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Identifying potential inhibitors of biofilm-antagonistic proteins to promote biofilm formation: a virtual screening and molecular dynamics simulations approach


Microbial biofilms play a critical role in environmental biotechnology and associated applications. Biofilm production can be enhanced by inhibiting the function of proteins that negatively regulate their formation. With this objective, an in silico approach was adopted to identify competitive inhibitors of eight biofilm-antagonistic proteins, namely AbrB and SinR (from Bacillus subtilis) and AmrZ, PDE (EAL), PslG, RetS, ShrA and TpbA (from Pseudomonas aeruginosa). Fifteen inhibitors that structurally resembled the natural ligand of each protein were shortlisted using ligand-based and structure-based virtual screening. The top four inhibitors obtained from molecular docking using Autodock Vina were further docked using SwissDock and DOCK 6.9 to obtain a consensus hit for each protein based on different scoring functions. Further analysis of the protein–ligand complexes revealed that these top inhibitors formed significant non-covalent interactions with their respective protein binding sites. The eight protein-ligand complexes were then subjected to molecular dynamics simulations for 30 ns using GROMACS. RMSD and radius of gyration values of 0.1–0.4 nm and 1.0–3.5 nm, respectively, along with hydrogen bond formation throughout the trajectory indicated that all the complexes remained stable, compact and intact during the simulation period. Binding energy values between –20 and –77 kJ/mol obtained from MM-PBSA calculations further confirmed the high affinities of the eight inhibitors for their respective receptors. The outcome of this study holds great promise to enhance biofilms that are central to biotechnological processes associated with microbial electrochemical technologies, wastewater treatment, bioremediation and the industrial production of value-added products.

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All additional data relating to the study are available from the authors upon reasonable request.


  1. 1.

    Samantaray PK, Madras G, Bose S (2019) Microbial biofilm membranes for water remediation and photobiocatalysis. In: Rathinam NK and Sani RK (eds). Next generation biomanufacturing technologies. ACS.

  2. 2.

    Qureshi N, Annous BA, Ezeji TC et al (2005) Biofilm reactors for industrial bioconversion process: employing potential of enhanced reaction rates. Microb Cell Fact 4:24.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Germec M, Demirci A, Turhan I (2020) Biofilm reactors for value-added products production: an in-depth review. Biocatal Agric Biotechnol 27:101662.

    Article  Google Scholar 

  4. 4.

    Ramírez-Vargas C, Prado A, Arias C et al (2018) Microbial electrochemical technologies for wastewater treatment: Principles and evolution from microbial fuel cells to bioelectrochemical-based constructed wetlands. Water 10:1128.

    CAS  Article  Google Scholar 

  5. 5.

    Skariyachan S, Sridhar VS, Packirisamy S et al (2018) Recent perspectives on the molecular basis of biofilm formation by Pseudomonas aeruginosa and approaches for treatment and biofilm dispersal. Folia Microbiol (Praha) 63:413–432.

    CAS  Article  Google Scholar 

  6. 6.

    Sangshetti JN, Khan FAK, Patil RH et al (2015) Biofilm inhibition of linezolid-like Schiff bases: synthesis, biological activity, molecular docking and in silico ADME prediction. Bioorganic Med Chem Lett 25:874–880.

    CAS  Article  Google Scholar 

  7. 7.

    Lasa I, Penadés JR (2006) Bap: A family of surface proteins involved in biofilm formation. Res Microbiol 157:99–107.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Latasa C, Solano C, Penadés JR, Lasa I (2006) Biofilm-associated proteins. CR Biol 329:849–857.

    CAS  Article  Google Scholar 

  9. 9.

    Reguera G (2018) Microbial nanowires and electroactive biofilms. FEMS Microbiol Ecol 94:86.

    CAS  Article  Google Scholar 

  10. 10.

    Hu Y, Mukherjee M, Cao B (2019) Biofilm-biology-informed biofilm engineering for environmental biotechnology. In: Rathinam NK and Sani RK (eds) Introduction to biofilm engineering, ACS. 59–82.

  11. 11.

    Jones CJ, Newsom D, Kelly B et al (2014) ChIP-Seq and RNA-Seq reveal an AmrZ-mediated mechanism for cyclic di-GMP synthesis and biofilm development by Pseudomonas aeruginosa. PLoS Pathog 10:e1003984.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bhagirath AY, Pydi SP, Li Y et al (2017) Characterization of the direct interaction between hybrid sensor kinases PA1611 and RetS that controls biofilm formation and the Type III secretion system in Pseudomonas aeruginosa. ACS Infect Dis 3:162–175.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Zhou L, Li T, An J et al (2017) Subminimal inhibitory concentration (sub-MIC) of antibiotic induces electroactive biofilm formation in bioelectrochemical systems. Water Res 125:280–287.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Monzon O, Yang Y, Li Q, Alvarez PJJ (2016) Quorum sensing autoinducers enhance biofilm formation and power production in a hypersaline microbial fuel cell. Biochem Eng J 109:222–227.

    CAS  Article  Google Scholar 

  15. 15.

    Berman HM, Battistuz T, Bhat TN et al (2002) The protein data bank. Acta Crystallogr Sect D Biol Crystallogr 58:899–907.

    Article  Google Scholar 

  16. 16.

    Sullivan DM, Bobay BG, Kojetin DJ et al (2008) Insights into the nature of DNA binding of AbrB-like transcription factors. Structure 16:1702–1713.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Colledge VL, Fogg MJ, Levdikov VM et al (2011) Structure and organisation of SinR, the master regulator of biofilm formation in Bacillus subtilis. J Mol Biol 411:597–613.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Pryor EE Jr, Waligora EA, Xu B et al (2012) The transcription factor AmrZ utilizes multiple DNA binding modes to recognize activator and repressor sequences of Pseudomonas aeruginosa virulence genes. PLoS Pathog 8:e1002648.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Bellini D, Horrell S, Hutchin A et al (2017) Dimerisation induced formation of the active site and the identification of three metal sites in EAL-phosphodiesterases. Sci Rep 7:1–11.

    CAS  Article  Google Scholar 

  20. 20.

    Baker P, Whitfield GB, Hill PJ et al (2015) Characterization of the Pseudomonas aeruginosa glycoside hydrolase PslG reveals that its levels are critical for Psl polysaccharide biosynthesis and biofilm formation. J Biol Chem 290:28374–28387.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Mancl JM, Ray WK, Helm RF, Schubot FD (2019) Helix cracking regulates the critical interaction between RetS and GacS in Pseudomonas aeruginosa. Structure 27:785-793.e5.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Xu K, Li S, Yang W et al (2015) Structural and biochemical analysis of Tyrosine Phosphatase Related to Biofilm Formation A (TpbA) from the opportunistic pathogen Pseudomonas aeruginosa PAO1. PLoS ONE 10:e0124330.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Pu M, Sheng L, Song S et al (2018) Serine hydroxymethyltransferase ShrA (PA2444) controls rugose small-colony variant formation in Pseudomonas aeruginosa. Front Microbiol 9:315.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bienert S, Waterhouse A, De Beer TAP et al (2017) The SWISS-MODEL repository-new features and functionality. Nucleic Acids Res 45:D313–D319.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Logan BE, Rossi R, Ragab A, Saikaly PE (2019) Electroactive microorganisms in bioelectrochemical systems. Nat Rev Microbiol 17:307–319.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Yu S, Su T, Wu H et al (2015) PslG, a self-produced glycosyl hydrolase, triggers biofilm disassembly by disrupting exopolysaccharide matrix. Cell Res 25:1352–1367.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Zoete V, Daina A, Bovigny C, Michielin O (2016) SwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening. J Chem Inf Model 56:1399–1404.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: A major update to the drugbank database for 2018. Nucleic Acids Res 46:D1074–D1082.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31:3381–3385.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Baba N, Akaho E (2011) VSDK: Virtual screening of small molecules using autodock vina on windows platform. Bioinformation 6:387–388.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    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 31:455–461.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Hanwell MD, Curtis DE, Lonie DC et al (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 4:17.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical tool box. J Cheminform 3:1–14.

    CAS  Article  Google Scholar 

  36. 36.

    Boittier ED, Tang YY, Buckley ME et al (2020) (2020) Assessing molecular docking tools to guide targeted drug discovery of CD38 inhibitors. Int J Mol Sci 21(21):5183.

    CAS  Article  PubMed Central  Google Scholar 

  37. 37.

    Grosdidier A, Zoete V, Michielin O (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39:270–277.

    CAS  Article  Google Scholar 

  38. 38.

    Allen WJ, Balius TE, Mukherjee S et al (2015) DOCK 6: Impact of new features and current docking performance. J Comput Chem 36:1132–1156.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Grosdidier A, Zoete V, Michielin O (2007) EADock: Docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins Struct Funct Bioinforma 67:1010–1025.

    CAS  Article  Google Scholar 

  40. 40.

    Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera - A visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Abelyan N, Grabski H, Tiratsuyan S (2020) In silico Screening of flavones and its derivatives as potential inhibitors of Quorum-Sensing regulator LasR of Pseudomonas aeruginosa. Mol Biol 54:134–143.

    CAS  Article  Google Scholar 

  42. 42.

    Yang J, Chen Y, Shen T et al (2005) Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model 45:1134–1146.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Lindahl, Abraham, Hess, Spoel van der (2020) GROMACS 2020.3 Manual.

  44. 44.

    Sousa Da Silva AW, Vranken WF (2012) ACPYPE - Antechamber PYthon parser interfacE. BMC Res Notes 5:1–8.

    Article  Google Scholar 

  45. 45.

    Lemkul J (2019) From proteins to perturbed hamiltonians: A suite of tutorials for the GROMACS-2018 molecular simulation package [Article v1.0]. Living J Comput Mol Sci. 1:5068.

  46. 46.

    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  Article  PubMed  Google Scholar 

  47. 47.

    Ferreira L, dos Santos R, Oliva G, Andricopulo A (2015) Molecular docking and structure-based drug design strategies. Molecules 20:13384–13421.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Santos FRS, Nunes DAF, Lima WG et al (2020) Identification of Zika Virus NS2B-NS3 protease inhibitors by structure-based virtual screening and drug repurposing approaches. J Chem Inf Model 60:731–737.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Zeng Z, Qian L, Cao L et al (2008) Virtual screening for novel quorum sensing inhibitors to eradicate biofilm formation of Pseudomonas aeruginosa. Appl Microbiol Biotechnol 79:119–126.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    de Freitas RF, Schapira M (2017) A systematic analysis of atomic protein-ligand interactions in the PDB. Med Chem Comm 8:1970–1981.

    Article  Google Scholar 

  51. 51.

    Adeniji SE, Arthur DE, Abdullahi M, Haruna A (2020) Quantitative structure–activity relationship model, molecular docking simulation and computational design of some novel compounds against DNA gyrase receptor. Chem Africa 3:391–408.

    CAS  Article  Google Scholar 

  52. 52.

    Das S, Sarmah S, Lyndem S, Singha Roy A (2021) An investigation into the identification of potential inhibitors of SARS-CoV-2 main protease using molecular docking study. J Biomol Struct Dyn 39:3347–3357.

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Zhou L, Ma YC, Tang X et al (2021) Identification of the potential dual inhibitor of protein tyrosine phosphatase sigma and leukocyte common antigen-related phosphatase by virtual screen, molecular dynamic simulations and post-analysis. J Biomol Struct Dyn 39:45–62.

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Joshi T, Joshi T, Sharma P et al (2021) Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae pv. Oryzae by targeting peptide deformylase. J Biomol Struct Dyn 39:823–840.

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Blanco-Díaz EG, Castrejón-González EO, Alvarado JFJ et al (2017) Rheological behavior of ionic liquids: analysis of the H-bond formation by molecular dynamics. J Mol Liq 242:265–271.

    CAS  Article  Google Scholar 

  56. 56.

    Sinha SK, Prasad SK, Islam MA et al (2021) Identification of bioactive compounds from Glycyrrhiza glabra as possible inhibitor of SARS-CoV-2 spike glycoprotein and non-structural protein-15: a pharmacoinformatics study. J Biomol Struct Dyn 39:4686–4700.

    CAS  Article  PubMed  Google Scholar 

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This work is dedicated to Bhagawan Sri Sathya Sai Baba, the founder chancellor of the Sri Sathya Sai Institute of Higher Learning. The authors thank Mr. Prasanth Ghanta, Mr. Sahashransu Satyajeet Mahapatra and Ms. Jyotsna Jai for their support in carrying out this study. Computational facilities provided by the Department of Mathematics and Computer Science, SSSIHL, and DBT-BIF, Govt. of India, are gratefully acknowledged. The valuable comments provided in the peer-review process brought in more clarity to the discussion of results.

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MM and ASV conceived and designed research. MM conducted experiments. MM and ASV analyzed data. MM wrote the manuscript. ASV reviewed and approved the manuscript.

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Correspondence to A. S. Vishwanathan.

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Mukhi, M., Vishwanathan, A.S. Identifying potential inhibitors of biofilm-antagonistic proteins to promote biofilm formation: a virtual screening and molecular dynamics simulations approach. Mol Divers (2021).

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  • In silico
  • Biofilms
  • Biofilm-antagonistic proteins
  • Virtual screening
  • Molecular docking
  • Molecular dynamics