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Identification of promising molecules against MurD ligase from Acinetobacter baumannii: insights from comparative protein modelling, virtual screening, molecular dynamics simulations and MM/PBSA analysis

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Abstract

Acinetobacter baumannii, an opportunistic bacterium of the multidrug-resistant (MDR) ESKAPE family of pathogens, is responsible for 2–10% infections associated with all gram-negative bacteria. The hospital-acquired nosocomial infections caused by A.baumannii include deadly diseases like ventilator-associated pneumonia, bacteremia, septicemia and urinary tract infections (UTI). Over the last 3 years, it has evolved into multiple strains demonstrating high antibiotic resistance against a wide array of antibiotics. Hence, it becomes imperative to identify novel drug-like molecules to treat such infections effectively. UDP-N-acetylmuramoyl-L-alanine-D-glutamate ligase (MurD) is an essential enzyme of the Mur family which is responsible for peptidoglycan biosynthesis, making it a unique and ideal drug target. Initially, a homology modelling approach was employed to predict the three-dimensional model of MurD from A. baumannii using MurD from Escherichia coli (PDB ID: 4UAG) as a suitable structural template. Subsequently, an optimised model of MurD was subjected to virtual high-throughput screening (vHTS) against a ZINC library of ~ 642,759 commercially available molecules to identify promising lead compounds demonstrating high binding affinities towards it. From the screening process, four promising molecules were identified based on the estimated binding affinities (ΔG), estimated inhibition constants (Ki), catalytic residue interactions and drug-like properties, which were then subjected to molecular dynamics (MD) simulation studies to reflect the physiological state of protein molecules in vivo equivalently. The binding free energies of the selected MurD-ligand complexes were also calculated using MM/PBSA (molecular mechanics with Poisson-Boltzmann and surface area solvation) approach. Finally, the global dynamics along with binding free energy analysis suggested that ZINC19221101 (ΔG = − 62.6 ± 5.6 kcal/mol) and ZINC12454357 (ΔG = − 46.1 ± 2.6 kcal/mol) could act as most promising candidates for inhibiting the function of MurD ligase and aid in drug discovery and development against A.baumannii.

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Abbreviations

MD:

Molecular dynamics simulations

MDRAB:

Multidrug-resistant Acinetobacter baumannii

SBDD:

Structure-based drug design

vHTS:

Virtual high-throughput screening

References

  1. Howard A, O’Donoghue M, Feeney A, Sleator RD (2012) Acinetobacter baumannii: an emerging opportunistic pathogen. Virulence. 3:243–250

    PubMed  PubMed Central  Google Scholar 

  2. Chaari A, Mnif B, Bahloul M, Mahjoubi F, Chtara K, Turki O et al (2013) Acinetobacter baumannii ventilator-associated pneumonia: epidemiology, clinical characteristics, and prognosis factors. Int J Infect Dis 17:e1225–e12e8

    PubMed  Google Scholar 

  3. Jiménez-Guerra G, Heras-Cañas V, Gutiérrez-Soto M, del Pilar Aznarte-Padial M, Expósito-Ruiz M, Navarro-Marí JM et al (2018) Urinary tract infection by Acinetobacter baumannii and Pseudomonas aeruginosa: evolution of antimicrobial resistance and therapeutic alternatives 67:790–797

  4. Ni S, Li S, Yang N, Zhang S, Hu D, Li Q et al (2015) Post-neurosurgical meningitis caused by acinetobacter baumannii: case series and review of the literature. Int J Clin Exp Med 8:21833–21838

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Al-Anazi KA, Abdalhamid B, Alshibani Z, Awad K, Alzayed A, Hassan H et al (2012) Acinetobacter baumannii septicemia in a recipient of an allogeneic hematopoietic stem cell transplantation. Case Rep Transplant 2012:646195

    PubMed  PubMed Central  Google Scholar 

  6. Santajit S, Indrawattana N (2016) Mechanisms of antimicrobial resistance in ESKAPE pathogens. Biomed Res Int 2016:2475067

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  8. Singh H, Thangaraj P, Chakrabarti A (2013) Acinetobacter baumannii: a brief account of mechanisms of multidrug resistance and current and future therapeutic management. J Clin Diagn Res 7:2602–2605

    PubMed  PubMed Central  Google Scholar 

  9. Shrivastava S, Shrivastava P, Ramasamy J (2018) Utilizing a toolkit to respond to the health needs of migrant people in the European region: World Health Organization 9:44–45

  10. Nelson RE, Schweizer ML, Perencevich EN, Nelson SD, Khader K, Chiang H-Y et al (2016) Costs and mortality associated with multidrug-resistant healthcare-associated Acinetobacter infections. Infect Control Hosp Epidemiol 37:1212–1218

    PubMed  Google Scholar 

  11. Teerawattanapong N, Panich P, Kulpokin D, Na Ranong S, Kongpakwattana K, Saksinanon A et al (2018) A systematic review of the burden of multidrug-resistant healthcare-associated infections among intensive care unit patients in Southeast Asia: the rise of multidrug-resistant Acinetobacter baumannii. Infect Control Hosp Epidemiol 39:525–533

    PubMed  Google Scholar 

  12. Bugg TDH, Braddick D, Dowson CG, Roper DI (2011) Bacterial cell wall assembly: still an attractive antibacterial target. Trends Biotechnol 29:167–173

    CAS  PubMed  Google Scholar 

  13. Kim HU, Kim TY, Lee SY (2010) Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol BioSyst 6:339–348

    CAS  PubMed  Google Scholar 

  14. Bugg TDH, Walsh CT (1992) Intracellular steps of bacterial cell wall peptidoglycan biosynthesis: enzymology, antibiotics, and antibiotic resistance. Nat Prod Rep 9:199–215

    CAS  PubMed  Google Scholar 

  15. Meroueh SO, Bencze KZ, Hesek D, Lee M, Fisher JF, Stemmler TL et al (2006) Three-dimensional structure of the bacterial cell wall peptidoglycan 103:4404–4409

  16. Barreteau H, Kovač A, Boniface A, Sova M, Gobec S, Blanot D (2008) Cytoplasmic steps of peptidoglycan biosynthesis. FEMS Microbiol Rev 32:168–207

    CAS  PubMed  Google Scholar 

  17. Chattaway FW Microbial cell walls and membranes: by H J Rogers, H R Perkins and J B Ward. pp 564. Chapman & Hall, London. 1980 £30. ISBN 0-412-12030-5. 1982, 10, 33

  18. Heijenoort JV Chapter 3 Biosynthesis of the bacterial peptidoglycan unit. In: New comprehensive biochemistry, Ghuysen, J.M., Hakenbeck, R. Eds., Elsevier, 1994, Vol. 27, pp. 39–54

  19. Bertrand JA, Auger G, Fanchon E, Martin L, Blanot D, van Heijenoort J et al (1997) Crystal structure of UDP-N-acetylmuramoyl-L-alanine: D-glutamate ligase from Escherichia coli 16:3416–3425

  20. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Bertrand JA, Auger G, Martin L, Fanchon E, Blanot D, Le Beller D et al (1999) Determination of the MurD mechanism through crystallographic analysis of enzyme complexes. J Mol Biol 289:579–590

    CAS  PubMed  Google Scholar 

  22. Kaur N, Khokhar M, Jain V, Bharatam PV, Sandhir R, Tewari R (2013) Identification of druggable targets for Acinetobacter baumannii via subtractive genomics and plausible inhibitors for MurA and MurB. Appl Biochem Biotechnol 171:417–436

    CAS  PubMed  Google Scholar 

  23. Ahmad S, Murtaza UA, Raza S, Azam SS (2019) Blocking the catalytic mechanism of MurC ligase enzyme from Acinetobacter baumannii: an in silico guided study towards the discovery of natural antibiotics. J Mol Liq 281:117–133

    CAS  Google Scholar 

  24. Amera GM, Khan RJ, Pathak A, Kumar A, Singh AK (2019) Structure based in-silico study on UDP-N-acetylmuramoyl-L-alanyl-D-glutamate-2,6-diaminopimelate ligase (MurE) from Acinetobacter baumannii as a drug target against nosocomial infections. Inform Med Unlocked 16:100216

    Google Scholar 

  25. Ahmad S, Raza S, Uddin R, Azam SS (2017) Binding mode analysis, dynamic simulation and binding free energy calculations of the MurF ligase from Acinetobacter baumannii. J Mol Graph Model 77:72–85

    CAS  PubMed  Google Scholar 

  26. Amera GM, Khan RJ, Pathak A, Jha RK, Muthukumaran J, Singh AK (2019) Screening of promising molecules against MurG as drug target in multi-drug-resistant-Acinetobacter baumannii - insights from comparative protein modeling, molecular docking and molecular dynamics simulation. J Biomol Struct Dyn:1–23

  27. Consortium TU (2018) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–DD15

    Google Scholar 

  28. Gasteiger E, Hoogland C, Gattiker A, Duvaud Se, Wilkins MR, Appel RD et al (2005) Protein Identification and Analysis tools on the ExPASy server. In: The proteomics protocols handbook, Walker, J.M. Ed., Humana Press, Totowa, pp. 571–607

  29. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R et al (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Deng W, Wang C, Zhang Y, Xu Y, Zhang S, Liu Z et al (2016) GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences. Sci Rep 6:39787

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Chauhan JS, Bhat AH, Raghava GPS, Rao A (2012) GlycoPP: a webserver for prediction of N- and O-glycosites in prokaryotic protein sequences. PLoS One 7:e40155

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Pan Z, Wang B, Zhang Y, Wang Y, Ullah S, Jian R et al (2015) dbPSP: a curated database for protein phosphorylation sites in prokaryotes. Database (Oxford) 2015:bav031-bav

    Google Scholar 

  33. Gupta A, Deshpande A, Amburi JK, Sabarinathan R, Senthilkumar R, Sekar K (2009) CSSP (Consensus Secondary Structure Prediction): a web-based server for structural biologists 42:336–338

  34. King RD, Sternberg MJ (1996) Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci 5:2298–2310

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Guermeur Y, Geourjon C, Gallinari P, Deleage G (1999) Improved performance in protein secondary structure prediction by inhomogeneous score combination. Bioinformatics (Oxford, England) 15:413–421

    CAS  Google Scholar 

  36. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202

    CAS  PubMed  Google Scholar 

  37. Rost B, Sander C (1993) Secondary structure prediction of all-helical proteins in two states. Protein Eng 6:831–836

    CAS  PubMed  Google Scholar 

  38. Rice P, Longden I, Bleasby A (2000) EMBOSS: the European molecular biology open software suite. Trends Genet 16:276–277

    CAS  PubMed  Google Scholar 

  39. Pieper U, Eswar N, Webb BM, Eramian D, Kelly L, Barkan DT et al (2008) Modbase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 37:D347–DD54

    PubMed  PubMed Central  Google Scholar 

  40. DeLano WF (2002) The PyMOL molecular graphics system. DeLano Scientific

  41. Biovia DS (2019) Discovery studio visualization Dassault Systèmes BIOVIA

  42. Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J et al (2009) Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: four approaches that performed well in CASP8 77:114–122

  43. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures 26:283–291

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

  45. Eisenberg D, Lüthy R, Bowie JU (1997) [20] VERIFY3D: assessment of protein models with three-dimensional profiles. Methods in enzymology, vol 277. Academic Press, pp 396–404

  46. Ye Y, Godzik A (2003) Flexible structure alignment by chaining aligned fragment pairs allowing twists. Bioinformatics (Oxford, England) 19(Suppl 2):ii246–ii255

    Google Scholar 

  47. Irwin JJ, Shoichet BK (2005) ZINC − a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Viswanathan U, Tomlinson SM, Fonner JM, Mock SA, Watowich SJ (2014) Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery web portal. J Chem Inf Model 54:2816–2825

    CAS  PubMed  Google Scholar 

  49. Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading 31:455–461

  50. Nguyen PTV, Yu H, Keller PA (2018) Molecular docking studies to explore potential binding pockets and inhibitors for Chikungunya virus envelope glycoproteins. Interdiscip Sci 10:515–524

    CAS  PubMed  Google Scholar 

  51. Hetényi C, van der Spoel D (2006) Blind docking of drug-sized compounds to proteins with up to a thousand residues 580:1447–1450

  52. Ghersi D, Sanchez R (2009) Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites 74:417–424

  53. Sander T, Freyss J, von Korff M, Rufener C (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 55:460–473

    CAS  PubMed  Google Scholar 

  54. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341

    CAS  Google Scholar 

  55. 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

    PubMed  PubMed Central  Google Scholar 

  56. Alonso H, Bliznyuk AA, Gready JE (2006) Combining docking and molecular dynamic simulations in drug design 26:531–568

  57. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible, and free. 26, 1701–18

  58. Pol-Fachin L, Fernandes CL, Verli H (2009) GROMOS96 43a1 performance on the characterization of glycoprotein conformational ensembles through molecular dynamics simulations. Carbohydr Res 344:491–500

    CAS  PubMed  Google Scholar 

  59. Schuttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr Sect D 60:1355–1363

    Google Scholar 

  60. Khan RJ, Jha RK, Amera GM, Jain M, Singh E, Pathak A et al (2020) Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2′-O-ribose methyltransferase. J Biomol Struct Dyn:1–14

  61. Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105:9954–9960

    CAS  Google Scholar 

  62. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations 18:1463–1472

  63. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method 52:7182–7190

  64. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems 98:10089–10092

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

    CAS  Google Scholar 

  66. Xue J, Huang X, Zhu Y (2019) Using molecular dynamics simulations to evaluate active designs of cephradine hydrolase by molecular mechanics/Poisson–Boltzmann surface area and molecular mechanics/generalized born surface area methods. RSC Adv 9:13868–13877

    CAS  Google Scholar 

  67. Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson-Boltzmann surface area method. Mol Inform 31:114–122

  68. Kumari R, Kumar R, Lynn A (2014) g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54:1951–1962

    CAS  PubMed  Google Scholar 

  69. Tiwari V (2019) Post-translational modification of ESKAPE pathogens as a potential target in drug discovery. Drug Discov Today 24:814–822

    CAS  PubMed  Google Scholar 

  70. Micheletti C (2013) Comparing proteins by their internal dynamics: exploring structure–function relationships beyond static structural alignments. Phys Life Rev 10:1–26

    PubMed  Google Scholar 

  71. Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797

    CAS  PubMed  Google Scholar 

  72. Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM (2018) PDBsum: structural summaries of PDB entries 27:129–134

  73. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14:133–141

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Morris GM, Lim-Wilby M (2008) Molecular docking. In: Kukol A (ed) Molecular modeling of proteins. Humana Press, Totowa, pp 365–382

    Google Scholar 

  75. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    CAS  PubMed  Google Scholar 

  76. Amera GM, Khan RJ, Pathak A, Jha RK, Muthukumaran J, Singh AK (2020) Computer aided ligand based screening for identification of promising molecules against enzymes involved in peptidoglycan biosynthetic pathway from Acinetobacter baumannii. Microb Pathog:104205

  77. Scott P, Deye G, Srinivasan A, Murray C, Moran K, Hulten E et al (2007) An outbreak of multidrug-resistant Acinetobacter baumannii-calcoaceticus complex infection in the US military health care system associated with military operations in Iraq. Clin Infect Dis 44:1577–1584

    CAS  PubMed  Google Scholar 

  78. Shelburne SA, Singh KV, White AC, Byrne L, Carmer A, Austin C et al (2008) Sequential outbreaks of infections by distinct Acinetobacter baumannii strains in a public teaching hospital in Houston, Texas 46:198–205

  79. Hujer AM, Higgins PG, Rudin SD, Buser GL, Marshall SH, Xanthopoulou K et al (2017) Nosocomial outbreak of extensively drug-resistant Acinetobacter baumannii isolates containing blaOXA-237 carried on a plasmid 61:e00797–e00717

  80. Zhao Y, Hu K, Zhang J, Guo Y, Fan X, Wang Y et al (2019) Outbreak of carbapenem-resistant Acinetobacter baumannii carrying the carbapenemase OXA-23 in ICU of the eastern Heilongjiang Province, China. BMC Infect Dis 19:452

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, Mulero M et al (2019) The light and dark sides of virtual screening: what is there to know? 20:1375

  82. Marklund EG, Benesch JL (2019) Weighing-up protein dynamics: the combination of native mass spectrometry and molecular dynamics simulations. Curr Opin Struct Biol 54:50–58

    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. Gizachew Muluneh Amera thanks the College of Natural Science, Wollo University, Dessie, Ethiopia for the sponsorship. The authors also thank Sharda University and Supercomputing Facility for Bioinformatics & Computational Biology, IIT Delhi.

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  1. Rajat Kumar Jha and Rameez Jabeer Khan contributed equally to this work.

Authors and Affiliations

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

    Rajat Kumar Jha, Rameez Jabeer Khan, Gizachew Muluneh Amera, Ekampreet Singh, Monika Jain, Jayaraman Muthukumaran & Amit Kumar Singh

  2. Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India

    Amita Pathak

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Jha, R.K., Khan, R.J., Amera, G.M. et al. Identification of promising molecules against MurD ligase from Acinetobacter baumannii: insights from comparative protein modelling, virtual screening, molecular dynamics simulations and MM/PBSA analysis. J Mol Model 26, 304 (2020). https://doi.org/10.1007/s00894-020-04557-4

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