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Targeting multi-drug-resistant Acinetobacter baumannii: a structure-based approach to identify the promising lead candidates against glutamate racemase

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Abstract

Context

Acinetobacter baumannii, one of the critical ESKAPE pathogens, is a highly resilient, multi-drug-resistant, Gramnegative, rod-shaped, highly pathogenic bacteria. It is responsible for almost 1–2% of all hospital-borne infections in immunocompromised patients and causes community outbreaks. Because of its resilience and MDR characteristics, looking for new strategies to check the infections related to this pathogen becomes paramount. The enzymes involved in the peptidoglycan biosynthetic pathway are attractive and the most promising drug targets. They contribute to the formation of the bacterial envelope and help to maintain the rigidity and integrity of the cell. The MurI (glutamate racemase) is one of the crucial enzymes that aid in the formation of the pentapeptide responsible for the interlinkage of peptidoglycan chains. It converts l-glutamate to d-glutamate, which is required to synthesise the pentapeptide chain.

Methods

In this study, the MurI protein of A. baumannii (strain AYE) was modelled and subjected to high-throughput virtual screening against the enamine-HTSC library, taking UDP-MurNAc-Ala binding site as the targeted site. Four ligand molecules, Z1156941329 (N-(1-methyl-2-oxo-3,4-dihydroquinolin-6-yl)-1-phenyl-3,4-dihydro-1H-isoquinoline-2-carboxamide), Z1726360919 (1-[2-[3-(benzimidazol-1-ylmethyl)piperidin-1-yl]-2-oxo-1-phenylethyl]piperidin-2-one), Z1920314754 (N-[[3-(3-methylphenyl)phenyl]methyl]-8-oxo-2,7-diazaspiro[4.4]nonane-2-carboxamide) and Z3240755352 (4R)-4-(2,5-difluorophenyl)-1-(4-fluorophenyl)-1,3a,4,5,7,7a-hexahydro-6H-pyrazolo[3,4-b]pyridin-6-one), were identified to be the lead candidates based on Lipinski’s rule of five, toxicity, ADME properties, estimated binding affinity and intermolecular interactions. The complexes of these ligands with the protein molecule were then subjected to MD simulations to scrutinise their dynamic behaviour, structural stability and effects on protein dynamics. The molecular mechanics/Poisson–Boltzmann surface area–based binding free energy analysis was also performed to compute the binding free energy of protein-ligand complexes, which offered the following values −23.32 ± 3.04 kcal/mol, −20.67 ± 2.91kcal/mol, −8.93 ± 2.90 kcal/mol and −26.73 ± 2.95 kcal/mol for MurI-Z1726360919, MurI-Z1156941329, MurI-Z3240755352 and MurI-Z3240755354 complexes respectively. Together, the results from various computational analyses utilised in this study proposed that Z1726360919, Z1920314754 and Z3240755352 could act as potential lead molecules to suppress the function of MurI protein from Acinetobacter baumannii.

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Data availability

Data and materials are available on request from the authors.

Abbreviations

ADME:

Absorption, Distribution, Metabolism and Excretion

DCCM:

Dynamical cross-correlation matrix

FEL:

Free energy landscape

MD:

Molecular dynamics

MM/PBSA:

Molecular mechanics/Poisson–Boltzmann surface area

ESKAPE:

Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp

MDR:

Multi-drug resistant

XDR:

Extensive drug-resistant

UDP-MurNAc-Ala:

Uridine diphosphate N-acetylmuramylalanine

References

  1. De Oliveira DMP et al (2020) Antimicrobial Resistance in ESKAPE Pathogens. Clin Microbiol Rev 33(3)

  2. Pendleton JN, Gorman SP, Gilmore BF (2013) Clinical relevance of the ESKAPE pathogens. Expert Rev Anti Infect Ther 11(3):297–308

    Article  CAS  PubMed  Google Scholar 

  3. Mulani MS et al (2019) Emerging strategies to combat ESKAPE pathogens in the era of antimicrobial resistance: a review. Front Microbiol 10:539

    Article  PubMed  PubMed Central  Google Scholar 

  4. Howard A et al (2012) Acinetobacter baumannii: an emerging opportunistic pathogen. Virulence 3(3):243–250

    Article  PubMed  PubMed Central  Google Scholar 

  5. Peleg AY, Seifert H, Paterson DL (2008) Acinetobacter baumannii: emergence of a successful pathogen. Clin Microbiol Rev 21(3):538–582

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Maragakis LL, Perl TM (2008) Acinetobacter baumannii: epidemiology, antimicrobial resistance, and treatment options. Clin Infect Dis 46(8):1254–1263

    Article  CAS  PubMed  Google Scholar 

  7. Antunes LC, Visca P, Towner KJ (2014) Acinetobacter baumannii: evolution of a global pathogen. Pathog Dis 71(3):292–301

    Article  CAS  PubMed  Google Scholar 

  8. Dijkshoorn L, Nemec A, Seifert H (2007) An increasing threat in hospitals: multidrug-resistant Acinetobacter baumannii. Nat Rev Microbiol 5(12):939–951

    Article  CAS  PubMed  Google Scholar 

  9. Smith CA (2006) Structure, function and dynamics in the mur family of bacterial cell wall ligases. J Mol Biol 362(4):640–655

    Article  CAS  PubMed  Google Scholar 

  10. Neshani A et al (2020) Antimicrobial peptides as a promising treatment option against Acinetobacter baumannii infections. Microb Pathog 146:104238

    Article  CAS  PubMed  Google Scholar 

  11. McConnell MJ, Pachon J (2010) Active and passive immunization against Acinetobacter baumannii using an inactivated whole cell vaccine. Vaccine 29(1):1–5

    Article  CAS  PubMed  Google Scholar 

  12. Garcia-Quintanilla M, Pulido MR, McConnell MJ (2013) First steps towards a vaccine against Acinetobacter baumannii. Curr Pharm Biotechnol 14(10):897–902

    Article  CAS  PubMed  Google Scholar 

  13. Kaur H, Kalia M, Taneja N (2021) Identification of novel non-homologous drug targets against Acinetobacter baumannii using subtractive genomics and comparative metabolic pathway analysis. Microb Pathog 152:104608

    Article  CAS  PubMed  Google Scholar 

  14. Kaur N et al (2013) Identification of druggable targets for Acinetobacter baumannii via subtractive genomics and plausible inhibitors for MurA and MurB. Appl Biochem Biotechnol 171(2):417–436

    Article  CAS  PubMed  Google Scholar 

  15. Egan AJF, Errington J, Vollmer W (2020) Regulation of peptidoglycan synthesis and remodelling. Nat Rev Microbiol 18(8):446–460

    Article  CAS  PubMed  Google Scholar 

  16. Le NH et al (2020) Peptidoglycan editing provides immunity to Acinetobacter baumannii during bacterial warfare. Sci Adv 6(30):eabb5614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lundqvist T et al (2007) Exploitation of structural and regulatory diversity in glutamate racemases. Nature 447(7146):817–822

    Article  CAS  PubMed  Google Scholar 

  18. de Jonge BL et al (2009) Pyrazolopyrimidinediones are selective agents for Helicobacter pylori that suppress growth through inhibition of glutamate racemase (MurI). Antimicrob Agents Chemother 53(8):3331–3336

    Article  PubMed  PubMed Central  Google Scholar 

  19. Doublet P et al (1993) The murI gene of Escherichia coli is an essential gene that encodes a glutamate racemase activity. J Bacteriol 175(10):2970–2979

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. The UniProt C (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45(D1):D158–D169

    Article  Google Scholar 

  21. Bernstein FC et al (1977) The Protein Data Bank. A computer-based archival file for macromolecular structures. Eur J Biochem 80(2):319–324

    Article  CAS  PubMed  Google Scholar 

  22. Johnson M et al (2008) NCBI BLAST: a better web interface. Nucleic Acids Res 36(Web Server issue):W5–W9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Waterhouse A et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Heo L, Park H, Seok C (2013) GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res 41(Web Server issue):W384–W388

    Article  PubMed  PubMed Central  Google Scholar 

  25. Morris AL et al (1992) Stereochemical quality of protein structure coordinates. Proteins 12(4):345–364

    Article  CAS  PubMed  Google Scholar 

  26. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170

    Article  CAS  PubMed  Google Scholar 

  28. Luthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83–85

    Article  CAS  PubMed  Google Scholar 

  29. Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243–250

    Article  CAS  PubMed  Google Scholar 

  30. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Sander T et al (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 55(2):460–473

    Article  CAS  PubMed  Google Scholar 

  32. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717

    Article  PubMed  PubMed Central  Google Scholar 

  33. Morris GM et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Van Der Spoel D et al (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718

    Article  PubMed  Google Scholar 

  35. Schuttelkopf AW, van Aalten DM (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 60(Pt 8):1355–1363

    Article  PubMed  Google Scholar 

  36. Huang W, Lin Z, van Gunsteren WF (2011) Validation of the GROMOS 54A7 force field with respect to beta-peptide folding. J Chem Theory Comput 7(5):1237–1243

    Article  CAS  PubMed  Google Scholar 

  37. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Laskowski RA et al (2018) PDBsum: structural summaries of PDB entries. Protein Sci 27(1):129–134

    Article  CAS  PubMed  Google Scholar 

  39. O'Boyle NM et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Dahlin JL et al (2015) PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS. J Med Chem 58(5):2091–2113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Dr. Amit Kumar Singh thanks Indian Council of Medical Research (ICMR) and Indian National Science Academy (INSA), New Delhi, India. The authors thank Sharda University for the support.

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Authors and Affiliations

  1. Department of Biotechnology, Sharda School of Engineering and Technology, Sharda University, P.C. 201310, Greater Noida, U.P., India

    Ankit Kumar, Ekampreet Singh, Rajat Kumar Jha, Rameez Jabeer Khan, Monika Jain, Jayaraman Muthukumaran & Amit Kumar Singh

  2. Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, P.C. 201310, Greater Noida, U.P., India

    Sudeep Varshney

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Contributions

Ankit Kumar: data curation; formal analysis; methodology; software; visualisation; writing—original draft; writing—review and editing. Ekampreet Singh: formal analysis; methodology; software; visualisation; writing-review and editing; Rajat Kumar Jha: formal analysis, writing-review and editing; Rameez Jabeer Khan: formal analysis; Monika Jain: formal analysis, writing—review and editing. Sudeep Varshney: software, writing—review and editing. Jayaraman Muthukumaran: validation, writing—review and editing. Amit Kumar Singh: conceptualisation, investigation, supervision, validation, writing—review and editing.

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Correspondence to Amit Kumar Singh.

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Kumar, A., Singh, E., Jha, R.K. et al. Targeting multi-drug-resistant Acinetobacter baumannii: a structure-based approach to identify the promising lead candidates against glutamate racemase. J Mol Model 29, 188 (2023). https://doi.org/10.1007/s00894-023-05587-4

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