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Challenges and Opportunities in Drug Discovery of Biased Ligands

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Book cover Computational Methods for GPCR Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1705))

Abstract

The observation of biased agonism in G protein-coupled receptors (GPCRs) has provided new approaches for the development of more efficacious and safer drugs. However, in order to rationally design biased drugs, one must understand the molecular basis of this phenomenon. Computational approaches can help in exploring the conformational universe of GPCRs and detecting conformational states with relevance for distinct functional outcomes. This information is extremely valuable for the development of new therapeutic agents that promote desired conformational receptor states and responses while avoiding the ones leading to undesired side-effects.

This book chapter intends to introduce the reader to powerful computational approaches for sampling the conformational space of these receptors, focusing first on molecular dynamics and the analysis of the produced data through methods such as dimensionality reduction, Markov State Models and adaptive sampling. Then, we show how to seek for compounds that target distinct conformational states via docking and virtual screening. In addition, we describe how to detect receptor-ligand interactions that drive signaling bias and comment current challenges and opportunities of presented methods.

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References

  1. Martí-Solano M, Guixà-González R, Sanz F et al (2013) Novel insights into biased agonism at G protein-coupled receptors and their potential for drug design. Curr Pharm Des 19:5156–5166

    Article  PubMed  Google Scholar 

  2. Violin JD, Dewire SM, Yamashita D et al (2010) Selectively engaging B-arrestins at the angiotensin II type 1 receptor reduces blood pressure and increases cardiac performance. Pharmacol Ther 335:572–579. https://doi.org/10.1124/jpet.110.173005

    Article  CAS  Google Scholar 

  3. Rosenbaum DM, Cherezov V, Hanson MA et al (2007) GPCR engineering yields high-resolution structural insights into 2-adrenergic receptor function. Science 318:1266–1273. https://doi.org/10.1126/science.1150609

    Article  CAS  PubMed  Google Scholar 

  4. Kang Y, Zhou XE, Gao X et al (2015) Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser. Nature 523:561–567. https://doi.org/10.1038/nature14656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rodríguez-Espigares I, Kaczor AA, Selent J (2016) In silico exploration of the conformational universe of GPCRs. Mol Inform 35:227–237. https://doi.org/10.1002/minf.201600012

    Article  PubMed  Google Scholar 

  6. Altis A, Nguyen PH, Hegger R, Stock G (2007) Dihedral angle principal component analysis of molecular dynamics simulations. J Chem Phys 126:244111. https://doi.org/10.1063/1.2746330

    Article  PubMed  Google Scholar 

  7. Brown WM, Martin S, Pollock SN et al (2008) Algorithmic dimensionality reduction for molecular structure analysis. J Chem Phys 129:64118

    Article  Google Scholar 

  8. Lange OF, Grubmüller H (2006) Generalized correlation for biomolecular dynamics. Proteins 62:1053–1061. https://doi.org/10.1002/prot.20784

    Article  CAS  PubMed  Google Scholar 

  9. Teodoro ML, Phillips GN, Kavraki LE (2003) Understanding protein flexibility through dimensionality reduction. J Comput Biol 10:617–634. https://doi.org/10.1089/10665270360688228

    Article  CAS  PubMed  Google Scholar 

  10. Bai Q, Pérez-Sánchez H, Zhang Y et al (2014) Ligand induced change of β2 adrenergic receptor from active to inactive conformation and its implication for the closed/open state of the water channel: insight from molecular dynamics simulation, free energy calculation and Markov state model analysis. Phys Chem Chem Phys 16:15874–15885. https://doi.org/10.1039/c4cp01185f

    Article  CAS  PubMed  Google Scholar 

  11. Ng HW, Laughton CA, Doughty SW (2013) Molecular dynamics simulations of the adenosine A2a receptor: structural stability, sampling, and convergence. J Chem Inf Model 53:1168–1178. https://doi.org/10.1021/ci300610w

    Article  CAS  PubMed  Google Scholar 

  12. Pérez-Hernández G, Paul F, Giorgino T et al (2013) Identification of slow molecular order parameters for Markov model construction. J Chem Phys 139:15102. https://doi.org/10.1063/1.4811489

    Article  Google Scholar 

  13. Scherer MK, Trendelkamp-Schroer B, Paul F et al (2015) PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J Chem Theory Comput 11:5525–5542. https://doi.org/10.1021/acs.jctc.5b00743

    Article  CAS  PubMed  Google Scholar 

  14. Razavi AM, Wuest WM, Voelz VA (2014) Computational screening and selection of cyclic peptide hairpin mimetics by molecular simulation and kinetic network models. J Chem Inf Model 54:1425–1432. https://doi.org/10.1021/ci500102y

    Article  CAS  PubMed  Google Scholar 

  15. Grossfield A, Feller SE, Pitman MC (2007) Convergence of molecular dynamics simulations of membrane proteins. Proteins 67:31–40. https://doi.org/10.1002/prot.21308

    Article  CAS  PubMed  Google Scholar 

  16. Hartigan AJ (1975) Clustering algorithms. John Wiley & Sons, Inc, Hoboken, NJ

    Google Scholar 

  17. Prinz J-H, Wu H, Sarich M et al (2011) Markov models of molecular kinetics: generation and validation. J Chem Phys 134:174105. https://doi.org/10.1063/1.3565032

    Article  PubMed  Google Scholar 

  18. Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, pp 1027–1035

    Google Scholar 

  19. Sculley D (2010) Web-scale k-means clustering. In: Proceedings of the 19th international conference on World wide web–WWW ‘10. ACM Press, New York, NY, p 1177

    Google Scholar 

  20. Pande VS, Beauchamp K, Bowman GR (2010) Everything you wanted to know about Markov state models but were afraid to ask. Methods 52:99–105. https://doi.org/10.1016/j.ymeth.2010.06.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Röblitz S, Weber M (2013) Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification. Adv Data Anal Classif 7:147–179. https://doi.org/10.1007/s11634-013-0134-6

    Article  Google Scholar 

  22. Noé F, Schütte C, Vanden-Eijnden E et al (2009) Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations. Proc Natl Acad Sci U S A 106:19011–19016. https://doi.org/10.1073/pnas.0905466106

    Article  PubMed  PubMed Central  Google Scholar 

  23. Swope WC, Pitera JW, Suits F (2004) Describing protein folding kinetics by molecular dynamics simulations. 1. Theory. J Phys Chem B 108:6571–6581. https://doi.org/10.1021/jp037421y

  24. Park S, Pande VS (2006) Validation of Markov state models using Shannon’s entropy. J Chem Phys 124:54118. https://doi.org/10.1063/1.2166393

    Article  Google Scholar 

  25. Bacallado S, Chodera JD, Pande V (2009) Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint. J Chem Phys 131:45106. https://doi.org/10.1063/1.3192309

    Article  Google Scholar 

  26. Bowman GR, Ensign DL, Pande VS (2010) Enhanced modeling via network theory: adaptive sampling of Markov state models. J Chem Theory Comput 6:787–794. https://doi.org/10.1021/ct900620b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Doerr S, Harvey MJ, Noé F, De Fabritiis G (2016) HTMD: high-throughput molecular dynamics for molecular discovery. J Chem Theory Comput 12:1845–1852. https://doi.org/10.1021/acs.jctc.6b00049

    Article  CAS  PubMed  Google Scholar 

  28. Kohlhoff KJ, Shukla D, Lawrenz M et al (2013) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6:15–21. https://doi.org/10.1038/nchem.1821

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bruno A, Costantino G (2012) Molecular dynamics simulations of G protein-coupled receptors. Mol Inform 31:222–230. https://doi.org/10.1002/minf.201100138

    Article  CAS  PubMed  Google Scholar 

  30. Kufareva I, Katritch V, Participants of GPCR Dock 2013 et al (2014) Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure 22:1120–1139. https://doi.org/10.1016/j.str.2014.06.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Woo AY-H, Jozwiak K, Toll L et al (2014) Tyrosine 308 is necessary for ligand-directed Gs protein-biased signaling of β2-adrenoceptor. J Biol Chem 289:19351–19363. https://doi.org/10.1074/jbc.M114.558882

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhang H, Unal H, Desnoyer R et al (2015) Structural basis for ligand recognition and functional selectivity at angiotensin receptor. J Biol Chem 290:29127–29139. https://doi.org/10.1074/jbc.M115.689000

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Weichert D, Banerjee A, Hiller C et al (2015) Molecular determinants of biased agonism at the dopamine D2 receptor. J Med Chem 58:2703–2717. https://doi.org/10.1021/jm501889t

  34. Manglik A, Lin H, Aryal DK et al (2016) Structure-based discovery of opioid analgesics with reduced side effects. Nature 537:185–190. https://doi.org/10.1038/nature19112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kaczor AA, Rutkowska E, Bartuzi D et al (2016) Chapter 17 – computational methods for studying G protein-coupled receptors (GPCRs). Methods Cell Biol 132:359–399. https://doi.org/10.1016/bs.mcb.2015.11.002

    Article  PubMed  Google Scholar 

  36. Topiol S, Sabio M (2015) The role of experimental and computational structural approaches in 7TM drug discovery. Expert Opin Drug Discovery 10:1071–1084. https://doi.org/10.1517/17460441.2015.1072166

    Article  Google Scholar 

  37. Costanzi S (2014) Modeling G protein-coupled receptors in complex with biased agonists. Trends Pharmacol Sci 35:277–283. https://doi.org/10.1016/j.tips.2014.04.004

    Article  CAS  PubMed  Google Scholar 

  38. Tarcsay A, Paragi G, Vass M et al (2013) The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 53:2990–2999. https://doi.org/10.1021/ci400087b

    Article  CAS  PubMed  Google Scholar 

  39. Bhattacharya S, Vaidehi N (2010) Computational mapping of the conformational transitions in agonist selective pathways of a G-protein coupled receptor. J Am Chem Soc 132:5205–5214. https://doi.org/10.1021/ja910700y

    Article  CAS  PubMed  Google Scholar 

  40. Kakarala KK, Jamil K (2016) Biased signaling: potential agonist and antagonist of PAR2. J Biomol Struct Dyn 34:1363–1376. https://doi.org/10.1080/07391102.2015.1079556

    Article  CAS  PubMed  Google Scholar 

  41. Gandhimathi A, Sowdhamini R (2015) Molecular modelling of human 5-hydroxytryptamine receptor (5-HT 2A ) and virtual screening studies towards the identification of agonist and antagonist molecules. J Biomol Struct Dyn 34(5):952–970. https://doi.org/10.1080/07391102.2015.1062802

    Article  PubMed  Google Scholar 

  42. Kooistra AJ, Roumen L, Leurs R et al (2013) From heptahelical bundle to hits from the haystack: structure-based virtual screening for GPCR ligands. In: Conn PM (ed) G protein coupled receptors modeling, activation, interactions and virtual screening. Academic Press, New York, pp 279–336

    Chapter  Google Scholar 

  43. Rodrigues T, Hauser N, Reker D et al (2015) Multidimensional de novo design reveals 5-HT2B receptor-selective ligands. Angew Chem Int Ed Engl 54(5):1551. https://doi.org/10.1002/anie.201410201

    Article  CAS  PubMed  Google Scholar 

  44. Marti-Solano M, Iglesias A, de Fabritiis G et al (2015) Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation. Mol Pharmacol 87:740–746. https://doi.org/10.1124/mol.114.097022

    Article  CAS  PubMed  Google Scholar 

  45. Nichols DE (2004) Hallucinogens. Pharmacol Ther 101:131–181. https://doi.org/10.1016/j.pharmthera.2003.11.002

    Article  CAS  PubMed  Google Scholar 

  46. Meltzer H (1999) The role of serotonin in antipsychotic drug action. Neuropsychopharmacology 21:106S–115S. https://doi.org/10.1016/S0893-133X(99)00046-9

    Article  CAS  PubMed  Google Scholar 

  47. González-Maeso J, Sealfon SC (2009) Psychedelics and schizophrenia. Trends Neurosci 32:225–232. https://doi.org/10.1016/j.tins.2008.12.005

    Article  PubMed  Google Scholar 

  48. Berg KA, Stout BD, Cropper JD et al (1999) Novel actions of inverse agonists on 5-HT2C receptor systems. Mol Pharmacol 55(5):863–872

    CAS  PubMed  Google Scholar 

  49. Kurita M, Holloway T, García-Bea A et al (2012) HDAC2 regulates atypical antipsychotic responses through the modulation of mGlu2 promoter activity. Nat Neurosci 15:1245–1254. https://doi.org/10.1038/nn.3181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hertig S, Latorraca NR, Dror RO (2016) Revealing atomic-level mechanisms of protein allostery with molecular dynamics simulations. PLoS Comput Biol 12:e1004746. https://doi.org/10.1371/journal.pcbi.1004746

    Article  PubMed  PubMed Central  Google Scholar 

  51. Glykos NM (2006) Software news and updates carma: a molecular dynamics analysis program. J Comput Chem 27:1765–1768. https://doi.org/10.1002/jcc.20482

    Article  CAS  PubMed  Google Scholar 

  52. Koukos PI, Glykos NM (2013) Grcarma: a fully automated task-oriented interface for the analysis of molecular dynamics trajectories. J Comput Chem 34:2310–2312. https://doi.org/10.1002/jcc.23381

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  54. Schneider S, Provasi D, Filizola M (2016) How oliceridine (TRV-130) binds and stabilizes a μ-opioid receptor conformational state that selectively triggers G protein signaling pathways. Biochemistry 55:6456–6466. https://doi.org/10.1021/acs.biochem.6b00948

    Article  CAS  PubMed  Google Scholar 

  55. Perez A, Morrone JA, Simmerling C, Dill KA (2016) Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struct Biol 36:25–31. https://doi.org/10.1016/j.sbi.2015.12.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Barducci A, Bonomi M, Parrinello M (2011) Metadynamics. WIRE Comput Mol Sci 1:826–843. https://doi.org/10.1002/wcms.31

    Article  CAS  Google Scholar 

  57. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120:11919–11929. https://doi.org/10.1063/1.1755656

    Article  CAS  PubMed  Google Scholar 

  58. Miao Y, McCammon JA (2016) 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

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

I.R.-E. acknowledges financial support from Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (2015 FI_B00145). The paper was developed using the equipment purchased within the project “The equipment of innovative laboratories doing research on new medicines used in the therapy of civilization and neoplastic diseases” within the Operational Program Development of Eastern Poland 2007-2013, Priority Axis I Modern Economy, operations I.3 Innovation promotion.

T.M.S. acknowledges financial support from Hospital del Mar Medical Research Institute.

Finally, J.S. acknowledges financial support from Instituto de Salud Carlos III FEDER (PI15/00460).

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Authors and Affiliations

  1. Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain

    Ismael Rodríguez-Espigares, Tomasz Maciej Stepniewski & Jana Selent

  2. Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Faculty of Pharmacy with Division of Medical Analytics, Medical University of Lublin, 4A Chodzki St., PL-20093, Lublin, Poland

    Agnieszka A. Kaczor

  3. Department of Pharmaceutical Chemistry, School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211, Kuopio, Finland

    Agnieszka A. Kaczor

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  1. Ismael Rodríguez-Espigares
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  2. Agnieszka A. Kaczor
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  3. Tomasz Maciej Stepniewski
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  4. Jana Selent
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Corresponding author

Correspondence to Jana Selent .

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  1. Evotec (UK) Ltd., Abingdon, Oxfordshire, United Kingdom

    Alexander Heifetz

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Rodríguez-Espigares, I., Kaczor, A.A., Stepniewski, T.M., Selent, J. (2018). Challenges and Opportunities in Drug Discovery of Biased Ligands. In: Heifetz, A. (eds) Computational Methods for GPCR Drug Discovery. Methods in Molecular Biology, vol 1705. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7465-8_14

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  • DOI: https://doi.org/10.1007/978-1-4939-7465-8_14

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7464-1

  • Online ISBN: 978-1-4939-7465-8

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