci 2004;61:930-944. https://doi.org/10.1007/s00018-003-3387-y
  16. Rost B, Liu J, Nair R, Wrzeszczynski KO, Ofran Y. Automatic prediction of protein function. Cell Mol Life Sci 2003;60:2637-2650. https://doi.org/10.1007/s00018-003-3114-8
  17. dos Santos GC, dos Santos DM, Olivieri JR, Canduri F, Silva RG, et al. Docking and small angle X-ray scattering studies of purine nucleoside phosphorylase. Biochem Biophys Res Commun 2003;309:923-928. https://doi.org/10.1016/j.bbrc.2003.08.093
  18. Mills CL, Beuning PJ, Ondrechen MJ. Biochemical functional predictions for protein structures of unknown or uncertain function. Comput Struct Biotechnol J 2015;13:182-191. https://doi.org/10.1016/j.csbj.2015.02.003
  19. de Azevedo WF. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem 2011;18:1353-1366. https://doi.org/10.2174/092986711795029519
  20. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook (Walker JM, ed.). Totowa: Humana Press, 2005. pp. 571-607.
  21. Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 2004;13:1402-1406. https://doi.org/10.1110/ps.03479604
  22. Yu CS, Chen YC, Lu CH, Hwang JK. Prediction of protein subcellular localization. Proteins 2006;64:643-651. https://doi.org/10.1002/prot.21018
  23. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010;26:1608-1615. https://doi.org/10.1093/bioinformatics/btq249
  24. Tusnady GE, Simon I. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol 1998;283:489-506. https://doi.org/10.1006/jmbi.1998.2107
  25. Tusnady GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics 2001;17:849-850. https://doi.org/10.1093/bioinformatics/17.9.849
  26. Moller S, Croning MD, Apweiler R. Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 2001;17:646-653. https://doi.org/10.1093/bioinformatics/17.7.646
  27. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001;305:567-580. https://doi.org/10.1006/jmbi.2000.4315
  28. Marchler-Bauer A, Bo Y, Han L, He J, Lanczycki CJ, Lu S, et al. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures. Nucleic Acids Res 2017;45:D200-D203. https://doi.org/10.1093/nar/gkw1129
  29. Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at GenomeNet. Nucleic Acids Res 2002;30:42-46. https://doi.org/10.1093/nar/30.1.42
  30. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res 2014;42:D222-D230. https://doi.org/10.1093/nar/gkt1223
  31. Wilson D, Madera M, Vogel C, Chothia C, Gough J. The SUPERFAMILY database in 2007: families and functions. Nucleic Acids Res 2007;35:D308-D313. https://doi.org/10.1093/nar/gkl910
  32. Sigrist CJ, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, et al. New and continuing developments at PROSITE. Nucleic Acids Res 2013;41:D344-D347. https://doi.org/10.1093/nar/gks1067
  33. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607-D613. https://doi.org/10.1093/nar/gky1131
  34. Combet C, Blanchet C, Geourjon C, Deleage G. NPS@: network protein sequence analysis. Trends Biochem Sci 2000;25:147-150. https://doi.org/10.1016/S0968-0004(99)01540-6
  35. Jones DT, Cozzetto D. DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 2015;31:857-863. https://doi.org/10.1093/bioinformatics/btu744
  36. Buchan DW, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res 2019;47:W402-W407. https://doi.org/10.1093/nar/gkz297
  37. Bitencourt-Ferreira G, de Azevedo WF Jr. Homology modeling of protein targets with MODELLER. Methods Mol Biol 2019;2053:231-249. https://doi.org/10.1007/978-1-4939-9752-7_15
  38. Zimmermann L, Stephens A, Nam SZ, Rau D, Kubler J, Lozajic M, et al. A Completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol 2018;430:2237-2243. https://doi.org/10.1016/j.jmb.2017.12.007
  39. Biegert A, Mayer C, Remmert M, Soding J, Lupas AN. The MPI bioinformatics toolkit for protein sequence analysis. Nucleic Acids Res 2006;34:W335-W339. https://doi.org/10.1093/nar/gkl217
  40. Gabler F, Nam SZ, Till S, Mirdita M, Steinegger M, Soding J, et al. Protein sequence analysis using the MPI bioinformatics toolkit. Curr Protoc Bioinformatics 2020;72:e108.
  41. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-170. https://doi.org/10.1126/science.1853201
  42. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35:W407-W410. https://doi.org/10.1093/nar/gkm290
  43. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 2018;46:W363-W367. https://doi.org/10.1093/nar/gky473
  44. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007;8:4.
  45. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2: a server for in silico prediction of allergens. J Mol Model 2014;20:2278.
  46. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R; Open Source Drug Discovery Consortium, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One 2013;8:e73957.
  47. Zhou P. Determining protein half-lives. Methods Mol Biol 2004;284:67-77.
  48. Smialowski P, Martin-Galiano AJ, Mikolajka A, Girschick T, Holak TA, Frishman D. Protein solubility: sequence based prediction and experimental verification. Bioinformatics 2007;23:2536-2542. https://doi.org/10.1093/bioinformatics/btl623
  49. Restrepo-Montoya D, Vizcaino C, Nino LF, Ocampo M, Patarroyo ME, Patarroyo MA. Validating subcellular localization prediction tools with mycobacterial proteins. BMC Bioinformatics 2009;10:134.
  50. Saikat AS, Uddin ME, Ahmad T, Mahmud S, Imran MA, Ahmed S, et al. Structural and functional elucidation of IF-3 protein of Chloroflexus aurantiacus involved in protein biosynthesis: an in silico approach. Biomed Res Int 2021;2021:9050026.
  51. Schneider G, Fechner U. Advances in the prediction of protein targeting signals. Proteomics 2004;4:1571-1580. https://doi.org/10.1002/pmic.200300786
  52. Saikat AS. An in silico approach for potential natural compounds as inhibitors of protein CDK1/Cks2. Chem Proc 2021;8:5.
  53. Bagos PG, Liakopoulos TD, Hamodrakas SJ. Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method. BMC Bioinformatics 2005;6:7.
  54. Ren Z, Ren PX, Balusu R, Yang X. Transmembrane helices tilt, bend, slide, torque, and unwind between functional states of rhodopsin. Sci Rep 2016;6:34129.
  55. Wong WC, Maurer-Stroh S, Schneider G, Eisenhaber F. Transmembrane helix: simple or complex. Nucleic Acids Res 2012;40:W370-W375. https://doi.org/10.1093/nar/gks379
  56. Bianchi F, Textor J, van den Bogaart G. Transmembrane helices are an overlooked source of major histocompatibility complex class I epitopes. Front Immunol 2017;8:1118.
  57. Walther TH, Ulrich AS. Transmembrane helix assembly and the role of salt bridges. Curr Opin Struct Biol 2014;27:63-68. https://doi.org/10.1016/j.sbi.2014.05.003
  58. Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, et al. CDD: NCBI's conserved domain database. Nucleic Acids Res 2015;43:D222-D226. https://doi.org/10.1093/nar/gku1221
  59. Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res 2011;39:D225-D229. https://doi.org/10.1093/nar/gkq1189
  60. Marchler-Bauer A, Bryant SH. CD-Search: protein domain annotations on the fly. Nucleic Acids Res 2004;32:W327-W331. https://doi.org/10.1093/nar/gkh454
  61. Goulding CW, Apostol MI, Gleiter S, Parseghian A, Bardwell J, Gennaro M, et al. Gram-positive DsbE proteins function differently from Gram-negative DsbE homologs: a structure to function analysis of DsbE from Mycobacterium tuberculosis. J Biol Chem 2004;279:3516-3524.
  62. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer EL, et al. Pfam: the protein families database in 2021. Nucleic Acids Res 2021;49:D412-D419. https://doi.org/10.1093/nar/gkaa913
  63. Holmgren A. Thioredoxin. Annu Rev Biochem 1985;54:237-271. https://doi.org/10.1146/annurev.bi.54.070185.001321
  64. Gleason FK, Holmgren A. Thioredoxin and related proteins in procaryotes. FEMS Microbiol Rev 1988;4:271-297. https://doi.org/10.1111/j.1574-6968.1988.tb02747.x
  65. Holmgren A. Thioredoxin and glutaredoxin systems. J Biol Chem 1989;264:13963-13966. https://doi.org/10.1016/S0021-9258(18)71625-6
  66. Eklund H, Gleason FK, Holmgren A. Structural and functional relations among thioredoxins of different species. Proteins 1991;11:13-28. https://doi.org/10.1002/prot.340110103
  67. Zeller T, Klug G. Thioredoxins in bacteria: functions in oxidative stress response and regulation of thioredoxin genes. Naturwissenschaften 2006;93:259-266. https://doi.org/10.1007/s00114-006-0106-1
  68. Kim MK, Zhao L, Jeong S, Zhang J, Jung JH, Seo HS, et al. Structural and biochemical characterization of thioredoxin-2 from Deinococcus radiodurans. Antioxidants (Basel) 2021;10:1843.
  69. Marsh JA, Teichmann SA. Structure, dynamics, assembly, and evolution of protein complexes. Annu Rev Biochem 2015;84:551-575. https://doi.org/10.1146/annurev-biochem-060614-034142
  70. Sowmya G, Ranganathan S. Protein-protein interactions and prediction: a comprehensive overview. Protein Pept Lett 2014;21:779-789. https://doi.org/10.2174/09298665113209990056
  71. Xie L, Bourne PE. Functional coverage of the human genome by existing structures, structural genomics targets, and homology models. PLoS Comput Biol 2005;1:e31.
  72. Wang M, Herrmann CJ, Simonovic M, Szklarczyk D, von Mering C. Version 4.0 of PaxDb: protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics 2015;15:3163-3168.
  73. Chim N, Riley R, The J, Im S, Segelke B, Lekin T, et al. An extracellular disulfide bond forming protein (DsbF) from Mycobacterium tuberculosis: structural, biochemical, and gene expression analysis. J Mol Biol 2010;396:1211-1226. https://doi.org/10.1016/j.jmb.2009.12.060
  74. Premkumar L, Heras B, Duprez W, Walden P, Halili M, Kurth F, et al. Rv2969c, essential for optimal growth in Mycobacterium tuberculosis, is a DsbA-like enzyme that interacts with VKOR-derived peptides and has atypical features of DsbA-like disulfide oxidases. Acta Crystallogr D Biol Crystallogr 2013;69:1981-1994. https://doi.org/10.1107/S0907444913017800
  75. Nagai S, Wiker HG, Harboe M, Kinomoto M. Isolation and partial characterization of major protein antigens in the culture fluid of Mycobacterium tuberculosis. Infect Immun 1991;59:372-382. https://doi.org/10.1128/iai.59.1.372-382.1991
  76. Hahn MY, Raman S, Anaya M, Husson RN. The Mycobacterium tuberculosis extracytoplasmic-function sigma factor SigL regulates polyketide synthases and secreted or membrane proteins and is required for virulence. J Bacteriol 2005;187:7062-7071. https://doi.org/10.1128/JB.187.20.7062-7071.2005
  77. Chim N, Harmston CA, Guzman DJ, Goulding CW. Structural and biochemical characterization of the essential DsbA-like disulfide bond forming protein from Mycobacterium tuberculosis. BMC Struct Biol 2013;13:23.
  78. Hodge EA, Benhaim MA, Lee KK. Bridging protein structure, dynamics, and function using hydrogen/deuterium-exchange mass spectrometry. Protein Sci 2020;29:843-855. https://doi.org/10.1002/pro.3790
  79. Uversky VN. Protein intrinsic disorder and structure-function continuum. Prog Mol Biol Transl Sci 2019;166:1-17. https://doi.org/10.1016/bs.pmbts.2019.05.003
  80. Hu J, Han J, Li H, Zhang X, Liu LL, Chen F, et al. Human embryonic kidney 293 cells: a vehicle for biopharmaceutical manufacturing, structural biology, and electrophysiology. Cells Tissues Organs 2018;205:1-8. https://doi.org/10.1159/000485501
  81. Liu ZP, Wu LY, Wang Y, Zhang XS, Chen L. Bridging protein local structures and protein functions. Amino Acids 2008;35:627-650. https://doi.org/10.1007/s00726-008-0088-8
  82. Hegyi H, Gerstein M. The relationship between protein structure and function: a comprehensive survey with application to the yeast genome. J Mol Biol 1999;288:147-164. https://doi.org/10.1006/jmbi.1999.2661
  83. Li WG, Xu TL. Emerging approaches to probing ion channel structure and function. Neurosci Bull 2012;28:351-374. https://doi.org/10.1007/s12264-012-1248-0
  84. Lobb B, Doxey AC. Novel function discovery through sequence and structural data mining. Curr Opin Struct Biol 2016;38:53-61. https://doi.org/10.1016/j.sbi.2016.05.017
  85. Hameduh T, Haddad Y, Adam V, Heger Z. Homology modeling in the time of collective and artificial intelligence. Comput Struct Biotechnol J 2020;18:3494-3506. https://doi.org/10.1016/j.csbj.2020.11.007
  86. Saikat AS, Islam R, Mahmud S, Imran MA, Alam MS, Masud MH, et al. Structural and functional annotation of uncharacterized protein NCGM946K2_146 of Mycobacterium tuberculosis: an in-silico approach. Proceedings 2020;66:13.
  87. Alva V, Nam SZ, Soding J, Lupas AN. The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis. Nucleic Acids Res 2016;44:W410-W415. https://doi.org/10.1093/nar/gkw348
  88. Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83-85. https://doi.org/10.1038/356083a0
  89. Conklin L, Hviid A, Orenstein WA, Pollard AJ, Wharton M, Zuber P. Vaccine safety issues at the turn of the 21st century. BMJ Glob Health 2021;6(Suppl 2):e004898.
  90. Riese P, Schulze K, Ebensen T, Prochnow B, Guzman CA. Vaccine adjuvants: key tools for innovative vaccine design. Curr Top Med Chem 2013;13:2562-2580.  https://doi.org/10.2174/15680266113136660183
  91. De Groot AS, Moise L, Terry F, Gutierrez AH, Hindocha P, Richard G, et al. Better epitope discovery, precision immune engineering, and accelerated vaccine design using immunoinformatics tools. Front Immunol 2020;11:442.
  92. Liljeroos L, Malito E, Ferlenghi I, Bottomley MJ. Structural and computational biology in the design of immunogenic vaccine antigens. J Immunol Res 2015;2015:156241.
  93. He L, Zhu J. Computational tools for epitope vaccine design and evaluation. Curr Opin Virol 2015;11:103-112. https://doi.org/10.1016/j.coviro.2015.03.013
  94. Crowcroft NS, Klein NP. A framework for research on vaccine effectiveness. Vaccine 2018;36:7286-7293. https://doi.org/10.1016/j.vaccine.2018.04.016
  95. Vela Ramirez JE, Sharpe LA, Peppas NA. Current state and challenges in developing oral vaccines. Adv Drug Deliv Rev 2017;114:116-131. https://doi.org/10.1016/j.addr.2017.04.008
  96. Wallis J, Shenton DP, Carlisle RC. Novel approaches for the design, delivery and administration of vaccine technologies. Clin Exp Immunol 2019;196:189-204. https://doi.org/10.1111/cei.13287
  97. Wang J, Yu Y, Zhao Y, Zhang D, Li J. Evaluation and integration of existing methods for computational prediction of allergens. BMC Bioinformatics 2013;14 Suppl 4:S1.
  98. Dimitrov I, Flower DR, Doytchinova I. AllerTOP: a server for in silico prediction of allergens. BMC Bioinformatics 2013;14 Suppl 6:S4.
  99. Duan X, Zhang M, Chen F. Prediction and analysis of antimicrobial peptides from rapeseed protein using in silico approach. J Food Biochem 2021;45:e13598. 
  100. Chai TT, Koh JA, Wong CC, Sabri MZ, Wong FC. Computational screening for the anticancer potential of seed-derived antioxidant peptides: a cheminformatic approach. Molecules 2021;26:7396. 
  101. Gopinatth V, Mendez RL, Ballinger E, Kwon JY. Therapeutic potential of tuna backbone peptide and its analogs: an in vitro and in silico study. Molecules 2021;26:2064.