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Journal of molecular modeling
Mar 17, 2025;31 (4): 119.

Structural insights into molecular and cellular level FXR binding potentials of GW4064 and LY2562175 hybrids by multi in silico modelling analyses.

Tanmoy Banerjee, Soumya Mitra, Shuvam Sar, Amit Kumar Halder, Parthasarathi Panda, Nilanjan Ghosh
Author Information
  1. Tanmoy Banerjee: Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
  2. Soumya Mitra: Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
  3. Shuvam Sar: Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
  4. Amit Kumar Halder: Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, 713206, India.
  5. Parthasarathi Panda: Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, 713206, India.
  6. Nilanjan Ghosh: Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India. nilanjanghosh.phamacy@jadavpuruniversity.in.
PMID: 40095214 DOI: 10.1007/s00894-025-06336-5

Abstract

CONTEXT: Non-alcoholic fatty liver disease (NAFLD) has become a significant health concern. Existing farnesoid X receptor (FXR) agonists like GW4064 and LYS2562175 show poor pharmacokinetics, prompting researchers to develop alternative molecules. This study aims to pinpoint the structural features responsible for exhibiting FXR agonism of a series of hybrid structures of GW4064 and LYS2562175 with improved pharmacokinetic properties which supersede the existing parent ligands. Electronegative components were found to critically influence biological activity on the molecular level, supported by 2D- and 3D-Quantitative Structure Activity Relationship (2D- and 3D-QSAR) analyses. Quantitative Activity-Activity Relationship (QAAR) highlighted key descriptors impacting cellular level FXR binding potential. Molecular dynamics (MD) simulations identified pivotal interactions, such as π-π and H-bond interactions with key residues. Furthermore, binding free energy calculated with Molecular Mechanics with Generalised Born and Surface Area solvation (MM-GBSA) analyses with selected compounds reflected the variations in their binding potential towards FXR protein.
METHODS: The study began by curating ligand SMILES and preparing a dataset with molecular and cellular activity as dependent variables. AlvaDesc descriptors and interpretable descriptors were calculated using the OCHEM webserver. QSAR analyses were performed using Sequential Forward Selection (SFS) and Genetic Algorithm (GA) methods, while QAAR analysis used 50% effective concentration at the molecular level as an independent variable with the same algorithms. 3D QSAR analysis was performed with the Open3DQSAR tool. Docking studies in AutoDock 4.2 with FXR protein identified optimal ligand poses, and 500 ns MD simulations were performed with Amber 20. The use of open-access tools ensures reproducibility and accessibility for future research.

Keywords

2D-QSAR 3D-QSAR Farnesoid X receptor Molecular dynamics simulation Non-alcoholic fatty liver disease

References

  1. Bansal S, Vachher M, Arora T, Kumar B, Burman A (2023) Visceral fat: a key mediator of NAFLD development and progression. Human Nutrition & Metabolism 33:200210. https://doi.org/10.1016/j.hnm.2023.200210 .
  2. Klop B, Elte JW, Cabezas MC (2013) Dyslipidemia in obesity: mechanisms and potential targets. Nutrients 5:1218–1240. https://doi.org/10.3390/nu5041218 [DOI: 10.3390/nu5041218]
  3. Abd El-Kader SM, El-Den Ashmawy EM (2015) Non-alcoholic fatty liver disease: the diagnosis and management. World J Hepatol 7:846–858. https://doi.org/10.4254/wjh.v7.i6.846 [DOI: 10.4254/wjh.v7.i6.846]
  4. Sanders FW, Griffin JL (2016) De novo lipogenesis in the liver in health and disease: more than just a shunting yard for glucose. Biol Rev Camb Philos Soc 91:452–468. https://doi.org/10.1111/brv.12178 [DOI: 10.1111/brv.12178]
  5. Berardo C, Di Pasqua LG, Cagna M, Richelmi P, Vairetti M, Ferrigno A (2020) Nonalcoholic fatty liver disease and non-alcoholic steatohepatitis: current issues and future perspectives in preclinical and clinical research. Int J Mol Sci 21. https://doi.org/10.3390/ijms21249646 .
  6. Masarone M, Rosato V, Dallio M, Gravina AG, Aglitti A, Loguercio C et al (2018) Role of oxidative stress in pathophysiology of nonalcoholic fatty liver disease. Oxid Med Cell Longev 2018:9547613. https://doi.org/10.1155/2018/9547613 [DOI: 10.1155/2018/9547613]
  7. Zoller H, Tilg H (2016) Nonalcoholic fatty liver disease and hepatocellular carcinoma. Metabolism 65:1151–1160. https://doi.org/10.1016/j.metabol.2016.01.010 [DOI: 10.1016/j.metabol.2016.01.010]
  8. Mazuy C, Helleboid A, Staels B, Lefebvre P (2015) Nuclear bile acid signaling through the farnesoid X receptor. Cell Mol Life Sci 72:1631–1650. https://doi.org/10.1007/s00018-014-1805-y [DOI: 10.1007/s00018-014-1805-y]
  9. Panzitt K, Wagner M (2021) FXR in liver physiology: multiple faces to regulate liver metabolism. Biochim Biophys Acta Mol Basis Dis 1867:166133. https://doi.org/10.1016/j.bbadis.2021.166133 [DOI: 10.1016/j.bbadis.2021.166133]
  10. Fang Y, Hegazy L, Finck BN, Elgendy B (2021) Recent advances in the medicinal chemistry of farnesoid X receptor. J Med Chem 64:17545–17571. https://doi.org/10.1021/acs.jmedchem.1c01017 [DOI: 10.1021/acs.jmedchem.1c01017]
  11. Jiang L, Zhang H, Xiao D, Wei H, Chen Y (2021) Farnesoid X receptor (FXR): structures and ligands. Comput Struct Biotechnol J 19:2148–2159. https://doi.org/10.1016/j.csbj.2021.04.029 [DOI: 10.1016/j.csbj.2021.04.029]
  12. Radun R, Trauner M (2021) Role of FXR in bile acid and metabolic homeostasis in NASH: pathogenetic concepts and therapeutic opportunities. Semin Liver Dis 41:461–475. https://doi.org/10.1055/s-0041-1731707 [DOI: 10.1055/s-0041-1731707]
  13. Zhang Y, Jackson JP, St Claire RL, 3rd, Freeman K, Brouwer KR, Edwards JE (2017) Obeticholic acid, a selective farnesoid X receptor agonist, regulates bile acid homeostasis in sandwich-cultured human hepatocytes. Pharmacol Res Perspect 5. https://doi.org/10.1002/prp2.329 .
  14. Jiao Y, Lu Y, Li XY (2015) Farnesoid X receptor: a master regulator of hepatic triglyceride and glucose homeostasis. Acta Pharmacol Sin 36:44–50. https://doi.org/10.1038/aps.2014.116 [DOI: 10.1038/aps.2014.116]
  15. Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ (2018) Mechanisms of NAFLD development and therapeutic strategies. Nat Med 24:908–922. https://doi.org/10.1038/s41591-018-0104-9 [DOI: 10.1038/s41591-018-0104-9]
  16. Feng S, Yang M, Zhang Z, Wang Z, Hong D, Richter H et al (2009) Identification of an N-oxide pyridine GW4064 analog as a potent FXR agonist. Bioorg Med Chem Lett 19:2595–2598. https://doi.org/10.1016/j.bmcl.2009.03.008 [DOI: 10.1016/j.bmcl.2009.03.008]
  17. Smalley TL Jr, Boggs S, Caravella JA, Chen L, Creech KL, Deaton DN et al (2015) Novel heterocyclic scaffolds of GW4064 as farnesoid X receptor agonists. Bioorg Med Chem Lett 25:280–284. https://doi.org/10.1016/j.bmcl.2014.11.050 [DOI: 10.1016/j.bmcl.2014.11.050]
  18. Akwabi-Ameyaw A, Caravella JA, Chen L, Creech KL, Deaton DN, Madauss KP et al (2011) Conformationally constrained farnesoid X receptor (FXR) agonists: alternative replacements of the stilbene. Bioorg Med Chem Lett 21:6154–6160. https://doi.org/10.1016/j.bmcl.2011.08.034 [DOI: 10.1016/j.bmcl.2011.08.034]
  19. Abel U, Schluter T, Schulz A, Hambruch E, Steeneck C, Hornberger M et al (2010) Synthesis and pharmacological validation of a novel series of non-steroidal FXR agonists. Bioorg Med Chem Lett 20:4911–4917. https://doi.org/10.1016/j.bmcl.2010.06.084 [DOI: 10.1016/j.bmcl.2010.06.084]
  20. Bass JY, Caldwell RD, Caravella JA, Chen L, Creech KL, Deaton DN et al (2009) Substituted isoxazole analogs of farnesoid X receptor (FXR) agonist GW4064. Bioorg Med Chem Lett 19:2969–2973. https://doi.org/10.1016/j.bmcl.2009.04.047 [DOI: 10.1016/j.bmcl.2009.04.047]
  21. Li J, Liu M, Li Y, Sun DD, Shu Z, Tan Q et al (2020) Discovery and optimization of non-bile acid FXR agonists as preclinical candidates for the treatment of nonalcoholic steatohepatitis. J Med Chem 63:12748–12772. https://doi.org/10.1021/acs.jmedchem.0c01065 [DOI: 10.1021/acs.jmedchem.0c01065]
  22. Bhardwaj V, Singh R, Singh P, Purohit R, Kumar S (2020) Elimination of bitter-off taste of stevioside through structure modification and computational interventions. J Theor Biol 486:110094. https://doi.org/10.1016/j.jtbi.2019.110094 [DOI: 10.1016/j.jtbi.2019.110094]
  23. Amin SA, Trivedi P, Adhikari N, Routholla G, Vijayasarathi D, Das S et al (2021) Quantitative activity–activity relationship (QAAR) driven design to develop hydroxamate derivatives of pentanoic acids as selective HDAC8 inhibitors: synthesis, biological evaluation and binding mode of interaction studies. New J Chem 45:17149–17162. https://doi.org/10.1039/D1NJ02636D [DOI: 10.1039/D1NJ02636D]
  24. Singh R, Bhardwaj VK, Purohit R (2022) Inhibition of nonstructural protein 15 of SARS-CoV-2 by golden spice: a computational insight. Cell Biochem Funct 40:926–934. https://doi.org/10.1002/cbf.3753 [DOI: 10.1002/cbf.3753]
  25. Singh R, Bhardwaj VK, Sharma J, Das P, Purohit R (2022) Identification of selective cyclin-dependent kinase 2 inhibitor from the library of pyrrolone-fused benzosuberene compounds: an in silico exploration. J Biomol Struct Dyn 40:7693–7701. https://doi.org/10.1080/07391102.2021.1900918 [DOI: 10.1080/07391102.2021.1900918]
  26. Bhardwaj VK, Purohit R (2023) A comparative study on inclusion complex formation between formononetin and beta-cyclodextrin derivatives through multiscale classical and umbrella sampling simulations. Carbohydr Polym 310:120729. https://doi.org/10.1016/j.carbpol.2023.120729 [DOI: 10.1016/j.carbpol.2023.120729]
  27. ACD/Labs (2021) ChemSketch. 2021.1.2 ed. Toronto, ON, Canada: advanced chemistry development, IAL. https://www.acdlabs.com/resources/free-chemistry-software-apps/chemsketch-freeware/ . Accessed 20 Nov 2024
  28. Mauri A (2020) alvaDesc: a tool to calculate and analyze molecular descriptors and fingerprints. In: Roy K, editor. Ecotoxicological QSARs. New York, NY: Springer US, pp 801–20. https://doi.org/10.1007/978-1-0716-0150-1_32
  29. Sushko I, Pandey AK, Novotarskyi S, Körner R, Rupp M, Teetz W et al (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. Journal of Cheminformatics 3:P20. https://doi.org/10.1186/1758-2946-3-S1-P20 [DOI: 10.1186/1758-2946-3-S1-P20]
  30. Menezes F, Popowicz GM (2022) ULYSSES: an efficient and easy to use semiempirical library for C+. J Chem Inf Model 62:3685–3694. https://doi.org/10.1021/acs.jcim.2c00757 [DOI: 10.1021/acs.jcim.2c00757]
  31. Eklund M, Norinder U, Boyer S, Carlsson L (2014) Choosing feature selection and learning algorithms in QSAR. J Chem Inf Model 54:837–843. https://doi.org/10.1021/ci400573c [DOI: 10.1021/ci400573c]
  32. Marcano-Cedeño A, Quintanilla-Domínguez J, Cortina-Januchs MG, Andina D  (2010) Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. IECON 2010–36th Annu Conf IEEE Ind Electron Soc 7–10  https://doi.org/10.1109/IECON.2010.5675075
  33. Mitra S, Halder AK, Koley A, Ghosh N, Panda P, Mandal SC et al (2024) Unveiling structural determinants for FXR antagonism in 1,3,4-trisubstituted-Pyrazol amide derivatives: a multi-scale in silico modelling approach. Comput Biol Med 180:108991. https://doi.org/10.1016/j.compbiomed.2024.108991 [DOI: 10.1016/j.compbiomed.2024.108991]
  34. Leardi R, Boggia R, Terrile M (1992) Genetic algorithms as a strategy for feature selection. J Chemometr 6:267–81. https://doi.org/10.1002/cem.1180060506 .
  35. Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K (2015) “NanoBRIDGES” software: open access tools to perform QSAR and nano-QSAR modeling. Chemometr Intell Lab Syst 147:1–13. https://doi.org/10.1016/j.chemolab.2015.07.007 .
  36. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488. https://doi.org/10.1002/minf.201000061 [DOI: 10.1002/minf.201000061]
  37. Tetko IV, Tanchuk VY, Villa AE (2001) Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices. J Chem Inf Comput Sci 41:1407–1421. https://doi.org/10.1021/ci010368v [DOI: 10.1021/ci010368v]
  38. Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276. https://doi.org/10.1016/s1093-3263(01)00123-1 [DOI: 10.1016/s1093-3263(01)00123-1]
  39. Pratim Roy P, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. https://doi.org/10.3390/molecules14051660 [DOI: 10.3390/molecules14051660]
  40. Gramatica P (2013) On the development and validation of QSAR models. Methods Mol Biol 930:499–526. https://doi.org/10.1007/978-1-62703-059-5_21 [DOI: 10.1007/978-1-62703-059-5_21]
  41. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701. https://doi.org/10.1002/qsar.200610151 .
  42. Yoo W, Mayberry R, Bae S, Singh K, Peter He Q, Lillard JW Jr (2014) A study of effects of multicollinearity in the multivariable analysis. Int J Appl Sci Technol 4:9–19 [PMID: 25664257]
  43. Rucker C, Rucker G, Meringer M (2007) y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–2357. https://doi.org/10.1021/ci700157b [DOI: 10.1021/ci700157b]
  44. Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab Syst 145:22–9. https://doi.org/10.1016/j.chemolab.2015.04.013 .
  45. Tosco P, Balle T, Shiri F (2011) Open3DALIGN: an open-source software aimed at unsupervised ligand alignment. J Comput Aided Mol Des 25:777–783. https://doi.org/10.1007/s10822-011-9462-9 [DOI: 10.1007/s10822-011-9462-9]
  46. Tosco P, Balle T (2011) Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17:201–208. https://doi.org/10.1007/s00894-010-0684-x [DOI: 10.1007/s00894-010-0684-x]
  47. Mitra S, Chatterjee S, Bose S, Panda P, Basak S, Ghosh N et al (2024) Finding structural requirements of structurally diverse alpha-glucosidase and alpha-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses. J Mol Graph Model 126:108640. https://doi.org/10.1016/j.jmgm.2023.108640 [DOI: 10.1016/j.jmgm.2023.108640]
  48. Potemkin AV, Grishina MA, Potemkin VA (2017) Grid-based continual analysis of molecular interior for drug discovery, QSAR and QSPR. Curr Drug Discov Technol 14:181–205. https://doi.org/10.2174/1570163814666170207144018 [DOI: 10.2174/1570163814666170207144018]
  49. Akwabi-Ameyaw A, Bass JY, Caldwell RD, Caravella JA, Chen L, Creech KL et al (2008) Conformationally constrained farnesoid X receptor (FXR) agonists: naphthoic acid-based analogs of GW 4064. Bioorg Med Chem Lett 18:4339–4343. https://doi.org/10.1016/j.bmcl.2008.06.073 [DOI: 10.1016/j.bmcl.2008.06.073]
  50. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256 [DOI: 10.1002/jcc.21256]
  51. Halder AK, Cordeiro M (2021) Multi-target in silico prediction of inhibitors for mitogen-activated protein kinase-interacting kinases. Biomolecules 11. https://doi.org/10.3390/biom11111670 .
  52. Halder AK, Honarparvar B (2019) Molecular alteration in drug susceptibility against subtype B and C-SA HIV-1 proteases: MD study. Struct Chem 30:1715–1727. https://doi.org/10.1007/s11224-019-01305-0 [DOI: 10.1007/s11224-019-01305-0]
  53. Mitra S, Halder AK, Ghosh N, Mandal SC, Cordeiro M (2023) Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD. Comput Biol Med 157:106789. https://doi.org/10.1016/j.compbiomed.2023.106789 [DOI: 10.1016/j.compbiomed.2023.106789]
  54. Dolinsky TJ, Czodrowski P, Li H, Nielsen JE, Jensen JH, Klebe G et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:W522–W525. https://doi.org/10.1093/nar/gkm276 [DOI: 10.1093/nar/gkm276]
  55. Roe DR, Cheatham TE 3rd (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9:3084–3095. https://doi.org/10.1021/ct400341p [DOI: 10.1021/ct400341p]
  56. Chong LT, Pitera JW, Swope WC, Pande VS (2009) Comparison of computational approaches for predicting the effects of missense mutations on p53 function. J Mol Graph Model 27:978–982. https://doi.org/10.1016/j.jmgm.2008.12.006 [DOI: 10.1016/j.jmgm.2008.12.006]
  57. Roy K, Kar S, Das RN (2015) Chapter 4 - Topological QSAR. In: Roy K, Kar S, Das RN (eds) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, Boston, pp 103–149 [DOI: 10.1016/B978-0-12-801505-6.00004-1]
  58. Todeschini R, Consonni V, Mannhold R, Kubinyi H, Folkers G (2009) Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, References. Wiley, Weinheim
  59. Todeschini R, Consonni V, Mannhold R, Kubinyi H, Timmerman H (2008) Handbook of Molecular Descriptors. Wiley, Place
  60. Tian T, Li S, Fang M, Zhao D, Zeng J (2024) MolSHAP: interpreting quantitative structure-activity relationships using Shapley values of R-groups. J Chem Inf Model 64:2236–2249. https://doi.org/10.1021/acs.jcim.3c00465 [DOI: 10.1021/acs.jcim.3c00465]

Grants

  1. Vide Memo. 2027 (Sanc.)/STBT-11012 (19)/ 6/2023-ST SEC, dated 24-01-2024/Department of Science and Technology and Biotechnology, Govt. of West Bengal, India
  2. Vide Memo. 2027 (Sanc.)/STBT-11012 (19)/ 6/2023-ST SEC, dated 24-01-2024/Department of Science and Technology and Biotechnology, Govt. of West Bengal, India
  3. Vide Memo. 2027 (Sanc.)/STBT-11012 (19)/ 6/2023-ST SEC, dated 24-01-2024/Department of Science and Technology and Biotechnology, Govt. of West Bengal, India
  4. Vide Memo. 2027 (Sanc.)/STBT-11012 (19)/ 6/2023-ST SEC, dated 24-01-2024/Department of Science and Technology and Biotechnology, Govt. of West Bengal, India
  5. Vide Memo. 2027 (Sanc.)/STBT-11012 (19)/ 6/2023-ST SEC, dated 24-01-2024/Department of Science and Technology and Biotechnology, Govt. of West Bengal, India
  6. Vide Memo. 2027 (Sanc.)/STBT-11012 (19)/ 6/2023-ST SEC, dated 24-01-2024/Department of Science and Technology and Biotechnology, Govt. of West Bengal, India

MeSH Term

Receptors, Cytoplasmic and Nuclear
Quantitative Structure-Activity Relationship
Molecular Dynamics Simulation
Isoxazoles
Humans
Ligands
Protein Binding
Molecular Docking Simulation
Binding Sites

Chemicals

Receptors, Cytoplasmic and Nuclear
farnesoid X-activated receptor
Isoxazoles
GW 4064
Ligands
Journal Article

Word Cloud

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