Identification of Peptidoglycan Glycosyltransferase FtsI as a Potential Drug Target against Salmonella Enteritidis and Salmonella Typhimurium Serovars Through Subtractive Genomics, Molecular Docking and Molecular Dynamics Simulation Approaches


Cite item

Full Text

Abstract

Introduction:Salmonella enterica serovar Enteritidis and Salmonella enterica serovar Typhimurium are among the main causative agents of nontyphoidal Salmonella infections, imposing a significant global health burden. The emergence of antibiotic resistance in these pathogens underscores the need for innovative therapeutic strategies.

Objective:To identify proteins as potential drug targets against Salmonella enteritidis and Salmonella typhimurium serovars using in silico approaches.

Methods:In this study, a subtractive genomics approach was employed to identify potential drug targets. The whole proteome of Salmonella enteritidis PT4 and Salmonella typhimurium (D23580), containing 393 and 478 proteins, respectively, was analyzed through subtractive genomics to identify human homologous proteins of the pathogen and also the proteins linked to shared metabolic pathways of pathogen and its host.

Results:Subsequent analysis revealed 19 common essential proteins shared by both strains. To ensure hostspecificity, we identified 10 non-homologous proteins absent in humans. Among these proteins, peptidoglycan glycosyltransferase FtsI was pivotal, participating in pathogen-specific pathways and making it a promising drug target. Molecular docking highlighted two potential compounds, Balsamenonon A and 3,3',4',7-Tetrahydroxyflavylium, with strong binding affinities with FtsI. A 100 ns molecular dynamics simulation having 10,000 frames substantiated the strong binding affinity and demonstrated the enduring stability of the predicted compounds at the docked site.

Conclusion:The findings in this study provide the foundation for drug development strategies against Salmonella infections, which can contribute to the prospective development of natural and cost-effective drugs targeting Salmonella Enteritidis and Salmonella Typhimurium.

About the authors

Imran Gulzar

Bioinformatics and Biotechnology, Government College University, Faisalabad

Email: info@benthamscience.net

Asma Khalil

Bioinformatics and Biotechnology, Government College University, Faisalabad

Email: info@benthamscience.net

Usman Ali Ashfaq

Bioinformatics and Biotechnology, Government College University, Faisalabad

Email: info@benthamscience.net

Sadia Liaquat

Bioinformatics and Biotechnology, Government College University, Faisalabad

Email: info@benthamscience.net

Asma Haque

Bioinformatics and Biotechnology, Government College University, Faisalabad

Author for correspondence.
Email: info@benthamscience.net

References

  1. Fookes M, Schroeder GN, Langridge GC, et al. Salmonella bongori provides insights into the evolution of the Salmonellae. PLoS Pathog 2011; 7(8): e1002191. doi: 10.1371/journal.ppat.1002191 PMID: 21876672
  2. Oludairo OO, Kwaga JK, Kabir J, et al. A review on Salmonella characteristics, taxonomy, nomenclature with special reference to non-typhoidal and typhoidal salmonellosis. Zagazig Vet J 2022; 50: 161-76.
  3. Park E. The genomic epidemiology of typhoidal and invasive nontyphoidal Salmonella in sub-Saharan Africa. University of Oxford 2019.
  4. Saleh Mohammed Jajere SMJ. A review of Salmonella enterica with particular focus on the pathogenicity and virulence factors, host specificity and antimicrobial resistance including multidrug resistance. Vet World 2019; 12(4): 504-21.
  5. Wilson M, Wilson PJ, Wilson M. Gastroenteritis due to Salmonella. Close Encounters of the Microbial Kind. Researchgate 2021; pp. 451-61.
  6. Raspoet R. Survival strategies of Salmonella enteritidis to cope with antibacterial factors in the chicken oviduct and in egg white. Ghent University 2014.
  7. Muthumbi E. Understanding the carriage and transmission of non- typhoidal Salmonella infections in Kenya. London School of Hygiene & Tropical Medicine 2024.
  8. Canals R, Chaudhuri RR, Steiner RE, et al. The fitness landscape of the African Salmonella typhimurium ST313 strain D23580 reveals unique properties of the pBT1 plasmid. PLoS Pathog 2019; 15(9): e1007948. doi: 10.1371/journal.ppat.1007948 PMID: 31560731
  9. Wang X, Biswas S, Paudyal N, et al. Antibiotic resistance in Salmonella typhimurium isolates recovered from the food chain through national antimicrobial resistance monitoring system between 1996 and 2016. Front Microbiol 2019; 10: 985. doi: 10.3389/fmicb.2019.00985 PMID: 31134024
  10. Hur J, Kim JH, Park JH, Lee YJ, Lee JH. Molecular and virulence characteristics of multi-drug resistant Salmonella enteritidis strains isolated from poultry. Vet J 2011; 189(3): 306-11. doi: 10.1016/j.tvjl.2010.07.017 PMID: 20822940
  11. Kumari H, Kumar K, Kumar G. Acute gastroenteritis: Its causes, maintenance, and treatment. J Pharm Negat Results 2022; 13(8): 5064-78.
  12. Shahid F, Shehroz M, Zaheer T, Ali AJFA-IDD. Subtractive genomics approaches: Towards anti-bacterial drug discovery. Front Anti-Infect Drug Discov 2020; 8(15): 144-58.
  13. Naorem RS, Pangabam BD, Bora SS, et al. Identification of putative vaccine and drug targets against the methicillin-resistant Staphylococcus aureus by reverse vaccinology and subtractive genomics approaches. Molecules 2022; 27(7): 2083. doi: 10.3390/molecules27072083 PMID: 35408485
  14. Khan K, Alhar MSO, Abbas MN, et al. Integrated bioinformatics-based subtractive genomics approach to decipher the therapeutic drug target and its possible intervention against brucellosis. Bioengineering 2022; 9(11): 633. doi: 10.3390/bioengineering9110633 PMID: 36354544
  15. Kumar A, Thotakura PL, Tiwary BK. Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions. 2016; 16: 1-12.
  16. Rathi B, Sarangi AN, Trivedi N. Genome subtraction for novel target definition in Salmonella typhi. Bioinformation 2009; 4(4): 143-50. doi: 10.6026/97320630004143 PMID: 20198190
  17. UniProt: A hub for protein information. Nucleic Acids Res 2015; 43(Database issue): D204-12. PMID: 25348405
  18. Wen QF, Liu S, Dong C, Guo HX, Gao YZ, Guo FB. Geptop 2.0: An updated, more precise, and faster Geptop server for identification of prokaryotic essential genes. Front Microbiol 2019; 10: 1236. doi: 10.3389/fmicb.2019.01236 PMID: 31214154
  19. Zhang C, Zheng W, Freddolino PL, Zhang Y. MetaGO: Predicting gene ontology of non-homologous proteins through low-resolution protein structure prediction and protein–protein network mapping. J Mol Biol 2018; 430(15): 2256-65. doi: 10.1016/j.jmb.2018.03.004 PMID: 29534977
  20. Du J, Yuan Z, Ma Z, Song J, Xie X, Chen Y. KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model. Mol Biosyst 2014; 10(9): 2441-7. doi: 10.1039/C4MB00287C PMID: 24994036
  21. Gardy JL, Laird MR, Chen F, et al. PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 2005; 21(5): 617-23. doi: 10.1093/bioinformatics/bti057 PMID: 15501914
  22. Yu NY, Wagner JR, Laird MR, et al. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010; 26(13): 1608-15. doi: 10.1093/bioinformatics/btq249 PMID: 20472543
  23. Bhasin M, Garg A, Raghava GPS. PSLpred: Prediction of subcellular localization of bacterial proteins. Bioinformatics 2005; 21(10): 2522-4. doi: 10.1093/bioinformatics/bti309 PMID: 15699023
  24. Yin R, Feng BY, Varshney A, Pierce BG. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. Protein Sci 2022; 31(8): e4379. doi: 10.1002/pro.4379 PMID: 35900023
  25. Selvam K, Senbagam D, Selvankumar T, Sudhakar C, Kamala-Kannan S, Senthilkumar B. Cellulase enzyme: Homology modeling, binding site identification and molecular docking. J Mol Struct 2017; 1150: 61-7.
  26. 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(Web Server): W407-10. doi: 10.1093/nar/gkm290 PMID: 17517781
  27. Wallner B, Elofsson A. Prediction of global and local model quality in CASP7 using Pcons and ProQ. Proteins 2007; 69(S8) (Suppl. 8): 184-93. doi: 10.1002/prot.21774 PMID: 17894353
  28. Li YY, An J, Jones SJM. A computational approach to finding novel targets for existing drugs. PLOS Comput Biol 2011; 7(9): e1002139. doi: 10.1371/journal.pcbi.1002139 PMID: 21909252
  29. Ferreira L, Dos Santos R, Oliva G, Andricopulo A. Molecular docking and structure-based drug design strategies. Molecules 2015; 20(7): 13384-421. doi: 10.3390/molecules200713384 PMID: 26205061
  30. Dallakyan S. Small-molecule library screening by docking with PyRx. Methods Mol Biol 2015; 1263: 243-50.
  31. Stylianakis I, Zervos N, Lii JH, Pantazis DA, Kolocouris A. Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory. J Comput Aided Mol Des 2023; 37(12): 607-56. doi: 10.1007/s10822-023-00513-5 PMID: 37597063
  32. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J. CASTp: Computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 2006; 34(Web Server issue): W116-8. PMID: 16844972
  33. Studio DJA. Discovery studio. Studio DJA 2008; p. 420.
  34. Pettersen EF, Goddard TD, Huang CC, et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci 2021; 30(1): 70-82. doi: 10.1002/pro.3943 PMID: 32881101
  35. Bergdorf M, Kim ET, Rendleman CA. Desmond/GPU Performance as of November 2014. D.E. Shaw Research 2014.
  36. Albaugh A, Boateng HA, Bradshaw RT, et al. Advanced potential energy surfaces for molecular simulation. J Phys Chem B 2016; 120(37): 9811-32. PMID: 27513316
  37. Bolhuis PG, Swenson DWJAT. Transition path sampling as Markov chain Monte Carlo of trajectories: Recent algorithms, software, applications, and future outlook. Adv Theory Simul 2021; (4)4: 2000237.
  38. da Fonseca AM, Caluaco BJ, Madureira JMC, et al. Screening of potential inhibitors targeting the main protease structure of SARS- CoV-2 via molecular docking, and approach with molecular dynamics, RMSD, RMSF, H-bond, SASA and MMGBSA. 2023; 1-15. PMID: 37490200
  39. Bojkova D, Klann K, Koch B, et al. Proteomics of SARS-CoV-2- infected host cells reveals therapy targets. Nature 2020; 583(7816): 469-72. doi: 10.1038/s41586-020-2332-7 PMID: 32408336
  40. Luisa BG. Cellular energy metabolism and its regulation. Elsevier 2012.
  41. Weiss DS, Chen JC, Ghigo JM, Boyd D, Beckwith J. Localization of FtsI (PBP3) to the septal ring requires its membrane anchor, the Z ring, FtsA, FtsQ, and FtsL. J Bacteriol 1999; 181(2): 508-20. doi: 10.1128/JB.181.2.508-520.1999 PMID: 9882665
  42. Di Guilmi A, Dessen A, Dideberg O, Vernet T. Bifunctional penicillin-binding proteins: Focus on the glycosyltransferase domain and its specific inhibitor moenomycin. Curr Pharm Biotechnol 2002; 3(2): 63-75. doi: 10.2174/1389201023378436 PMID: 12022260
  43. Culp E, Wright GD. Bacterial proteases, untapped antimicrobial drug targets. J Antibiot 2017; 70(4): 366-77. doi: 10.1038/ja.2016.138 PMID: 27899793
  44. Agarwal S. An overview of molecular docking. JSM Chem 2016; 4: 1024-8.
  45. Foloppe N, Hubbard R. Towards predictive ligand design with free-energy based computational methods? Curr Med Chem 2006; 13(29): 3583-608. doi: 10.2174/092986706779026165 PMID: 17168725
  46. Eng S-K, Pusparajah P, Ab Mutalib N-S, Ser H-L, Chan K-G. Salmonella: A review on pathogenesis, epidemiology and antibiotic resistance. Front Life Sci 2015; 8(3): 284-93.
  47. Kamboj S, Gupta N, Bandral JD, Gandotra G. Food safety and hygiene: A review. Int J Chem Stud 2020; 8: 358-68.
  48. Solana J, Garrote-Sánchez E. DELEAT: Gene essentiality prediction and deletion design for bacterial genome reduction. 2021; 22: 1-17.
  49. Muzzi A, Masignani V, Rappuoli R. The pan-genome: Towards a knowledge-based discovery of novel targets for vaccines and antibacterials. Drug Discov Today 2007; 12(11-12): 429-39. doi: 10.1016/j.drudis.2007.04.008 PMID: 17532526
  50. Aalberse RC, Akkerdaas J, Van Ree R. Cross-reactivity of IgE antibodies to allergens. Allergy 2001; 56(6): 478-90. doi: 10.1034/j.1398-9995.2001.056006478.x PMID: 11421891
  51. Ameji PJ, Uzairu A, Shallangwa GA, Uba S. Molecular docking-based virtual screening, drug-likeness, and pharmacokinetic profiling of some anti-Salmonella typhimurium cephalosporin derivatives. J Taibah Univ Med Sci 2023; 18(6): 1417-31. doi: 10.1016/j.jtumed.2023.05.021 PMID: 38162870
  52. Putra MY. In silico studies of drug discovery and design against COVID-19 focusing on ACE2 and spike protein virus receptors: A systematic review. Sci Pharm 2023; 2(3): 171-83.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers