Journal of Molecular Modeling

, 25:360 | Cite as

Computer-aided identification of lead compounds as Staphylococcal epidermidis FtsZ inhibitors using molecular docking, virtual screening, DFT analysis, and molecular dynamic simulation

  • Swayansiddha TripathyEmail author
  • Susanta Kumar Sahu
  • Mohammed Afzal Azam
  • Srikanth Jupudi
Original Paper


In an effort to face the multiple drug-resistant bacteria, various approaches have been discovered to design potent compounds and search new targets through computational design tools. With an aim to identify selective inhibitors against filamentous temperature-sensitive mutant Z (FtsZ), a library of Phase database compounds have been virtually screened. High-throughput virtual screening of compounds against Staphylococcal epidermidis FtsZ protein (4M8I) was performed using three sequential docking modes like high-throughput virtual screening, Glide standard precision, followed by Glide extra precision. Four top-ranked compounds were selected from molecular mechanics-generalized Born surface area (MM-GBSA) binding energy with better predicted free binding energies of − 89.309, − 54.382, − 53.667, and − 52.133 kcal/mol, respectively. It is also showed that the contribution of van der Waals and electrostatic solvation energy terms are playing a major part to make the hit molecule (T6288784) binding to S. epidermidis FtsZ protein. The result of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and energy gap analysis predicts the molecular reactivity and stability of hit molecules. Subsequently, Lipinski’s rule of five and properties of absorption, distribution, metabolism, and excretion (ADME) were to calculate their bioavailability. The average binding energy − 9.67 kcal/mol of the best proposed hit molecule (T6288784) was found with half-maximal inhibitory concentration (IC50) value to be 75.53 nM. A 15-ns molecular dynamics simulation study revealed the stable conformation of hit molecule. On a wide-range research discipline, in silico studies of our proposed compound confirm promising results and can be successfully used towards the development of novel FtsZ inhibitor with better binding affinity.

Graphical Abstract



FtsZ  Molecular docking  DFT calculation  ADME analysis  Molecular dynamics simulation 



filamentous temperature-sensitive protein Z


guanosine 5′-triphosphate


guanosine 5′-diphosphate (GTP)

Glide SP

Glide standard precision

Glide XP

Glide extra precision


electron volt


molecular mechanics-generalized Born surface area


molecular dynamics


absorption, distribution, metabolism, excretion


half-maximal inhibitory concentration


estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution


estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution


predicted water/gas partition coefficient


predicted octanol/gas partition coefficient


predicted octanol/water partition coefficient


predicted aqueous solubility, logarithm S S in mol dm−3


predicted IC50 value for blockage of HERG K+ channels


predicted apparent Caco-2 cell permeability in nm/sec


predicted brain/blood partition coefficient


predicted apparent Madin-Darby canine kidney cell permeability in nm/sec


predicted skin permeability


prediction of binding to human serum albumin


percent human oral absorption


nano molar



The authors would like to thank the Head of the Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Ooty, India, and the Innovative Informatica Technologies, Hyderabad, India, for supporting the use of the software Schrödinger.

Funding information

We would like to thank the Department of Science and Technology (DST), the Women Scientist Scheme-A (WOS-A), and the Ministry of Science and Technology, Government of India, for the support (File No. SR/WOSA/CS-1108/2015- G).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

894_2019_4238_MOESM1_ESM.docx (1.7 mb)
ESM 1 (DOCX 1722 kb)


  1. 1.
    Errington J, Daniel RA, Scheffers DJ (2003) Cytokinesis in bacteria. Microbiol Mol Biol Rev 67(1):52–65. CrossRefGoogle Scholar
  2. 2.
    Romberg L, Levin PA (2003) Assembly dynamics of the bacterial cell division protein FtsZ: poised at the edge of stability. Annu Rev Microbiol 57:125–154. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Katherine AH, Thiago MAS, Gabriella MN, Valerie H, Jared TS, Douglas BW (2016) Targeting the bacterial division protein FtsZ. J Med Chem 59(15):6975–6998. CrossRefGoogle Scholar
  4. 4.
    Haydon DJ, Stokes NR, Ure R, Galbraith G, Bennett JM, Brown DR, Baker PJ, Barynin VV, Rice DW, Sedelnikova SE, Heal JR, Sheridan JM, Aiwale ST, Chauhan PK, Srivastava A, Taneja A, Collins I, Errington J, Czaplewski LG (2008) An inhibitor of FtsZ with potent and selective anti-staphylococcal activity. Science 321:1673–1675. CrossRefPubMedGoogle Scholar
  5. 5.
    Vuong C, Gerke C, Somerville GA, Fischer ER, Otto M (2003) Quorum-sensing control of biofilm factors in Staphylococcus epidermidis. J Infect Dis 188(5):706–718. CrossRefPubMedGoogle Scholar
  6. 6.
    Otto M (2009) Staphylococcus epidermidis—the ‘accidental’ pathogen. Nature. 7:555–567. CrossRefGoogle Scholar
  7. 7.
    Sabaté Brescó M, Harris LG, Thompson K, Stanic B, Morgenstern M, O’Mahony L, Richards RG, Moriarty TF (2017) Pathogenic mechanisms and host interactions in Staphylococcus epidermidis device-related infection. Front Microbiol 8:1–24. CrossRefGoogle Scholar
  8. 8.
    Nguyen TH, Park MD, Otto M (2017) Host response to Staphylococcus epidermidis colonization and infections. Front. Cell. Infect. Microbiol 7:1–7. CrossRefGoogle Scholar
  9. 9.
    Tripathy S, Azam MA, Jupudi S, Sahu SK (2017) Pharmacophore generation, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on benzamide analogs as FtsZ inhibitors. J Biomole Str and Dyna 36(12):3218–3230 CrossRefGoogle Scholar
  10. 10.
    Lipinski CA, Lombardo F, Dominy BW, Feeney P (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25. CrossRefGoogle Scholar
  11. 11.
    Ohtsuki S, Uchida Y, Kubo Y, Terasaki T (2011) Quantitative targeted absolute proteomics-based ADME research as a new path to drug discovery and development: methodology, advantages, strategy, and prospects. J Pharm Sci 100(9):3547–3559. CrossRefPubMedGoogle Scholar
  12. 12.
    Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein-ligand interactions docking and scoring: successes and gaps. J Med Chem 49(20):5851–5855. CrossRefPubMedGoogle Scholar
  13. 13.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49(21):6177–6196. CrossRefPubMedGoogle Scholar
  14. 14.
    Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234. CrossRefPubMedGoogle Scholar
  15. 15.
    Jacobson MP, Pincus DL, Rapp CS, Day TJF, Honig B, Shaw DE, Friesner RA (2004) A hierarchical approach to all-atom protein loop prediction. Proteins 55(2):351–367. CrossRefPubMedGoogle Scholar
  16. 16.
    Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theo and Comput 6(5):1509–1519. CrossRefGoogle Scholar
  17. 17.
    Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99CrossRefGoogle Scholar
  18. 18.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. CrossRefPubMedGoogle Scholar
  19. 19.
    Yu Z, Jacobson MP, Friesner RA (2006) What role do surfaces play in GB models? A new-generation of surface-generalized Born model based on a novel gaussian surface for biomolecules. J Comput Chem 27:72–89. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II The accuracy of ranking poses generated from docking. J Comput Chem 32(5):866–877. CrossRefPubMedGoogle Scholar
  21. 21.
    Wang HY, Cao ZX, Li LL, Jiang PD, Zhao YL, Luo SD, Yang L, Wei YQ, Yang SY (2008) Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors. Bioorg Med Chem Lett 18(18):4972–4977. CrossRefPubMedGoogle Scholar
  22. 22.
    Matysiak J (2007) Evaluation of electronic, lipophilic and membrane affinity effects on antiproliferative activity of 5-substituted-2-(2,4-dihydroxyphenyl)-1,3,4-thiadiazoles against various human cancer cells. Eur J Med Chem 42(7):940–947CrossRefGoogle Scholar
  23. 23.
    Zhenminga D, Hepinga S, Yufanga L, Dianshenga L, Bob L (2011) Experimental and theoretical study of 10-methoxy-2-phenylbenzo[h]quinoline. Spectrochim Acta A 78:1143–1148. CrossRefGoogle Scholar
  24. 24.
    Genc ZK, Tkin S, Sandal S, Sekerci M, Genc M (2014) Synthesis and DFT studies of structural and some spectral parametes of nickel (II) complex with 2-(2-hydroxybenzoyl)-N-(1-adamantyl) hydrazine carbothioamide. Res Chem Intermed 41:4477–4488CrossRefGoogle Scholar
  25. 25.
    Gece G (2009) Quantum chemical study of some cyclic nitrogen compounds as corrosion inhibitors of steel in NaCl media. Corros Sci 51(8):1876–1878. CrossRefGoogle Scholar
  26. 26.
    Lu Y, Wang Y, Zhu W (2010) Nonbonding interactions of organic halogens in biological systems: implications for drug discovery and biomolecular design. Phys Chem Chem Phys 12(18):4543–4551. CrossRefPubMedGoogle Scholar
  27. 27.
    Queiroz AN, Gomes BAQ, Moraes WM, Borges RS (2009) A theoretical antioxidant pharmacophore for resveratrol. Eur J Med Chem 44:1644–1649. CrossRefPubMedGoogle Scholar
  28. 28.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Lyles RH, Poindexter C, Evans A, Brown M, Cooper CR (2008) Nonlinear model-based estimates of IC50 for studies involving continuous therapeutic dose–response data. Contemporary Clinical Trials 29(6):878–886. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ(1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 1998; 19, 1639–1662.<1639::AID-JCC10>3.0.CO;2-B CrossRefGoogle Scholar
  31. 31.
    Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. British J Pharmacol 162(6):1239–1249. CrossRefGoogle Scholar
  32. 32.
    Debnath T, Majumdar S, Kalle A, Aparna V, Debnath S (2015) Identification of potent histone deacetylase 8 inhibitors using pharmacophore-based virtual screening, three-dimensional quantitative structure–activity relationship, and docking study. Res Rep Med Chem 5:21–39. CrossRefGoogle Scholar
  33. 33.
    Dong J, Wang NN, Yao ZJ, Zhang L, Cheng Y, Ouyang D, Lu AP, Cao DS (2018) ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Aust J Chem 10:29. CrossRefGoogle Scholar
  34. 34.
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/ IEEE Conference on Supercomputing (SC06), Tampa, FLGoogle Scholar
  35. 35.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and parametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical ncalucations on peptides. J Phys Chem B 105:6474–6487. CrossRefGoogle Scholar
  36. 36.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. CrossRefGoogle Scholar
  37. 37.
    Shinoda W, Mikami M (2003) Rigid-body dynamics in the isothermal-isobaric ensemble: a test on the accuracy and computational efficiency. J Comput Chem 24(8):920–930. CrossRefPubMedGoogle Scholar
  38. 38.
    Martyna GJ, Klein ML, Tuckerman M (1992) Nose-Hoover chains-the canonical ensemble via continuous dynamics. J Chem Phys 97: 2635–2643. CrossRefGoogle Scholar
  39. 39.
    Martyna GJ, Tobias DJ, Klein ML (1994) Constant-pressure molecular dynamics algorithms. J Chem Phys 101:4177–4189. CrossRefGoogle Scholar
  40. 40.
    Dixon SL, Smondyrev AM, Rao SN (2006) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67(5):370–372. CrossRefPubMedGoogle Scholar
  41. 41.
    Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69(2):146–148. CrossRefPubMedGoogle Scholar
  42. 42.
    Johnson ER, Yan W, Davidson ER (2010) Spin-state splittings, highest occupied molecular orbital and lowest unoccupied molecular orbital energies and chemical hardness. J Chem Phys 133:164107CrossRefGoogle Scholar
  43. 43.
    Sakkiah S, Lee KW (2012) Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors. Acta Pharmacol Sin 33:964–978. CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science. 253(5016):164–170CrossRefGoogle Scholar
  45. 45.
    Lobanov MY, Bogatyreva NS, Galzitskaya OV (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42(4):623–628. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University Department of Pharmaceutical SciencesUtkal UniversityBhubaneswarIndia
  2. 2.Department of Pharmaceutical ChemistryJ.S.S. College of PharmacyUdhagamandalamIndia

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