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image of Multistep In Silico Pipeline for Targeting FimH Adhesin with Chalcones as Anti-Adhesion Therapeutics

Abstract

Introduction

FimH, a bacterial adhesin on (UPEC), facilitates host cell attachment and initiates Urinary Tract Infections (UTIs). With rising antibiotic resistance, alternative therapeutics targeting bacterial adhesion are urgently needed. This study investigates chalcone derivatives as potential anti-adhesive agents against FimH, aiming to inhibit bacterial colonization and reduce virulence through an approach.

Methods

A total of 200 chalcone derivatives were subjected to toxicity screening using ProTox-II, followed by molecular docking using MVD 6.0 and AutoDock Vina against FimH (PDB IDs: 5AAP and 4XO8). The most promising compounds underwent structural pharmacophore modeling in LigandScout, pharmacokinetic profiling SwissADME and PreADMET, and further validation through molecular dynamics simulations and MMPBSA free energy calculations.

Results

Chalcones 103, 122, and 137 showed strong binding affinities, with highly negative MolDock scores surpassing native ligands. Key residues such as Gln133, Asp47, and Phe1 were identified as essential for hydrogen bonding. Pharmacokinetic profiles revealed high gastrointestinal absorption, BBB permeability, and compliance with major drug-likeness filters. RMSF analysis indicated 4XO8’s structural rigidity, while MMPBSA confirmed strong binding energies, particularly for the 4XO8-137 complex.

Discussion

These findings suggest chalcone derivatives, especially Chalcone 137, demonstrate promising anti-adhesive properties, structural stability, and favourable pharmacokinetics, making them viable candidates for further drug development.

Conclusion

Chalcones 103, 122, and 137, particularly the 4XO8-137 complex, exhibit strong therapeutic potential as non-antibiotic anti-adhesion agents against UTI-causing , warranting further experimental validation and .

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2025-10-23
2025-12-16
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References

  1. Van Eyssen S.R. Samarkina A. Isbilen O. Zeden M.S. Volkan E. FimH and type 1 pili mediated tumor cell cytotoxicity by uropathogenic Escherichia coli in vitro. Pathogens 2023 12 6 751 10.3390/pathogens12060751 37375441
    [Google Scholar]
  2. Nasi G.I. Georgakopoulou K.I. Theodoropoulou M.K. Bacterial Lectin FimH and its aggregation hot-spots: An alternative strategy against uropathogenic Escherichia coli. Pharmaceutics 2023 15 3 1018 10.3390/pharmaceutics15031018 36986878
    [Google Scholar]
  3. Qin J. Wilson K.A. Sarkar S. Heras B. O’Mara M.L. Totsika M. Conserved FimH mutations in the global Escherichia coli ST131 multi-drug resistant lineage weaken interdomain interactions and alter adhesin function. Comput. Struct. Biotechnol. J. 2022 20 4532 4541 10.1016/j.csbj.2022.08.040 36090810
    [Google Scholar]
  4. Lopatto E.D.B. Santiago-Borges J.M. Sanick D.A. Monoclonal antibodies targeting the FimH adhesin protect against uropathogenic E. coli UTI. Sci. Adv. 2025 11 25 eadw0698 10.1126/sciadv.adw0698 40540557
    [Google Scholar]
  5. Zhou Z. Tang R. Fang Y. Lv T. Liu J. Wang X. Facile synthesis of FimH antagonist and its analogues: Simple entry to complex C -Mannoside inhibitors of E. coli Adhesion. ACS Med. Chem. Lett. 2024 15 10 1724 1730 10.1021/acsmedchemlett.4c00308 39411527
    [Google Scholar]
  6. Goshisht M.K. Emerging nanomaterial strategies for antibacterial and antibiofilm applications: innovations in bacterial detection, mechanistic understanding, and therapeutic interventions to address antibiotic resistance. Acta Pathol Microbiol Scand Suppl 2025 133 7 70050 10.1111/apm.70050 40667603
    [Google Scholar]
  7. Sarshar M. Behzadi P. Ambrosi C. Zagaglia C. Palamara A.T. Scribano D. FimH and anti-adhesive therapeutics: A disarming strategy against uropathogens. Antibiotics 2020 9 7 397 10.3390/antibiotics9070397 32664222
    [Google Scholar]
  8. Rammohan A. Reddy J.S. Sravya G. Rao C.N. Zyryanov G.V. Chalcone synthesis, properties and medicinal applications: A review. Environ. Chem. Lett. 2020 18 2 433 458 10.1007/s10311‑019‑00959‑w
    [Google Scholar]
  9. Rajendran G. Bhanu D. Aruchamy B. Chalcone: A promising bioactive scaffold in medicinal chemistry. Pharmaceuticals 2022 15 10 1250 10.3390/ph15101250 36297362
    [Google Scholar]
  10. Adhikari S. Nath P. Deb V.K. Pharmacological potential of natural chalcones: A recent studies and future perspective. Front. Pharmacol. 2025 16 1570385 10.3389/fphar.2025.1570385 40599794
    [Google Scholar]
  11. Nematollahi M.H. Mehrabani M. Hozhabri Y. Mirtajaddini M. Iravani S. Antiviral and antimicrobial applications of chalcones and their derivatives: From nature to greener synthesis. Heliyon 2023 9 10 20428 10.1016/j.heliyon.2023.e20428 37810815
    [Google Scholar]
  12. Singha B. Arora B. Karmaker R. Evaluating terrestrol A as an inhibitor against SARS‐CoV‐2 and invasive fungal pathogens: A comprehensive computational analysis. ChemistrySelect 2024 9 14 202304761 10.1002/slct.202304761
    [Google Scholar]
  13. Singha B. Gogoi P.P. Longkumer P. A holistic computational exploration of AZD7762 as a potent selective modulator of LXRα, LXRβ and FXR: An underexplored pathway in cancer therapeutics. Comput. Biol. Med. 2025 194 110433 10.1016/j.compbiomed.2025.110433 40472507
    [Google Scholar]
  14. Dhaliwal J.S. Moshawih S. Goh K.W. Pharmacotherapeutics applications and chemistry of chalcone derivatives. Molecules 2022 27 20 7062 10.3390/molecules27207062 36296655
    [Google Scholar]
  15. Supong A. Chandra Bhomick P. Karmaker R. Sinha D. Bora Sinha U. Synthesis and characterization of brominated activated carbon using a green strategy: Combined experimental and theoretical study. Chem. Phys. Lett. 2024 850 141477 10.1016/j.cplett.2024.141477
    [Google Scholar]
  16. Daina A. Michielin O. Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017 7 1 42717 10.1038/srep42717 28256516
    [Google Scholar]
  17. Frisch M.J. Trucks G.W. Schlegel H.B. Gaussian 09. Wallingford, CT Gaussian Inc 2009
    [Google Scholar]
  18. Dennington R. Keith T. Millam T. GaussView, Version 611. Semichem Inc 2019
    [Google Scholar]
  19. Banerjee P. Kemmler E. Dunkel M. Preissner R. ProTox 3.0: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024 52 W1 W513-20 10.1093/nar/gkae303 38647086
    [Google Scholar]
  20. Singha B. Gogoi P.P. Longkumer P. Boruah N. Sinha U.B. Evaluation of some designed halogenated variants of gentisyl alcohol: molecular docking, DFT, Druglikeness, and ADMET studies for assessing biological properties. J Appl Chem 2024 13 98 113
    [Google Scholar]
  21. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  22. Wolber G. Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 2005 45 1 160 169 10.1021/ci049885e 15667141
    [Google Scholar]
  23. de Ruyck J. Lensink M.F. Bouckaert J. Structures of C -mannosylated anti-adhesives bound to the type 1 fimbrial FimH adhesin. IUCrJ 2016 3 3 163 167 10.1107/S2052252516002487 27158502
    [Google Scholar]
  24. Sauer M.M. Jakob R.P. Eras J. Catch-bond mechanism of the bacterial adhesin FimH. Nat. Commun. 2016 7 1 10738 10.1038/ncomms10738 26948702
    [Google Scholar]
  25. Stillhart C. Vučićević K. Augustijns P. Impact of gastrointestinal physiology on drug absorption in special populations––An UNGAP review. Eur. J. Pharm. Sci. 2020 147 105280 10.1016/j.ejps.2020.105280 32109493
    [Google Scholar]
  26. Cornelissen F.M.G. Markert G. Deutsch G. Explaining blood–brain barrier permeability of small molecules by integrated analysis of different transport mechanisms. J. Med. Chem. 2023 66 11 7253 7267 10.1021/acs.jmedchem.2c01824 37217193
    [Google Scholar]
  27. Segarra M. Aburto M.R. Acker-Palmer A. Blood–brain barrier dynamics to maintain brain homeostasis. Trends Neurosci. 2021 44 5 393 405 10.1016/j.tins.2020.12.002 33423792
    [Google Scholar]
  28. Ahmed Juvale I.I. Abdul Hamid A.A. Abd Halim K.B. Che Has A.T. P-glycoprotein: New insights into structure, physiological function, regulation and alterations in disease. Heliyon 2022 8 6 09777 10.1016/j.heliyon.2022.e09777 35789865
    [Google Scholar]
  29. Abdallah R.M. Hasan H.E. Hammad A. Predictive modeling of skin permeability for molecules: Investigating FDA-approved drug permeability with various AI algorithms. PLOS Digit Health 2024 3 4 0000483 10.1371/journal.pdig.0000483 38568888
    [Google Scholar]
  30. Halder S.K. Elma F. In silico identification of novel chemical compounds with antituberculosis activity for the inhibition of InhA and EthR proteins from Mycobacterium tuberculosis. J. Clin. Tuberc. Other Mycobact. Dis. 2021 24 100246 10.1016/j.jctube.2021.100246 34124395
    [Google Scholar]
  31. Tijjani H. Olatunde A. Adegunloye A.P. Ishola A.A. In silico insight into the interaction of 4-aminoquinolines with selected SARS-CoV-2 structural and nonstructural proteins. Coron Drug Discov 2022 3 313 333 10.1016/B978‑0‑323‑95578‑2.00001‑7
    [Google Scholar]
  32. Lipinski C.A. Lombardo F. Dominy B.W. Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. Adv. Drug Deliv. Rev. 2001 46 1-3 3 26 10.1016/S0169‑409X(00)00129‑0 11259830
    [Google Scholar]
  33. Ghose A.K. Viswanadhan V.N. Wendoloski J.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1999 1 1 55 68 10.1021/cc9800071 10746014
    [Google Scholar]
  34. Kralj S. Jukič M. Bren U. Molecular filters in medicinal chemistry. Encyclopedia 2023 3 2 501 511 10.3390/encyclopedia3020035
    [Google Scholar]
  35. Egan W.J. Merz K.M. Baldwin J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000 43 21 3867 3877 10.1021/jm000292e 11052792
    [Google Scholar]
  36. Martin Y.C. A bioavailability score. J. Med. Chem. 2005 48 9 3164 3170 10.1021/jm0492002 15857122
    [Google Scholar]
  37. Daina A. Zoete V. A BOILED‐Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 2016 11 11 1117 1121 10.1002/cmdc.201600182 27218427
    [Google Scholar]
  38. Hussein H.A. A DFT study of structural-stability, Mulliken charges, MEP, FMO, and NLO properties of trans alkenyl substituted chalcones conformers: Theoretical study. Struct. Chem. 2023 34 6 2201 2223 10.1007/s11224‑023‑02139‑7
    [Google Scholar]
  39. Sharma K. Melavanki R. Hiremath S.M. Kusanur R. Geethanjali HS, D N. Synthesis, spectroscopic characterization, electronic and docking studies on novel chalcone derivatives (3DPP and 5PPD) by experimental and DFT methods. J. Mol. Struct. 2022 1256 132553 10.1016/j.molstruc.2022.132553
    [Google Scholar]
  40. Ojha J.K. Ramesh G. Reddy B.V. Structure, chemical reactivity, NBO, MEP analysis and thermodynamic parameters of pentamethyl benzene using DFT study. Chemical Physics Impact 2023 7 100280 10.1016/j.chphi.2023.100280
    [Google Scholar]
  41. Lindorff-Larsen K. Piana S. Palmo K. Improved side‐chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010 78 8 1950 1958 10.1002/prot.22711 20408171
    [Google Scholar]
  42. Sousa da Silva A.W. Vranken W.F. ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res. Notes 2012 5 1 367 10.1186/1756‑0500‑5‑367 22824207
    [Google Scholar]
  43. Berendsen H.J.C. van der Spoel D. van Drunen R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995 91 1-3 43 56 10.1016/0010‑4655(95)00042‑E
    [Google Scholar]
  44. Lindahl E. Hess B. van der Spoel D. GROMACS 3.0: A package for molecular simulation and trajectory analysis. J. Mol. Model. 2001 7 8 306 317 10.1007/s008940100045
    [Google Scholar]
  45. Van Der Spoel D. Lindahl E. Hess B. Groenhof G. Mark A.E. Berendsen H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005 26 16 1701 1718 10.1002/jcc.20291 16211538
    [Google Scholar]
  46. Hess B. Kutzner C. van der Spoel D. Lindahl E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008 4 3 435 447 10.1021/ct700301q 26620784
    [Google Scholar]
  47. Abraham M.J. Murtola T. Schulz R. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015 1-2 19 25 10.1016/j.softx.2015.06.001
    [Google Scholar]
  48. Massova I. Kollman P.A. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des. 2000 18 1 113 135 10.1023/A:1008763014207
    [Google Scholar]
  49. Perutz M.F. Electrostatic effects in proteins. Science 1978 201 4362 1187 1191 10.1126/science.694508 694508
    [Google Scholar]
  50. Davis M.E. McCammon J.A. Electrostatics in biomolecular structure and dynamics. Chem. Rev. 1990 90 3 509 521 10.1021/cr00101a005
    [Google Scholar]
  51. Honig B. Nicholls A. Classical electrostatics in biology and chemistry. Science 1995 268 5214 1144 1149 10.1126/science.7761829 7761829
    [Google Scholar]
  52. Baker N.A. Sept D. Joseph S. Holst M.J. McCammon J.A. Electrostatics of nanosystems: Application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 2001 98 18 10037 10041 10.1073/pnas.181342398 11517324
    [Google Scholar]
  53. Kumari R. Kumar R. Lynn A. g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 2014 54 7 1951 1962 10.1021/ci500020m 24850022
    [Google Scholar]
  54. Pettersen E.F. Goddard T.D. Huang C.C. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004 25 13 1605 1612 10.1002/jcc.20084 15264254
    [Google Scholar]
  55. Abdullahi S.H. Moin A.T. Uzairu A. Molecular docking studies of some benzoxazole and benzothiazole derivatives as VEGFR-2 target inhibitors: In silico design, MD simulation, pharmacokinetics and DFT studies. Intelligent Pharmacy 2024 2 2 232 250 10.1016/j.ipha.2023.11.010
    [Google Scholar]
  56. Febrina E. Asnawi A. Lead compound discovery using pharmacophore-based models of small-molecule metabolites from human blood as inhibitor cellular entry of SARS-CoV-2. J. Pharm. Pharmacogn. Res. 2023 11 5 810 822 10.56499/jppres23.1688_11.5.810
    [Google Scholar]
  57. Pradeep S. Sai Chakith M.R. Sindhushree S.R. Exploring shared therapeutic targets for Alzheimer’s disease and glioblastoma using network pharmacology and protein-protein interaction approach. Front Chem. 2025 13 1549186 10.3389/fchem.2025.1549186 40144222
    [Google Scholar]
  58. Storchmannová K. Balouch M. Juračka J. Štěpánek F. Berka K. Meta-analysis of permeability literature data shows possibilities and limitations of popular methods. Mol. Pharm. 2025 22 3 1293 1304 10.1021/acs.molpharmaceut.4c00975 39977255
    [Google Scholar]
  59. Sheena Mary Y. Shyma Mary Y. Krátký M. Vinsova J. Baraldi C. Gamberini M.C. DFT, molecular docking and SERS (concentration and solvent dependant) investigations of a methylisoxazole derivative with potential antimicrobial activity. J. Mol. Struct. 2021 1232 130034 10.1016/j.molstruc.2021.130034
    [Google Scholar]
  60. Akbari Z. Stagno C. Iraci N. Biological evaluation, DFT, MEP, HOMO-LUMO analysis and ensemble docking studies of Zn(II) complexes of bidentate and tetradentate Schiff base ligands as antileukemia agents. J. Mol. Struct. 2024 1301 137400 10.1016/j.molstruc.2023.137400
    [Google Scholar]
  61. Trezza A. Visibelli A. Roncaglia B. Unveiling dynamic hotspots in protein–ligand binding: Accelerating target and drug discovery approaches. Int. J. Mol. Sci. 2025 26 9 3971 10.3390/ijms26093971 40362212
    [Google Scholar]
  62. Wang C. Nguyen P.H. Pham K. Calculating protein–ligand binding affinities with MMPBSA: Method and error analysis. J. Comput. Chem. 2016 37 27 2436 2446 10.1002/jcc.24467 27510546
    [Google Scholar]
  63. Cong Y. Li M. Feng G. Li Y. Wang X. Duan L. Trypsin-Ligand binding affinities calculated using an effective interaction entropy method under polarized force field. Sci. Rep. 2017 7 1 17708 10.1038/s41598‑017‑17868‑z 29255159
    [Google Scholar]
  64. Interlandi G. Rate limiting step of the allosteric activation of the bacterial adhesin FimH investigated by molecular dynamics simulations. Proteins 2024 92 1 117 133 10.1002/prot.26588 37700555
    [Google Scholar]
  65. Jena S. Dutta J. Tulsiyan K.D. Sahu A.K. Choudhury S.S. Biswal H.S. Noncovalent interactions in proteins and nucleic acids: beyond hydrogen bonding and π-stacking. Chem. Soc. Rev. 2022 51 11 4261 4286 10.1039/D2CS00133K 35560317
    [Google Scholar]
  66. Kullmann R. Delbianco M. Roth C. Weikl T.R. Role of van der Waals, electrostatic, and hydrogen-bond interactions for the relative stability of cellulose Iβ and II crystals. J. Phys. Chem. B 2024 128 49 12114 12121 10.1021/acs.jpcb.4c06841 39589929
    [Google Scholar]
  67. Ayar A. Aksahin M. Mesci S. Yazgan B. Gül M. Yıldırım T. Antioxidant, cytotoxic activity and pharmacokinetic studies by Swiss Adme, Molinspiration, Osiris and DFT of PhTAD-substituted dihydropyrrole derivatives. Curr. Computeraided Drug Des. 2022 18 1 52 63 10.2174/1573409917666210223105722 33622227
    [Google Scholar]
  68. Tripathi N. Goshisht M.K. Sahu S.K. Arora C. Applications of artificial intelligence to drug design and discovery in the big data era: A comprehensive review. Mol. Divers. 2021 25 3 1643 1664 10.1007/s11030‑021‑10237‑z 34110579
    [Google Scholar]
  69. Osman M.S. Awad T.A. Shantier S.W. Identification of some chalcone analogues as potential antileishmanial agents: An integrated in vitro and in silico evaluation. Arab. J. Chem. 2022 15 4 103717 10.1016/j.arabjc.2022.103717
    [Google Scholar]
  70. Thillainayagam M. Pandian L. Murugan K.K. In silico analysis reveals the anti-malarial potential of quinolinyl chalcone derivatives. J. Biomol. Struct. Dyn. 2015 33 5 961 977 10.1080/07391102.2014.920277 24871811
    [Google Scholar]
  71. Pola S. Banoth K.K. Sankaranarayanan M. Ummani R. Garlapati A. Design, synthesis, in silico studies, and evaluation of novel chalcones and their pyrazoline derivatives for antibacterial and antitubercular activities. Med. Chem. Res. 2020 29 10 1819 1835 10.1007/s00044‑020‑02602‑8
    [Google Scholar]
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