Advertisement

Biotechnology Letters

, Volume 41, Issue 1, pp 115–128 | Cite as

Microbes, not humans: exploring the molecular basis of Pseudouridimycin selectivity towards bacterial and not human RNA polymerase

  • Ali H. Rabbad
  • Clement Agoni
  • Fisayo A. Olotu
  • Mahmoud E. SolimanEmail author
Original Research Paper
  • 110 Downloads

Abstract

Objective

Bacterial RNA polymerase (bRNAP) represent a crucial target for curtailing microbial activity but its structural and sequence similarities with human RNA polymerase II (hRNAPII) makes it difficult to target. Recently, Pseudouridimycin (PUM), a novel nucleoside analogue was reported to selectively inhibit bRNAP and not hRNAP. Till date, underlying mechanisms of PUM selectivity remains unresolved, hence the aim of this study.

Results

Using sequence alignment method, we observed that the β′ of bRNAP and the RPB1 subunits of hRNAPII were highly conserved while the β and RPB2 subunits of both proteins were also characterized by high sequence variations. Furthermore, the impact of these variations on the differential binding of PUM was evaluated using MMPB/SA binding free energy and per-residue decomposition analysis. These revealed that PUM binds better to bRNAP than hRNAP with prominent bRNAP active site residues that contributed the most to PUM binding and stabilization lacking in hRNAPII active site due to positional substitution. Also, the binding of PUM to hRNAP was characterized by the formation of unfavorable interactions. In addition, PUM assumed favorable orientations that possibly enhanced its mobility towards the hydrophobic core region of bRNAP. On the contrary, unfavorable intramolecular interactions characterize PUM orientations at the binding site of hRNAPII, which could restrict its movement due to electrostatic repulsions.

Conclusion

These findings would enhance the design of potent and selective drugs for broad-spectrum antimicrobial activity.

Keywords

Binding free energy Pseudouridimycin RNA polymerase selectivity Sequence alignment 

Notes

Acknowledgements

The authors thank the College of Health Sciences, University of KwaZulu-Natal for their infrastructural and financial support. Likewise, we thank the Center for High Performance Computing, Cape-Town for providing computational resources.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. Agoni C, Ramharack P, Soliman M (2018) Co-inhibition as a strategic therapeutic approach to overcome rifampin resistance in tuberculosis therapy: atomistic insights. Fut Med Chem.  https://doi.org/10.4155/fmc-2017-0197 Google Scholar
  2. Artsimovitch I, Vassylyev DG (2006) Is it easy to stop RNA polymerase? Cell Cycle 5:399–404CrossRefGoogle Scholar
  3. Berendsen HJC, Postma JPM, Van Gunsteren WF et al (2012) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  4. Burgess RR, Erickson B, Gentry D et al (1987) In: Reznikoff WS (ed) Bacterial RNA polymerase subunits and genes in RNA polymerase and the regulation of transcription, vol 198. Elsevier, New York, pp 3–15Google Scholar
  5. Campbell EA, Korzheva N, Mustaev A et al (2001) Structural mechanism for rifampicin inhibition of bacterial RNA polymerase. Cell 104:901–912CrossRefGoogle Scholar
  6. Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688CrossRefGoogle Scholar
  7. Chang Q, Wang W, Regev-Yochay G et al (2015) Antibiotics in agriculture and the risk to human health: how worried should we be? Evol Appl 8:240–247CrossRefGoogle Scholar
  8. Collins SL, Carr DF, Pirmohamed M (2016) Advances in the pharmacogenomics of adverse drug reactions. Drug Saf 39:15–27CrossRefGoogle Scholar
  9. Cramer P, Bushnell DA, Kornberg RD (2001) Structural basis of transcription: RNA polymerase II at 2.8 angstrom resolution. Sci (New York, NY) 292:1863–1876CrossRefGoogle Scholar
  10. Crane EA, Gademann K (2016) Capturing biological activity in natural product fragments by chemical synthesis. Angew Chem Int Ed 55:3882–3902CrossRefGoogle Scholar
  11. Davies J, Davies D (2010) Origins and evolution of antibioitc resistance. Microbiol Mol Biol Rev 74:417–433CrossRefGoogle Scholar
  12. Ebright RH (2000) RNA polymerase: structural similarities between bacterial RNA polymerase and eukaryotic RNA polymerase II. J Mol Biol 304:687–698CrossRefGoogle Scholar
  13. El Rashedy AA, Olotu FA, Soliman MES (2018) Dual drug targeting of mutant Bcr-Abl induces inactive conformation: new strategy for the treatment of chronic myeloid leukemia and overcoming monotherapy resistance. Chem Biodivers 15:e1700533CrossRefGoogle Scholar
  14. Essmann U, Perera L, Berkowitz ML et al (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593CrossRefGoogle Scholar
  15. Ferraris DM, Miggiano R, Rossi F, Rizzi M (2018) Mycobacterium tuberculosis molecular determinants of infection, survival strategies, and vulnerable targets. Pathogens 7:17CrossRefGoogle Scholar
  16. Frieri M, Kumar K, Boutin A (2017) Antibiotic resistance. J Infect Public Health 10:369–378CrossRefGoogle Scholar
  17. Garon SL, Pavlos RK, White KD et al (2017) Pharmacogenomics of off-target adverse drug reactions. Br J Clin Pharmacol 83:1896–1911CrossRefGoogle Scholar
  18. Grest GS, Kremer K (1986) Molecular dynamics simulation for polymers in the presence of a heat bath. Phys Rev A 33:3628–3631CrossRefGoogle Scholar
  19. Hanwell MD, Curtis DE, Lonie DC et al (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminf 4:17CrossRefGoogle Scholar
  20. Hayes JM, Archontis G (2012) MM-GB(PB)SA calculations of protein-ligand binding free energies. In: Molecular dynamics—studies of synthetic and biological macromolecules. InTechOpenGoogle Scholar
  21. Hou T, Wang J, Li Y et al (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Comput Sci 51:69–82CrossRefGoogle Scholar
  22. Huggins DJ, Sherman W, Tidor B (2012) Rational approaches to improving selectivity in drug design. J Med Chem 55:1424–1444CrossRefGoogle Scholar
  23. Kumar M, Curtis A, Hoskins C (2018) Application of nanoparticle technologies in the combat against anti-microbial resistance. Pharmaceutics 10:11CrossRefGoogle Scholar
  24. Larkin MA, Blackshields G, Brown NP et al (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23:2947–2948CrossRefGoogle Scholar
  25. Lee J, Borukhov S (2016) Bacterial RNA polymerase–DNA interaction: the driving force of gene expression and the target for drug action. Front Mol Biosci 3:73Google Scholar
  26. MacGowan A, Macnaughton E (2017) Antibiotic resistance. Medicine (Baltimore) 45:622–628CrossRefGoogle Scholar
  27. Machaba KE, Cele FN, Mhlongo NN, Soliman MES (2016) Sliding clamp of DNA polymerase III as a drug target for TB therapy: comprehensive conformational and binding analysis from molecular dynamic simulations. Cell Biochem Biophys 74:473–481CrossRefGoogle Scholar
  28. Machaba KE, Mhlongo NN, Dokurugu YM, Soliman ME (2017) Tailored-pharmacophore model to enhance virtual screening and drug discovery: a case study on the identification of potential inhibitors against drug-resistant Mycobacterium tuberculosis (3R)-hydroxyacyl-ACP dehydratases. Fut Med Chem 9:1055–1071CrossRefGoogle Scholar
  29. Maffioli SI, Zhang Y, Degen D et al (2017) Antibacterial nucleoside-analog inhibitor of bacterial RNA polymerase. Cell 169:1240–1248CrossRefGoogle Scholar
  30. Malleshappa Gowder S, Chatterjee J, Chaudhuri T, Paul K (2014) Prediction and analysis of surface hydrophobic residues in tertiary structure of proteins. Sci World J.  https://doi.org/10.1155/2014/971258 Google Scholar
  31. Molodtsov V, Fleming PR, Eyermann CJ et al (2015) X-ray crystal structures of Escherichia coli RNA polymerase with switch region binding inhibitors enable rational design of squaramides with an improved fraction unbound to human plasma protein. J Med Chem 58:3156–3171CrossRefGoogle Scholar
  32. Mukhopadhyay J, Das K, Ismail S et al (2008) The RNA polymerase “Switch Region” is a target for inhibitors. Cell 135:295–307CrossRefGoogle Scholar
  33. Murakami K (2015) Structural biology of bacterial RNA polymerase. Biomolecules 5:848–864CrossRefGoogle Scholar
  34. Murakami KS, Darst SA (2003) Bacterial RNA polymerases: the wholo story. Curr Opin Struct Biol 13:31–39CrossRefGoogle Scholar
  35. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera: a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  36. Qu C, Xu L, Yin Y et al (2017) Nucleoside analogue 2′-C-methylcytidine inhibits hepatitis E virus replication but antagonizes ribavirin. Arch Virol 162:2989–2996CrossRefGoogle Scholar
  37. Richardson LA (2017) Understanding and overcoming antibiotic resistance. PLoS Biol 15:e2003775CrossRefGoogle Scholar
  38. Rivers EC, Mancera RL (2008) New anti-tuberculosis drugs in clinical trials with novel mechanisms of action. Drug Discov Today 13:1090–1098CrossRefGoogle Scholar
  39. Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular synamics trajectory data. J Chem Theory Comput 9:3084–3095CrossRefGoogle Scholar
  40. Santos RS, Figueiredo C, Azevedo NF et al (2017) Nanomaterials and molecular transporters to overcome the bacterial envelope barrier: towards advanced delivery of antibiotics. Adv Drug Deliv Rev 1:1–10.  https://doi.org/10.1016/j.addr.2017.12.010 Google Scholar
  41. Seifert E (2014) OriginPro 9.1: scientific data analysis and graphing software: software review. J Chem Inf Model 54:1552CrossRefGoogle Scholar
  42. Sheppard C, James E, Barton G et al (2016) Is it easy to stop RNA polymerase? Cell Cycle 5:399–404Google Scholar
  43. Sievers F, Wilm A, Dineen D et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539CrossRefGoogle Scholar
  44. Sinokrot H, Smerat T, Najjar A, Karaman R (2017) Advanced prodrug strategies in nucleoside and non-nucleoside antiviral agents: a review of the recent five years. Molecules 22:1736CrossRefGoogle Scholar
  45. Speck-Planche A, Cordeiro MNDS (2012) Computer-aided drug design methodologies toward the design of anti-hepatitis C agents. Curr Top Med Chem 12:802–813CrossRefGoogle Scholar
  46. Srivastava A, Degen D, Ebright YW, Ebright RH (2012) Frequency, spectrum, and nonzero fitness costs of resistance to myxopyronin in Staphylococcus aureus Google Scholar
  47. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680CrossRefGoogle Scholar
  48. 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 31:455–461Google Scholar
  49. Ventola CL (2015) The antibiotic resistance crisis: part 1: causes and threats. P T A peer-reviewed. J Formul Manag 40:277–283Google Scholar
  50. Vivet-Boudou V, Isel C, El Safadi Y et al (2015) Evaluation of anti-HIV-1 mutagenic nucleoside analogues. J Biol Chem 290:371–383CrossRefGoogle Scholar
  51. Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general Amber force field. J Comput Chem 25:1157–1174CrossRefGoogle Scholar
  52. Wang L, Hu C, Shao L (2017) The antimicrobial activity of nanoparticles: present situation and prospects for the future. Int J Nanomed 12:1227–1249CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Ali H. Rabbad
    • 1
  • Clement Agoni
    • 1
  • Fisayo A. Olotu
    • 1
  • Mahmoud E. Soliman
    • 1
    Email author
  1. 1.Molecular Bio-Computation Drug Design Research Group, School of Health SciencesUniversity of KwaZulu NatalDurbanSouth Africa

Personalised recommendations