Mechanism of antagonist ligand binding to REV-ERBα.

Journal

Scientific reports

ISSN: 2045-2322

Titre abrégé: Sci Rep

Pays: England

ID NLM: 101563288

Informations de publication

Date de publication:
10 Apr 2024

Historique:

received: 11 01 2024

accepted: 04 04 2024

medline: 11 4 2024

pubmed: 11 4 2024

entrez: 10 4 2024

Statut: epublish

Résumé

REV-ERBα, a therapeutically promising nuclear hormone receptor, plays a crucial role in regulating various physiological processes such as the circadian clock, inflammation, and metabolism. However, the availability of chemical probes to investigate the pharmacology of this receptor is limited, with SR8278 being the only identified synthetic antagonist. Moreover, no X-ray crystal structures are currently available that demonstrate the binding of REV-ERBα to antagonist ligands. This lack of structural information impedes the development of targeted therapeutics. To address this issue, we employed Gaussian accelerated molecular dynamics (GaMD) simulations to investigate the binding pathway of SR8278 to REV-ERBα. For comparison, we also used GaMD to observe the ligand binding process of STL1267, for which an X-ray structure is available. GaMD simulations successfully captured the binding of both ligands to the receptor's orthosteric site and predicted the ligand binding pathway and important amino acid residues involved in the antagonist SR8278 binding. This study highlights the effectiveness of GaMD in investigating protein-ligand interactions, particularly in the context of drug recognition for nuclear hormone receptors.

Identifiants

DOI: 10.1038/s41598-024-58945-4 PMID: 38600172

pubmed: 38600172

doi: 10.1038/s41598-024-58945-4

pii: 10.1038/s41598-024-58945-4

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

8401

Subventions

Organisme : NINDS NIH HHS

ID : R01NS126204

Pays : United States

Informations de copyright

Références

Mangelsdorf, D. J. et al. The nuclear receptor superfamily: The second decade. Cell 83, 835–839. https://doi.org/10.1016/0092-8674(95)90199-x (1995).

doi: 10.1016/0092-8674(95)90199-x pubmed: 8521507 pmcid: 6159888

Bookout, A. L. et al. Anatomical profiling of nuclear receptor expression reveals a hierarchical transcriptional network. Cell 126, 789–799. https://doi.org/10.1016/j.cell.2006.06.049 (2006).

doi: 10.1016/j.cell.2006.06.049 pubmed: 16923397 pmcid: 6211849

Burris, T. P. et al. International union of basic and clinical pharmacology CXIII: Nuclear receptor superfamily—Update 2023. Pharmacol. Rev. 75, 1233–1318. https://doi.org/10.1124/pharmrev.121.000436 (2023).

doi: 10.1124/pharmrev.121.000436 pubmed: 37586884 pmcid: 10595025

Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34. https://doi.org/10.1038/nrd.2016.230 (2017).

doi: 10.1038/nrd.2016.230 pubmed: 27910877

Schulman, I. G. Nuclear receptors as drug targets for metabolic disease. Adv. Drug Deliv. Rev. 62, 1307–1315. https://doi.org/10.1016/j.addr.2010.07.002.Nuclear (2011).

doi: 10.1016/j.addr.2010.07.002.Nuclear

Burris, T. P. et al. Nuclear receptors and their selective pharmacologic modulators. Pharmacol. Rev. 65, 710–778. https://doi.org/10.1124/pr.112.006833 (2013).

doi: 10.1124/pr.112.006833 pubmed: 23457206

Yin, K. & Smith, A. G. Nuclear receptor function in skin health and disease: Therapeutic opportunities in the orphan and adopted receptor classes. Cell. Mol. Life Sci. 73, 3789–3800 (2016).

doi: 10.1007/s00018-016-2329-4 pubmed: 27544210

Novac, N. & Heinzel, T. Nuclear receptors: Overview and classification. Curr. Drug Targets Inflamm. Allergy 3, 335–346. https://doi.org/10.2174/1568010042634541 (2004).

doi: 10.2174/1568010042634541 pubmed: 15584884

Meijer, F. A., Leijten-van de Gevel, I. A., de Vries, R. M. J. M. & Brunsveld, L. Allosteric small molecule modulators of nuclear receptors. Mol. Cell. Endocrinol. 485, 20–34. https://doi.org/10.1016/j.mce.2019.01.022 (2019).

doi: 10.1016/j.mce.2019.01.022 pubmed: 30703487

Helsen, C. & Claessens, F. Looking at nuclear receptors from a new angle. Mol. Cell. Endocrinol. 382, 97–106. https://doi.org/10.1016/j.mce.2013.09.009 (2014).

doi: 10.1016/j.mce.2013.09.009 pubmed: 24055275

Jin, L. & Li, Y. Structural and functional insights into nuclear receptor signaling. Adv. Drug Deliv. Rev. 62, 1218–1226. https://doi.org/10.1016/j.addr.2010.08.007 (2010).

doi: 10.1016/j.addr.2010.08.007 pubmed: 20723571 pmcid: 2991384

Rastinejad, F., Huang, P., Chandra, V. & Khorasanizadeh, S. Understanding nuclear receptor form and function using structural biology. J. Mol. Endocrinol. 51, T1–T21. https://doi.org/10.1530/JME-13-0173 (2013).

doi: 10.1530/JME-13-0173 pubmed: 24103914 pmcid: 3871882

Moras, D. & Gronemeyer, H. The nuclear receptor ligand-binding domain: Structure and function. Curr. Opin. Cell Biol. 10, 384–391. https://doi.org/10.1016/S0955-0674(98)80015-X (1998).

doi: 10.1016/S0955-0674(98)80015-X pubmed: 9640540

Hunter, A. L. et al. Nuclear receptor REVERBα is a state-dependent regulator of liver energy metabolism. Proc. Natl. Acad. Sci. 117, 25869–25879. https://doi.org/10.1073/pnas.2005330117 (2020).

doi: 10.1073/pnas.2005330117 pubmed: 32989157 pmcid: 7568238

Duez, H. & Staels, B. Rev-erb-alpha: An integrator of circadian rhythms and metabolism. J. Appl. Physiol. 107, 1972–1980. https://doi.org/10.1152/japplphysiol.00570.2009 (2009).

doi: 10.1152/japplphysiol.00570.2009 pubmed: 19696364

Pourcet, B. et al. Nuclear receptor subfamily 1 group D member 1 regulates circadian activity of NLRP3 inflammasome to reduce the severity of fulminant hepatitis in mice. Gastroenterology 154, 1449-1464.e20. https://doi.org/10.1053/j.gastro.2017.12.019 (2018).

doi: 10.1053/j.gastro.2017.12.019 pubmed: 29277561

Duez, H. & Pourcet, B. Nuclear receptors in the control of the NLRP3 inflammasome pathway. Front. Endocrinol. (Lausanne) 12, 630536. https://doi.org/10.3389/fendo.2021.630536 (2021).

doi: 10.3389/fendo.2021.630536 pubmed: 33716981

Makhija, S. et al. REV-ERB activation as a novel pharmacological approach for treating inflammatory pain. Front. Pharmacol. https://doi.org/10.3389/fphar.2023.1171931 (2023).

doi: 10.3389/fphar.2023.1171931 pubmed: 37153791 pmcid: 10154555

Kojetin, D., Wang, Y., Kamenecka, T. M. & Burris, T. P. Identification of SR8278, a synthetic antagonist of the nuclear heme receptor REV-ERB. ACS Chem. Biol. 6, 131–134. https://doi.org/10.1021/cb1002575 (2011).

doi: 10.1021/cb1002575 pubmed: 21043485

Welch, R. D., Billon, C., Valfort, A.-C., Burris, T. P. & Flaveny, C. A. Pharmacological inhibition of REV-ERB stimulates differentiation, inhibits turnover and reduces fibrosis in dystrophic muscle. Sci. Rep. 7, 17142. https://doi.org/10.1038/s41598-017-17496-7 (2017).

doi: 10.1038/s41598-017-17496-7 pubmed: 29215066 pmcid: 5719458

Hegazy, L. et al. Identification of novel mitochondrial pyruvate carrier inhibitors by homology modeling and pharmacophore-based virtual screening. Biomedicines 10, 365 (2022).

doi: 10.3390/biomedicines10020365 pubmed: 35203575 pmcid: 8962382

Muratspahić, E. et al. Design and structural validation of peptide–drug conjugate ligands of the kappa-opioid receptor. Nat. Commun. 14, 8064. https://doi.org/10.1038/s41467-023-43718-w (2023).

doi: 10.1038/s41467-023-43718-w pubmed: 38052802 pmcid: 10698194

Kchouk, S. & Hegazy, L. Pharmacophore modeling for biological targets with high flexibility: LXRβ case study. Med. Drug Discov. 15, 100135. https://doi.org/10.1016/j.medidd.2022.100135 (2022).

doi: 10.1016/j.medidd.2022.100135

Amaro, R. E. et al. Ensemble docking in drug discovery. Biophys. J. 114, 2271–2278. https://doi.org/10.1016/j.bpj.2018.02.038 (2018).

doi: 10.1016/j.bpj.2018.02.038 pubmed: 29606412 pmcid: 6129458

Chan, F.-Y. et al. Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. J. Chem. Inf. Model. 53, 2131–2140. https://doi.org/10.1021/ci400203f (2013).

doi: 10.1021/ci400203f pubmed: 23848971

Sliwoski, G., Kothiwale, S., Meiler, J. & Lowe, E. W. Jr. Computational methods in drug discovery. Pharmacol. Rev. 66, 334–395. https://doi.org/10.1124/pr.112.007336 (2013).

doi: 10.1124/pr.112.007336 pubmed: 24381236

Simons, S. S. Jr., Edwards, D. P. & Kumar, R. Minireview: Dynamic structures of nuclear hormone receptors: New promises and challenges. Mol. Endocrinol. 28, 173–182. https://doi.org/10.1210/me.2013-1334 (2014).

doi: 10.1210/me.2013-1334 pubmed: 24284822

Huang, P., Chandra, V. & Rastinejad, F. Structural overview of the nuclear receptor superfamily: Insights into physiology and therapeutics. Annu. Rev. Physiol. 72, 247–272. https://doi.org/10.1146/annurev-physiol-021909-135917 (2010).

doi: 10.1146/annurev-physiol-021909-135917 pubmed: 20148675 pmcid: 3677810

Griffett, K. et al. The orphan nuclear receptor TLX is a receptor for synthetic and natural retinoids. Cell Chem. Biol. https://doi.org/10.2139/ssrn.3545512 (2020).

doi: 10.2139/ssrn.3545512 pubmed: 32763139

Karplus, M. & McCammon, J. A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9, 646–652 (2002).

doi: 10.1038/nsb0902-646 pubmed: 12198485

Basak, S. et al. Molecular mechanism of setron-mediated inhibition of full-length 5-HT3A receptor. Nat. Commun. 10, 1–11. https://doi.org/10.1038/s41467-019-11142-8 (2019).

doi: 10.1038/s41467-019-11142-8

Salo-Ahen, O. M. H. et al. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 9, 71 (2021).

doi: 10.3390/pr9010071

Henzler-Wildman, K. & Kern, D. Dynamic personalities of proteins. Nature 450, 964–972. https://doi.org/10.1038/nature06522 (2007).

doi: 10.1038/nature06522 pubmed: 18075575

Boehr, D. D., Dyson, H. J. & Wright, P. E. An NMR perspective on enzyme dynamics. Chem. Rev. 106, 3055–3079. https://doi.org/10.1021/cr050312q (2006).

doi: 10.1021/cr050312q pubmed: 16895318

Vilardaga, J.-P., Bünemann, M., Krasel, C., Castro, M. & Lohse, M. J. Measurement of the millisecond activation switch of G protein-coupled receptors in living cells. Nat. Biotechnol. 21, 807–812. https://doi.org/10.1038/nbt838 (2003).

doi: 10.1038/nbt838 pubmed: 12808462

Kästner, J. Umbrella sampling. WIREs Comput. Mol. Sci. 1, 932–942. https://doi.org/10.1002/wcms.66 (2011).

doi: 10.1002/wcms.66

Torrie, G. M. & Valleau, J. P. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J. Comput. Phys. 23, 187–199. https://doi.org/10.1016/0021-9991(77)90121-8 (1977).

doi: 10.1016/0021-9991(77)90121-8

Johnston, J. M. et al. Making structural sense of dimerization interfaces of delta opioid receptor homodimers. Biochemistry 50, 1682–1690. https://doi.org/10.1021/bi101474v (2011).

doi: 10.1021/bi101474v pubmed: 21261298

Bussi, G. & Laio, A. Using metadynamics to explore complex free-energy landscapes. Nat. Rev. Phys. 2, 200–212. https://doi.org/10.1038/s42254-020-0153-0 (2020).

doi: 10.1038/s42254-020-0153-0

Grubmüller, H. Predicting slow structural transitions in macromolecular systems: Conformational flooding. Phys. Rev. E 52, 2893–2906. https://doi.org/10.1103/PhysRevE.52.2893 (1995).

doi: 10.1103/PhysRevE.52.2893

Darve, E., Rodríguez-Gómez, D. & Pohorille, A. Adaptive biasing force method for scalar and vector free energy calculations. J. Chem. Phys. 128, 144120. https://doi.org/10.1063/1.2829861 (2008).

doi: 10.1063/1.2829861 pubmed: 18412436

Darve, E. & Pohorille, A. Calculating free energies using average force. J. Chem. Phys. 115, 9169–9183. https://doi.org/10.1063/1.1410978 (2001).

doi: 10.1063/1.1410978

Miao, Y. et al. Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J. Chem. Theory Comput. 10, 2677–2689. https://doi.org/10.1021/ct500090q (2014).

doi: 10.1021/ct500090q pubmed: 25061441 pmcid: 4095935

Kappel, K., Miao, Y. & McCammon, J. A. Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Q. Rev. Biophys. 48, 479–487. https://doi.org/10.1017/S0033583515000153 (2015).

doi: 10.1017/S0033583515000153 pubmed: 26537408 pmcid: 5435230

Miao, Y. & McCammon, J. A. G-protein coupled receptors: Advances in simulation and drug discovery. Curr. Opin. Struct. Biol. 41, 83–89. https://doi.org/10.1016/j.sbi.2016.06.008 (2016).

doi: 10.1016/j.sbi.2016.06.008 pubmed: 27344006 pmcid: 5154880

Miao, Y. & McCammon, J. A. Graded activation and free energy landscapes of a muscarinic G-protein-coupled receptor. Proc. Natl. Acad. Sci. 113, 12162–12167. https://doi.org/10.1073/pnas.1614538113 (2016).

doi: 10.1073/pnas.1614538113 pubmed: 27791003 pmcid: 5087018

Miao, Y. & McCammon, J. A. Chapter Six—Gaussian accelerated molecular dynamics: Theory, implementation, and applications. In Annual Reports in Computational Chemistry Vol. 13 (ed. Dixon, D. A.) 231–278 (Elsevier, 2017) (ISBN 1574-1400).

Murray, M. H. et al. Structural basis of synthetic agonist activation of the nuclear receptor REV-ERB. Nat. Commun. 13, 7131. https://doi.org/10.1038/s41467-022-34892-4 (2022).

doi: 10.1038/s41467-022-34892-4 pubmed: 36414641 pmcid: 9681850

Waterhouse, A. et al. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303. https://doi.org/10.1093/nar/gky427 (2018).

doi: 10.1093/nar/gky427 pubmed: 29788355 pmcid: 6030848

Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinform. 9, 40. https://doi.org/10.1186/1471-2105-9-40 (2008).

doi: 10.1186/1471-2105-9-40

Yang, J. et al. The I-TASSER Suite: Protein structure and function prediction. Nat. Methods 12, 7–8. https://doi.org/10.1038/nmeth.3213 (2015).

doi: 10.1038/nmeth.3213 pubmed: 25549265 pmcid: 4428668

Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H. & Kollman, P. A. Multidimensional free-energy calculations using the weighted histogram analysis method. J. Comput. Chem. 16, 1339–1350. https://doi.org/10.1002/jcc.540161104 (1995).

doi: 10.1002/jcc.540161104

Ponder, J. W. & Case, D. A. Force fields for protein simulations. Adv. Protein Chem. 66, 27–85 (2003).

doi: 10.1016/S0065-3233(03)66002-X pubmed: 14631816

Case, D. A. et al. AMBER 2018 (University of California, 2018).

Humphrey, W., Dalke, A. & Schulten, K. V. M. D. Visual molecular dynamics. J. Mol. Graph. 14, 33–38. https://doi.org/10.1016/0263-7855(96)00018-5 (1996).

doi: 10.1016/0263-7855(96)00018-5 pubmed: 8744570

Roe, D. R. & Cheatham, T. E. 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 (2013).

doi: 10.1021/ct400341p pubmed: 26583988

Pettersen, E. F. et al. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612. https://doi.org/10.1002/jcc.20084 (2004).

doi: 10.1002/jcc.20084 pubmed: 15264254