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Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

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Abstract

GPCR allosteric modulators target at the allosteric binding pockets of G protein-coupled receptors (GPCRs) with indirect influence on the effects of an orthosteric ligand. Such modulators exhibit significant advantages compared to the corresponding orthosteric ligands, including better chemical tractability or physicochemical properties, improved selectivity, and reduced risk of oversensitization towards their receptors. Metabotropic glutamate receptor 5 (mGlu5), a member of class C GPCRs, is a promising therapeutic target for treating many central nervous system diseases. The crystal structure of mGlu5 in the complex with the negative allosteric modulator mavoglurant was recently reported, providing a fundamental model for designing new allosteric modulators. Computational fragment-based drug discovery represents a powerful scaffold-hopping and lead structure-optimization tool for drug design. In the present work, a set of integrated computational methodologies was first used, such as fragment library generation and retrosynthetic combinatorial analysis procedure (RECAP) for novel compound generation. Then, the compounds generated were assessed by benchmark dataset verification, docking studies, and QSAR model simulation. Subsequently, structurally diverse compounds, with reported or unreported scaffolds, can be observed from top 20 in silico synthesized compounds, which were predicted to be potential mGlu5 modulators. In silico compounds with reported scaffolds may fill SAR holes in known, patented series of mGlu5 modulators. And the generation of compounds without reported tests on mGluR indicates that our approach is doable for exploring and designing novel compounds. Our case study of designing allosteric modulators on mGlu5 demonstrated that the established computational fragment-based approach is a useful methodology for facilitating new compound design in the future.

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References

  1. Wu H, Wang C, Gregory KJ, Han GW, Cho HP, Xia Y, et al. Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator. Science. 2014;344(6179):58–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science. 2001;291(5507):1304–51.

    Article  CAS  PubMed  Google Scholar 

  3. Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov. 2006;5(12):993–6.

    Article  CAS  PubMed  Google Scholar 

  4. Feng Z, Hu G, Ma S, Xie X-Q. Computational advances for the development of allosteric modulators and bitopic ligands in G protein-coupled receptors. AAPS J. 2015;17(5):1080–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wenthur CJ, Gentry PR, Mathews TP, Lindsley CW. Drugs for allosteric sites on receptors. Annu Rev Pharmacol Toxicol. 2014;54:165.

    Article  CAS  PubMed  Google Scholar 

  6. Wellendorph P, Bräuner-Osborne H. Molecular basis for amino acid sensing by family CG-protein-coupled receptors. Br J Pharmacol. 2009;156(6):869–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lane JR, Abdul-Ridha A, Canals M. Regulation of G protein-coupled receptors by allosteric ligands. ACS Chem Neurosci. 2013;4(4):527–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Melancon BJ, Hopkins CR, Wood MR, Emmitte KA, Niswender CM, Christopoulos A, et al. Allosteric modulation of seven transmembrane spanning receptors: theory, practice, and opportunities for central nervous system drug discovery. J Med Chem. 2012;55(4):1445–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Christopoulos A. Allosteric binding sites on cell-surface receptors: novel targets for drug discovery. Nat Rev Drug Discov. 2002;1(3):198–210.

    Article  CAS  PubMed  Google Scholar 

  10. Conn PJ, Christopoulos A, Lindsley CW. Allosteric modulators of GPCRs: a novel approach for the treatment of CNS disorders. Nat Rev Drug Discov. 2009;8(1):41–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bridges TM, Lindsley CW. G-protein-coupled receptors: from classical modes of modulation to allosteric mechanisms. ACS Chem Biol. 2008;3(9):530–41.

    Article  CAS  PubMed  Google Scholar 

  12. Digby GJ, Conn PJ, Lindsley CW. Orthosteric-and allosteric-induced ligand-directed trafficking at GPCRs. Curr Opin Drug Discov Devel. 2010;13(5):587.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Fang Z, Grütter C, Rauh D. Strategies for the selective regulation of kinases with allosteric modulators: exploiting exclusive structural features. ACS Chem Biol. 2012;8(1):58–70.

    Article  PubMed  Google Scholar 

  14. Möhler H, Fritschy J, Rudolph U. A new benzodiazepine pharmacology. J Pharmacol Exp Ther. 2002;300(1):2–8.

    Article  PubMed  Google Scholar 

  15. Pin J-P, Galvez T, Prézeau L. Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors. Pharmacol Ther. 2003;98(3):325–54.

    Article  CAS  PubMed  Google Scholar 

  16. Doré AS, Okrasa K, Patel JC, Serrano-Vega M, Bennett K, Cooke RM, et al. Structure of class C GPCR metabotropic glutamate receptor 5 transmembrane domain. Nature. 2014;511(7511):557–62.

    Article  PubMed  Google Scholar 

  17. Li G, Jørgensen M, Campbell BM. Metabotropic glutamate receptor 5-negative allosteric modulators for the treatment of psychiatric and neurological disorders (2009-July 2013). Pharmaceutical patent analyst. 2013;2(6):767–802.

    Article  CAS  PubMed  Google Scholar 

  18. Levenga J, Hayashi S, de Vrij FM, Koekkoek SK, van der Linde HC, Nieuwenhuizen I, et al. AFQ056, a new mGluR5 antagonist for treatment of fragile X syndrome. Neurobiol Dis. 2011;42(3):311–7.

    Article  CAS  PubMed  Google Scholar 

  19. Ritzén A, Sindet R, Hentzer M, Svendsen N, Brodbeck RM, Bundgaard C. Discovery of a potent and brain penetrant mGluR5 positive allosteric modulator. Bioorg Med Chem Lett. 2009;19(12):3275–8.

    Article  PubMed  Google Scholar 

  20. Hajduk PJ, Greer J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov. 2007;6(3):211–9.

    Article  CAS  PubMed  Google Scholar 

  21. Joseph-McCarthy D, Campbell AJ, Kern G, Moustakas D. Fragment-based lead discovery and design. J Chem Inf Model. 2014;54(3):693–704.

    Article  CAS  PubMed  Google Scholar 

  22. Allen KN, Bellamacina CR, Ding X, Jeffery CJ, Mattos C, Petsko GA, et al. An experimental approach to mapping the binding surfaces of crystalline proteins. J Phys Chem. 1996;100(7):2605–11.

    Article  CAS  Google Scholar 

  23. Chessari G, Woodhead AJ. From fragment to clinical candidate—a historical perspective. Drug Discov Today. 2009;14(13):668–75.

    Article  CAS  PubMed  Google Scholar 

  24. de Kloe GE, Bailey D, Leurs R, de Esch IJ. Transforming fragments into candidates: small becomes big in medicinal chemistry. Drug Discov Today. 2009;14(13):630–46.

    Article  PubMed  Google Scholar 

  25. Jencks WP. On the attribution and additivity of binding energies. Proc Natl Acad Sci. 1981;78(7):4046–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov. 2005;4(8):649–63.

    Article  CAS  PubMed  Google Scholar 

  27. Howard N, Abell C, Blakemore W, Chessari G, Congreve M, Howard S, et al. Application of fragment screening and fragment linking to the discovery of novel thrombin inhibitors. J Med Chem. 2006;49(4):1346–55.

    Article  CAS  PubMed  Google Scholar 

  28. Edwards PD, Albert JS, Sylvester M, Aharony D, Andisik D, Callaghan O, et al. Application of fragment-based lead generation to the discovery of novel, cyclic Amidine β-secretase inhibitors with nanomolar potency, cellular activity, and high ligand efficiency §. J Med Chem. 2007;50(24):5912–25.

    Article  CAS  PubMed  Google Scholar 

  29. Sun C, Petros AM, Hajduk PJ. Fragment-based lead discovery: challenges and opportunities. J Comput Aided Mol Des. 2011;25(7):607–10.

    Article  CAS  PubMed  Google Scholar 

  30. Loving K, Alberts I, Sherman W. Computational approaches for fragment-based and de novo design. Curr Top Med Chem. 2010;10(1):14–32.

    Article  CAS  PubMed  Google Scholar 

  31. Schuffenhauer A, Ruedisser S, Marzinzik A, Jahnke W, Selzer P, Jacoby E. Library design for fragment based screening. Curr Top Med Chem. 2005;5(8):751–62.

    Article  CAS  PubMed  Google Scholar 

  32. Siegal G, Eiso A, Schultz J. Integration of fragment screening and library design. Drug Discov Today. 2007;12(23):1032–9.

    Article  CAS  PubMed  Google Scholar 

  33. Lepre C. Fragment-based drug discovery using the SHAPES method. Expert Opin Drug Discovery. 2007;2(12):1555–66.

    Article  CAS  Google Scholar 

  34. Fjellström O, Akkaya S, Beisel H-G, Eriksson P-O, Erixon K, Gustafsson D, et al. Creating novel activated factor xi inhibitors through fragment based lead generation and structure aided drug design. PLoS One. 2015;10(1):e0113705.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ni S, Yuan Y, Huang J, Mao X, Lv M, Zhu J, et al. Discovering potent small molecule inhibitors of cyclophilin A using de novo drug design approach. J Med Chem. 2009;52(17):5295–8.

    Article  CAS  PubMed  Google Scholar 

  36. Huang Z, Mou L, Shen Q, Lu S, Li C, Liu X, et al. ASD v2. 0: updated content and novel features focusing on allosteric regulation. Nucleic Acids Res. 2014;42(D1):D510–D6.

    Article  CAS  PubMed  Google Scholar 

  37. Christopher JA, Aves SJ, Bennett KA, Doré AS, Errey JC, Jazayeri A, et al. Fragment and structure-based drug discovery for a class C GPCR: discovery of the mGlu5 negative allosteric modulator HTL14242 (3-chloro-5-[6-(5-fluoropyridin-2-yl) pyrimidin-4-yl] benzonitrile). J Med Chem. 2015;58(16):6653–64.

    Article  CAS  PubMed  Google Scholar 

  38. SYBYL-X 1.3 TI, 1699 South Hanley Rd., St. Louis, MO, 63144, USA. 2010. 2010.

  39. Feng Z, Ma S, Hu G, Xie X-Q. Allosteric binding site and activation mechanism of class C G-protein coupled receptors: metabotropic glutamate receptor family. AAPS J. 2015;17(3):737–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. The PyMOL Molecular graphics system, version 1.5.0.1 Schrodinger, LLC.

  41. Lewell XQ, Judd DB, Watson SP, Hann MM. RECAP Retrosynthetic Combinatorial Analysis Procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci. 1998;38(3):511–22.

    Article  CAS  PubMed  Google Scholar 

  42. Fechner U, Schneider G. Flux (1): a virtual synthesis scheme for fragment-based de novo design. J Chem Inf Model. 2006;46(2):699–707.

    Article  CAS  PubMed  Google Scholar 

  43. Teague SJ, Davis AM, Leeson PD, Oprea T. The design of leadlike combinatorial libraries. Angew Chem Int Ed. 1999;38(24):3743–8.

    Article  CAS  Google Scholar 

  44. Jain AN. Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J Comput Aided Mol Des. 1996;10(5):427–40.

    Article  CAS  PubMed  Google Scholar 

  45. Weiner SJ, Kollman PA, Nguyen DT, Case DA. An all atom force field for simulations of proteins and nucleic acids. J Comput Chem. 1986;7(2):230–52.

    Article  CAS  Google Scholar 

  46. Meng EC, Shoichet BK, Kuntz ID. Automated docking with grid-based energy evaluation. J Comput Chem. 1992;13(4):505–24.

    Article  CAS  Google Scholar 

  47. Sanner MF. Python: a programming language for software integration and development. J Mol Graph Model. 1999;17(1):57–61.

    CAS  PubMed  Google Scholar 

  48. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem. 1998;19(14):1639–62.

    Article  CAS  Google Scholar 

  49. Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem. 2006;49(23):6789–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Weiss DR, Bortolato A, Tehan B, Mason JS. GPCR-bench: a benchmarking set and practitioners’ guide for G protein-coupled receptor docking. J Chem Inf Model. 2016;56(4):642–51.

    Article  CAS  PubMed  Google Scholar 

  51. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem. 2012;55(14):6582–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ioele G, De Luca M, Oliverio F, Ragno G. Prediction of photosensitivity of 1, 4-dihydropyridine antihypertensives by quantitative structure-property relationship. Talanta. 2009;79(5):1418–24.

    Article  CAS  PubMed  Google Scholar 

  53. Raska I, Toropov A. Comparison of QSPR models of octanol/water partition coefficient for vitamins and non vitamins. Eur J Med Chem. 2006;41(11):1271–8.

    Article  CAS  PubMed  Google Scholar 

  54. Hogg R, Tanis E. Nonparametric methods. Probability and statistical inference. 4th ed. New York: MacMillan Publishing Co; 1993. p. 589–646.

    Google Scholar 

  55. Hogg RV, Tanis EA. Solutions Manual, Probability and Statistical Interference: Solutions: Macmillan; 1988.

  56. Ertl P. Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comput Sci. 2003;43(2):374–80.

    Article  CAS  PubMed  Google Scholar 

  57. Congreve M, Carr R, Murray C, Jhoti H. A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today. 2003;8(19):876–7.

    Article  PubMed  Google Scholar 

  58. Rees DC, Congreve M, Murray CW, Carr R. Fragment-based lead discovery. Nat Rev Drug Discov. 2004;3(8):660–72.

    Article  CAS  PubMed  Google Scholar 

  59. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2012;64:4–17.

    Article  Google Scholar 

  60. Bogan AA, Thorn KS. Anatomy of hot spots in protein interfaces. J Mol Biol. 1998;280(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  61. Leffler JE. The enthalpy-entropy relationship and its implications for organic chemistry. The Journal of Organic Chemistry. 1955;20(9):1202–31.

    Article  CAS  Google Scholar 

  62. Eftink MR, Anusiem A, Biltonen RL. Enthalpy-entropy compensation and heat capacity changes for protein-ligand interactions: general thermodynamic models and data for the binding of nucleotides to ribonuclease A. Biochemistry. 1983;22(16):3884–96.

    Article  CAS  PubMed  Google Scholar 

  63. Davenport AP, Russell FD. Radioligand binding assays: theory and practice. Current Directions in Radiopharmaceutical Research and Development: Springer; 1996; 169–79.

  64. Cui W, Yan X. Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR. Chemom Intell Lab Syst. 2009;98(2):130–5.

    Article  CAS  Google Scholar 

  65. Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53(7):2719–40.

    Article  CAS  PubMed  Google Scholar 

  66. Patlewicz G, Jeliazkova N, Safford R, Worth A, Aleksiev B. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res. 2008;19(5–6):495–524.

    Article  CAS  PubMed  Google Scholar 

  67. Bursavich MG, Gilbert AM, Stock JR. Piperazine metabotropic glutamate receptor 5 (MGLUR5) negative allosteric modulators for anxiety/depression. US Patent; 2009.

  68. Macdonald G, Tresadern G, Trabanco-Suarez A, Pastor-Fernandez J. Preparation of bicyclic thiazoles as allosteric modulators of mGluR5 receptors. WO Patent; 2011.

  69. Imogai H, Mutel V, Duvey GA, Cid-Nunez J, Le Poul E, Lutjens R. Novel thieno-pyridine and thieno-pyrimidine derivatives and their use as positive allosteric modulators of mGluR2-receptors. US Patent; 2005.

  70. Conn J, Lindsley C, Weaver C, Stauffer S, Williams R, McDonald G, et al. Preparation of O-benzyl nicotinamide analogs as mGluR5 positive allosteric modulators. WO Patent. 2011;

  71. Imogai HJ, Cid-Nunez JM, Andres-Gil JI, Trabanco-Suarez AA, Santamarina JO, Dautzenberg FM, et al.. 1, 4-Disubstituted 3-cyano-pyridone derivatives and their use as positive allosteric modulators of MGLUR2-receptors. US Patent; 2014.

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Acknowledgements

Authors would like to acknowledge the funding supports to the Xie laboratory from the NIH NIDA (P30 DA035778A1), NIH (R01 DA025612), and DOD (W81XWH-16-1-0490).

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Author notes

  1. Yuemin Bian and Zhiwei Feng contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yuemin Bian, Zhiwei Feng, Peng Yang & Xiang-Qun Xie

  2. NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yuemin Bian, Zhiwei Feng, Peng Yang & Xiang-Qun Xie

  3. Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yuemin Bian, Zhiwei Feng, Peng Yang & Xiang-Qun Xie

  4. Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Xiang-Qun Xie

  5. Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Xiang-Qun Xie

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  1. Yuemin Bian
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Bian, Y., Feng, Z., Yang, P. et al. Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5. AAPS J 19, 1235–1248 (2017). https://doi.org/10.1208/s12248-017-0093-5

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