In silico functional and structural characterization revealed virulent proteins of Francisella tularensis strain SCHU4

Document Type : Original article

Authors

1 Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Sector 32 C, Chandigarh, India

2 Department of Biophysics, Panjab University, Sector 25, 160014, Chandigarh, India

Abstract

Francisella tularensis is a pathogenic, aerobic gram-negative coccobacillus bacterium. It is the causative agent of tularemia, a rare infectious disease that can attack skin, lungs, eyes, and lymph nodes. The genome of F. tularensis has been sequenced, and ~16% of the proteome is still uncharacterized. Characterizations of these proteins are essential to find new drug targets for better therapeutics. In silico characterization of proteins has become an extremely important approach to determine the functionality of proteins as experimental functional elucidation is unable to keep pace with the current growth of the sequence database. Initially, we have annotated 577 Hypothetical Proteins (HPs) of F. tularensis strain SCHU4 with seven bioinformatics tools which characterized them based on the family, domain and motif. Out of 577 HPs, 119 HPs were annotated by five or more tools and are further screened to predict their virulence properties, subcellular localization, transmembrane helices as well as physicochemical parameters. VirulentPred predicted 66 HPs out of 119 as virulent. These virulent proteins were annotated to find the interacting partner using STRING, and proteins with high confidence interaction scores were used to predict their 3D structures using Phyre2. The three virulent proteins Q5NH99 (phosphoserine phosphatase), Q5NG42 (Cystathionine beta-synthase) and Q5NG83 (Rrf2-type helix turn helix domain) were predicted to involve in modulation of cytoskeletal and innate immunity of host, H2S (hydrogen sulfide) based antibiotic tolerance and nitrite and iron metabolism of bacteria. The above predicted virulent proteins can serve as novel drug targets in the era of antibiotic resistance.

Keywords

References

  1. Hirschmann JV. From squirrels to biological weapons: The early history of Tularemia. Am J Med Sci 2018;356:319-328.
  2. Dennis DT, Inglesby TV, Henderson DA, Bartlett JG, Ascher MS, Eitzen E, Fine AD, Friedlander AM, Hauer J, Layton M, Lillibridge SR, McDade JE, Osterholm MT, O'Toole T, Parker G, Perl TM, Russell PK, Tonat K. Working Group on Civilian Biodefense. Tularemia as a biological weapon: medical and public health management. JAMA 2001; 285:2763-2773.
  3. Sjöstedt A. Tularemia: history, epidemiology, pathogen physiology, and clinical manifestations. Ann N Y Acad Sci 2007;1105:1-29.
  4. Sandström G, Löfgren S, Tärnvik A. A capsule-deficient mutant of Francisella tularensis LVS exhibits enhanced sensitivity to killing by serum but diminished sensitivity to killing by polymorphonuclear leukocytes. Infect Immun 1988;56:1194-1202.
  5. Okan NA, Chalabaev S, Kim TH, Fink A, Ross RA, Kasper DL. Kdo hydrolase is required for Francisella tularensis virulence and evasion of TLR2-mediated innate immunity. mBio 2013;4:e00638-12.
  6. Salomonsson EN, Forslund AL, Forsberg A. Type IV Pili in Francisella - A virulence trait in an intracellular pathogen. Front Microbiol 2011;2:29.
  7. Chomel BB, Morton JA, Kasten RW, Chang CC. First pediatric case of Tularemia after a Coyote Bite. Case Rep Infect Dis 2016;2016:8095138.
  8. Akimana C, Kwaik YA. Francisella-arthropod vector interaction and its role in patho-adaptation to infect mammals. Front Microbiol 2011;2:34.
  9. Gunnell MK, Adams BJ, Robison RA. The genetic diversity and evolution of Francisella tularensis with comments on detection by PCR. Curr Issues Mol Biol 2016;18:79-91
  10. McLendon MK, Apicella MA, Allen LA. Francisella tularensis: taxonomy, genetics, and Immunopathogenesis of a potential agent of biowarfare. Annu Rev Microbiol 2006;60:167-185.
  11. Larsson P, Oyston PC, Chain P, Chu MC, Duffield M, Fuxelius HH, Garcia E, Hälltorp G, Johansson D, Isherwood KE, Karp PD, Larsson E, Liu Y, Michell S, Prior J, Prior R, Malfatti S, Sjöstedt A, Svensson K, Thompson N, Vergez L, Wagg JK, Wren BW, Lindler LE, Andersson SG, Forsman M, Titball RW. The complete genome sequence of Francisella tularensis, the causative agent of tularemia. Nat Genet 2005;37:153-159.
  12. Lubec G, Afjehi-Sadat L, Yang JW, John JP. Searching for hypothetical proteins: theory and practice based upon original data and literature. Prog Neurobiol 2005;77:90-127.
  13. Bock JR, Gough DA. Predicting protein–protein interactions from primary structure Joel. Bioinformatics 2001;17:455-460.
  14. Doerks T, Von Mering C, Bork P. Functional clues for hypothetical proteins based on genomic context analysis in prokaryotes. Nucleic Acids Res 2004;32:6321-6326.
  15. Adams MA, Suits MDL, Zheng J, Jia Z. Piecing together the structure-function puzzle: Experiences in structure-based functional annotation of hypothetical proteins. Proteomics 2007;7:2920-2932.
  16. Nourani E, Khunjush F, Durmus S. Computational approaches for prediction of pathogen-host protein-protein interactions. Front Microbiol 2015;6:1-10.
  17. Windsor AJ, Mitchell-Olds T. Comparative genomics as a tool for gene discovery. Curr Opin Biotechnol 2006;17:161-167.
  18. Lee BY, Hefta SA, Brennan PJ. Characterization of the major membrane protein of virulent Mycobacterium tuberculosis. Infect Immun 1992;60:2066-2074.
  19. Telkar S, Kumar H, Mahmood R. Synteny approach of drug target prediction among unique hypothetical proteins of Streptococcus Gordonii causing infective endocarditis. Sci Technol Arts Res J 2014;2:34.
  20. Islam MS, Shahik SM, Sohel M, Patwary NI, Hasan MA. In silico structural and functional annotation of hypothetical proteins of Vibrio cholerae O139. Genomics Inform 2015;13:53-59.
  21. School K, Marklevitz J, Schram WK, Harris LK. Predictive characterization of hypothetical proteins in Staphylococcus aureus NCTC 8325. Bioinformation 2016;12:209-220.
  22. Shahbaaz M, Ahmad F, Imtaiyaz Hassan M. Structure-based functional annotation of putative conserved proteins having lyase activity from Haemophilus influenzae. 3 Biotech 2015;5;317-336.
  23. Gazi MA, Mahmud S, Fahim SM, Kibria MG, Palit P, Islam MR, Rashid H, Das S, Mahfuz M, Ahmeed T. Functional prediction of hypothetical proteins from Shigella flexneri and validation of the predicted models by using ROC curve analysis. Genomics Inform 2018;16:e26.
  24. da Costa WLO, Araújo CLA, Dias LM, Pereira LCS, Alves JTC, Araújo FA, Folador EL, Henriques I, Silva A, Folador ARC. Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance. PLoS One 2016;13:e0198965.
  25. Singh A, Singal B, Nath O, Singh IK. Functional annotation and classification of the hypothetical proteins of Neisseria meningitidis H44/76. AJBIO 2015;3:57-64.
  26. 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.
  27. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, Yeh LS. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004;32:D115-D119.
  28. Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, Gwadz M, Hurwitz DI, Marchler GH, Song JS, Thanki N, Yamashita RA, Yang M, Zhang D, Zheng C, Lanczycki CJ, Marchler-Bauer A. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res 2020;48:D265-D268.
  29. Schultz J, Copley RR, Doerks T, Ponting CP, Bork P. SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Res 2000;28:231-234.
  30. Orengo CA, Pearl FM, Bray JE, Todd AE, Martin AC, Lo Conte L, Thornton JM. The CATH Database provides insights into protein structure/function relationships. Nucleic Acids Res 1999;27:275-279.
  31. Hulo N, Bairoch A, Bulliard V, Cerutti L, De Castro E, Langendijk-Genevaux PS, Pagni M, Sigrist CJ. The PROSITE database. Nucleic Acids Res 2006;34:D227-D230.
  32. Bateman A, Birney E, Durbin R, Eddy SR, Finn RD, Sonnhammer ELL. Pfam 3.1: 1313 multiple alignments and profile HMMs match the majority of proteins. Nucleic Acids Res 1999;27:260-262.
  33. Apweiler R, Attwood TK, Bairoch A, Bateman A, Birney E, Biswas M, Bucher P, Cerutti L, Corpet F, Croning MD, Durbin R, Falquet L, Fleischmann W, Gouzy J, Hermjakob H, Hulo N, Jonassen I, Kahn D, Kanapin A, Karavidopoulou Y, Lopez R, Marx B, Mulder NJ, Oinn TM, Pagni M, Servant F, Sigrist CJ, Zdobnov EM. InterPro--an integrated documentation resource for protein families, domains and functional sites. Bioinformatics 2000;16:1145-1150.
  34. Li YH, Xu JY, Tao L, Li XF, Li S, Zeng X, Chen SY, Zhang P, Qin C, Zhang C, Chen Z, Zhu F, Chen YZ. SVM-Prot 2016: A web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS One 2016;11:e0155290.
  35. Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at GenomeNet. Nucleic Acids Res 2002;30:42-46.
  36. 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.
  37. Sasson O, Vaaknin A, Fleischer H, Portugaly E, Bilu Y, Linial N, Linial M. ProtoNet: hierarchical classification of the protein space. Nucleic Acids Res 2003;31:348-352.
  38. Garg VK, Avashthi H, Tiwari A, Jain PA, Ramkete PW, Kayastha AM, Singh VK. MFPPI – Multi FASTA ProtParam Interface. Bioinformation 2016;12:74-77.
  39. Yu CS, Cheng CW, Su WC, Chang KC, Huang SW, Hwang JK, Lu CH. CELLO2GO: a web server for protein subcellular Localization prediction with functional gene ontology annotation. PLoS One 2014;9:e99368.
  40. Bhasin M, Garg A, Raghava GPS. PSLpred: Prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21:2522-2524.
  41. Rey S, Acab M, Gardy JL, Laird MR, deFays K, Lambert C, Brinkman FS. PSORTdb: a protein subcellular localization database for bacteria. Nucleic Acids Res 2005;33:D164-D168.
  42. Chen Y, Yu P, Luo J, Jiang Y. Secreted protein prediction system combining CJ-SPHMM, TMHMM, and PSORT. Mamm Genome 2003;14:859-865.
  43. Tusnady GE, Simon I. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol 1998;283:489-506.
  44. Bendtsen JD, Nielsen H, Von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 2004;340:783-795.
  45. Garg A, Gupta D. VirulentPred: A SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 2008;9:1-12.
  46. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017;45:D362-D368.
  47. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 2015;10:845-858.
  48. Kelley LA, Sternberg MJ. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 2009;4:363-371.
  49. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 1993;26:283-291.
  50. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. Protein Identification and Analysis Tools on the ExPASy Server. In: John M. Walker. The Proteomics Protocols Handbook. Totowa : Humana Press 2005;571-607.
  51. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, Hochstrasser DF. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol 1999;112:531-552.
  52. Behbahani M, Rabiei P, Mohabatkar H. Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2. Mol Biol Res Commun 2021;10:171-178.
  53. 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.
  54. Nielsen H, Engelbrecht J, Brunak S, von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng 1997;10:1-6
  55. Lumangtad LA, Bell TW. The signal peptide as a new target for drug design. Bioorg Med Chem Lett 2020;30:127115.
  56. Chitty JL, Tatzenko TL, Williams SJ, Koh YQ, Corfield EC, Butler MS, Robertson AA, Cooper MA, Kappler U, Kobe B, Fraser JA. GMP Synthase Is Required for Virulence Factor Production and Infection by Cryptococcus neoformans. J Biol Chem 2017;292:3049-3059.
  57. Garmory HS, Titball RW. ATP-binding cassette transporters are targets for the development of antibacterial vaccines and therapies. Infect Immun 2004;72;6757-6763.
  58. Pareek CS, Smoczynski R, Tretyn A. Sequencing technologies and genome sequencing. J Appl Genet 2011;52:413-435.
  59. Hawkins T, Kihara D. Function prediction of uncharacterized proteins. J Bioinform Comput Biol 2007;5:1-30.
  60. Arora G, Tiwari P, Mandal RS, Gupta A, Sharma D, Saha S, Singh R. High throughput screen identifies small molecule inhibitors specific for Mycobacterium tuberculosis phosphoserine phosphatase. J Biol Chem 2014;289:25149-25165.
  61. Moffatt CE, Inaba H, Hirano T, Lamont RJ. Porphyromonas gingivalis SerB-mediated dephosphorylation of host cell cofilin modulates invasion efficiency. Cell Microbiol 2012;14:577-588.
  62. Tribble GD, Mao S, James CE, Lamont RJ. A Porphyromonas gingivalis haloacid dehalogenase family phosphatase interacts with human phosphoproteins and is important for invasion. Proc Natl Acad Sci U S A 2006;103:11027-11032.
  63. Zhu H, Blake S, Chan KT, Pearson RB, Kang J. Cystathionine β-synthase in physiology and cancer. Biomed Res Int 2018;28:3205125.
  64. Kimura H. Production and physiological effects of hydrogen sulfide. Antioxid Redox Signal 2014;20;783-793.
  65. Ng SY, Ong KX, Surendran ST, Sinha A, Lai JJH, Chen J, Liang J, Tay LKS, Cui L, Loo HL, Ho P, Han J, Moreira W. Hydrogen sulfide sensitizes acinetobacter baumannii to killing by antibiotics. Front Microbiol 2020;11:1875.
  66. Shukla P, Khodade VS, SharathChandra M, Chauhan P, Mishra S, Siddaramappa S, Pradeep BE, Singh A, Chakrapani H. "On demand" redox buffering by H2S contributes to antibiotic resistance revealed by a bacteria-specific H2S donor. Chem Sci 2017;8:4967-4972.
  67. Shatalin K, Shatalina E, Mironov A, Nudler E. H2S: a universal defense against antibiotics in bacteria. Science 2011;334:986-990.
  68. Bodenmiller DM, Spiro S. The yjeB (nsrR) gene of Escherichia coli encodes a nitric oxide-sensitive transcriptional regulator. J Bacteriol 2006;188:874-881.
  69. Keon RG, Fu R, Voordouw G. Deletion of two downstream genes alters expression of the hmc operon of Desulfovibrio vulgaris subsp. vulgaris Hildenborough. Arch Microbiol 1997;167:376-383.
  70. Kumar R, Broms JE, Sjostedt A. Exploring the diversity within the genus Francisella-An integrated Pan-Genome and Genome-Mining approach. Front Microbiol 2020;11:1928.

Files

Share

How to cite

Statistics