Advertisement

Journal of Molecular Modeling

, Volume 17, Issue 6, pp 1473–1482 | Cite as

Integrating computational and mixture-based screening of combinatorial libraries

  • Austin B. Yongye
  • Clemencia Pinilla
  • Jose L. Medina-Franco
  • Marc A. Giulianotti
  • Colette T. Dooley
  • Jon R. Appel
  • Adel Nefzi
  • Thomas Scior
  • Richard A. Houghten
  • Karina Martínez-MayorgaEmail author
Original Paper

Abstract

Mixture-based synthetic combinatorial library (MB-SCL) screening is a well-established experimental approach for rapidly retrieving structure–activity relationships (SAR) and identifying hits. Virtual screening is also a powerful approach that is increasingly being used in drug discovery programs and has a growing number of successful applications. However, limited efforts have been made to integrate both techniques. To this end, we combined experimental data from a MB-SCL of bicyclic guanidines screened against the κ-opioid receptor and molecular similarity methods. The activity data and similarity analyses were integrated in a biometric analysis–similarity map. Such a map allows the molecules to be categorized as actives, activity cliffs, low similarity to the reference compounds, or missed hits. A compound with IC50 = 309 nM was found in the “missed hits” region, showing that active compounds can be retrieved from a MS-SCL via computational approaches. The strategy presented in this work is general and is envisioned as a general-purpose approach that can be applied to other MB-SCLs.

Mixture-based screening activity data and molecular similarity comparisons to known active compounds are integrated via a biometrical analysis-similarity map, to determine the extent to which molecular similarity methods can rescue missed hits from a mixture-based screening synthetic combinatorial library.

Keywords

Molecular similarity Mixture-based screening Biometric analysis Combinatorial chemistry Virtual screening 

Notes

Acknowledgments

This work was supported by the State of Florida, Executive Officer of the Governor’s Office of Tourism, Trade and Economic Development. We thank OpenEye Scientific Software for providing the OMEGA, ROCS, and VIDA programs, Dr. Tudor Oprea for providing a subset of the Wombat database, and Kyle Kryak for assistance. The work was funded in part by the National Institutes of Health grants 5R21DA019620-02 (RAH), 5P41GM081261-03 (RAH), 3P41GM079590-03S1 (RAH), and 1F03DAO2S850-D1A1 (AN).

Supplementary material

894_2010_850_MOESM1_ESM.doc (147 kb)
Table S1 Substituent groups and activities of the bicyclic guanidines employed for the percent inhibition evaluations (DOC 147 kb)
894_2010_850_MOESM2_ESM.doc (91 kb)
Table S2 Substituent groups and activities of the bicyclic guanidines employed for the IC50 evaluations (DOC 91 kb)
894_2010_850_MOESM3_ESM.doc (459 kb)
Fig. S1 Right: selection of molecules for synthesis based on percent inhibition. Left: scaffold utilized in this study. The R groups of the four internal queries are: R1, R2, R3: S-methyl, S-4-methoxybenzyl, 3-cyclohexylpropyl; S-methyl, R-4-methoxybenzyl, 1-adamantylethyl; S-cyclohexyl, S-cyclohexyl, 4-methyl-cyclohexylmethyl; S-cyclohexyl, R-isobutyl, 4-methyl-cyclohexylmethyl. The full list of substituents is provided in Table S1. Open circles, % inhibition < 27.46; closed circles, % inhibition > 27.46; asterisks, active reference compounds; see [5] (DOC 459 kb)
894_2010_850_MOESM4_ESM.doc (1.1 mb)
Fig. S2 The biometrical analysis–similarity filtering, recovery plots and area under the curves as a function of database screened are shown in the left, middle and right panels, respectively, for percent inhibition-based activities (DOC 1108 kb)
894_2010_850_MOESM5_ESM.doc (54 kb)
Fig. S3 Selection of molecules for synthesis based on IC50 values. Black circles: IC50 < 502 nM; gray circles: 502 nM < IC50 < 1000 nM; white circles: IC50 > 1000 nM. Crosses: IC50 not measured (DOC 54 kb)
894_2010_850_MOESM6_ESM.doc (674 kb)
Fig. S4 Schematic representation of a modified BA–similarity map (top left) and corresponding maps for 3D descriptors (top right) and 2D descriptors (bottom) (DOC 674 kb)

References

  1. 1.
    Fox S, Farr-Jones S, Sopchak L, Boggs A, Comley J (2004) High-throughput screening: searching for higher productivity. J Biomol Screen 9:354–358. doi: 10.1177/1087057104265290 Google Scholar
  2. 2.
    Goode DR, Totten RK, Heeres JT, Hergenrothert PJ (2008) Identification of promiscuous small molecule activators in high-throughput enzyme activation screens. J Med Chem 51:2346–2349. doi: 10.1021/jm701583b CrossRefGoogle Scholar
  3. 3.
    Hertzberg RP, Pope AJ (2000) High-throughput screening: new technology for the 21st century. Curr Opin Chem Biol 4:445–451CrossRefGoogle Scholar
  4. 4.
    Dolle RE (2001) Comprehensive survey of combinatorial library synthesis: 2000. J Comb Chem 3:477–517. doi: 10.1021/cc010049g CrossRefGoogle Scholar
  5. 5.
    Houghten RA, Pinilla C, Appel JR, Blondelle SE, Dooley CT, Eichler J, Nefzi A, Ostresh JM (1999) Mixture-based synthetic combinatorial libraries. J Med Chem 42:3743–3778. doi: 10.1021/jm990174v CrossRefGoogle Scholar
  6. 6.
    Houghten RA, Pinilla C, Giulianotti MA, Appel JR, Dooley CT, Nefzi A, Ostresh JM, Yu YP, Maggiora GM, Medina-Franco JL, Brunner D, Schneider J (2008) Strategies for the use of mixture-based synthetic combinatorial libraries: scaffold ranking, direct testing, in vivo, and enhanced deconvolution by computational methods. J Comb Chem 10:3–19. doi: 10.1021/cc7001205 Google Scholar
  7. 7.
    Pinilla C, Appel JR, Borras E, Houghten RA (2003) Advances in the use of synthetic combinatorial chemistry: mixture-based libraries. Nat Med 9:118–122. doi: 10.1038/70946 CrossRefGoogle Scholar
  8. 8.
    Armishaw CJ, Singh N, Medina-Franco JL, Clark RJ, Scott KC, Houghten RA, Jensen AA (2010) A synthetic combinatorial strategy for developing alpha-conotoxin analogs as potent alpha7 nicotinic acetylcholine receptor antagonists. J Biol Chem 285:1809–1821. doi: 10.1074/jbc.M109.071183 CrossRefGoogle Scholar
  9. 9.
    Reilley KJ, Giulianotti M, Dooley CT, Nefzi A, McLaughlin JP, Houghten RA (2010) Identification of two novel, potent, low-liability antinociceptive compounds from the direct in vivo screening of a large mixture-based combinatorial library. AAPS J 12:318–329. doi: 10.1208/s12248-010-9191-3 CrossRefGoogle Scholar
  10. 10.
    Yongye AB, Appel JR, Giulianotti MA, Dooley CT, Medina-Franco JL, Nefzi A, Houghten RA, Martinez-Mayorga K (2009) Identification, structure–activity relationships and molecular modeling of potent triamine and piperazine opioid ligands. Biorg Med Chem 17:5583–5597. doi: 10.1016/j.bmc.2009.06.026 Google Scholar
  11. 11.
    Hemmer B, Gran B, Zhao YD, Marques A, Pascal J, Tzou A, Kondo T, Cortese I, Bielekova B, Straus SE, McFarland HF, Houghten R, Simon R, Pinilla C, Martin R (1999) Identification of candidate T-cell epitopes and molecular mimics in chronic Lyme disease. Nat Med 5:1375–1382. doi: 10.1002/0471142735.im0905s45 CrossRefGoogle Scholar
  12. 12.
    Zhao Y, Gran B, Pinilla C, Markovic-Plese S, Hemmer B, Tzuo A, Whitney LW, Biddison WE, Martin R, Simon R (2001) Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands. J Immunol 167:2130–2141Google Scholar
  13. 13.
    Dooley CT, Chung NN, Wilkes BC, Schiller PW, Bidlack JM, Pasternak GW, Houghten RA (1994) An all D-amino-acid opioid peptide with central analgesic activity from a combinatorial library. Science 266:2019–2022. doi: 10.1126/science.7801131 Google Scholar
  14. 14.
    Houghten RA, Dooley CT, Appel JR (2006) In vitro and direct in vivo testing of mixture-based combinatorial libraries for the identification of highly active and specific opiate ligands. AAPS J 8:E371–E382. doi: 10.1208/aapsj080242 Google Scholar
  15. 15.
    Venhorst J, ter Laak AM, Commandeur JN, Funae Y, Hiroi T, Vermeulen NP (2003) Homology modeling of rat and human cytochrome P450 2D (CYP2D) isoforms and computational rationalization of experimental ligand-binding specificities. J Med Chem 46:74–86. doi: 10.1021/jm0209578 CrossRefGoogle Scholar
  16. 16.
    Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32:335–373. doi: 10.1186/1471-2105-10-58 CrossRefGoogle Scholar
  17. 17.
    Martin YC (1992) 3D database searching in drug desing. J Med Chem 35:2145–2154. doi: 10.1021/jm00090a001 CrossRefGoogle Scholar
  18. 18.
    Boehm M, Wu T-Y, Claussen H, Lemmen C (2008) Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces. J Med Chem 51:2468–2480. doi: 10.1021/jm0707727 CrossRefGoogle Scholar
  19. 19.
    Medina-Franco JL, Maggiora GM, Giulianotti MA, Pinilla C, Houghten RA (2007) A similarity-based data-fusion approach to the visual characterization and comparison of compound databases. Chem Biol Drug Desig 70:393–412. doi: 10.1111/j.1747-0285.2007.00579.x CrossRefGoogle Scholar
  20. 20.
    Maggiora GM (2006) On outliers and activity cliffs: why QSAR often disappoints. J Chem Inf Model 46:1535–1535. doi: 10.1021/ci060117s Google Scholar
  21. 21.
    Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1:882. doi: 10.1038/nrd941 CrossRefGoogle Scholar
  22. 22.
    Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New YorkGoogle Scholar
  23. 23.
    Nikolova N, Jaworska J (2003) Approaches to measure chemical similarity—a review. QSAR Comb Sci 22:1006–1026. doi: 10.1186/1471-2121-8-S1-S6 Google Scholar
  24. 24.
    Medina-Franco JL, Martinez-Mayorga K, Bender A, Mari’n RM, Giulianotti MA, Pinilla C, Houghten RA (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49:477–491. doi: 10.1021/ci800379q CrossRefGoogle Scholar
  25. 25.
    Martinez-Mayorga K, Medina-Franco JL, Giulianotti MA, Pinilla C, Dooley CT, Appel JR, Houghten RA (2008) Conformation–opioid activity relationships of bicyclic guanidines from 3D similarity analysis. Bioorg Med Chem 16:5932–5938. doi: 10.1016/j.bmc.2008.04.061 Google Scholar
  26. 26.
    Smith JAM, Hunter JC, Hill RG, Hughes J (1989) A kinetic analysis of κ-opioid agonist binding using the selective radioligand [3H]U69593. J Neurochem 53:27–36. doi: 10.1111/j.1471-4159.1989.tb07291.x
  27. 27.
    Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z, Mracec M (2004) WOMBAT: world of molecular bioactivity. In: Oprea TI (ed) Chemoinformatics in drug discovery. Wiley-VCH, New York, pp 223–239Google Scholar
  28. 28.
    OpenEye Scientific Software (2007) ROCS v.2.3.1. OpenEye Scientific Software, Santa Fe (see http://www.eyesopen.com)
  29. 29.
    OpenEye Scientific Software (2007) OMEGA v.2.2.1. OpenEye Scientific Software, Santa Fe (http://www.eyesopen.com)
  30. 30.
    Ostresh JM, Schoner CC, Hamashin VT, Nefzi A, Meyer JP, Houghten RA (1998) Solid-phase synthesis of trisubstituted bicyclic guanidines via cyclization of reduced N-acylated dipeptides. J Org Chem 63:8622–8623. doi: 10.1208/aapsj080242 Google Scholar
  31. 31.
    Sykes MJ, McKinnon RA, Miners JO (2008) Prediction of metabolism by cytochrome P4502C9: alignment and docking studies of a validated database of substrates. J Med Chem 51:780–791. doi: 10.1021/jm7009793 Google Scholar
  32. 32.
    Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L (2005) The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 38:404–415. doi: 10.1186/1471-2105-8-331 Google Scholar
  33. 33.
    Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547. doi: 10.1021/ci800101j Google Scholar
  34. 34.
    Shanmugasundaram V, Maggiora GM (2001) Characterizing property and activity landscapes using an information-theoretic approach. Abstr Pap Am Chem Soc 222:32-CINFGoogle Scholar
  35. 35.
    Patterson DE, Cramer RD, Ferguson AM, Clark RD, Weinberger LE (1996) Neighborhood behavior: a useful concept for validation of “molecular diversity” descriptors. J Med Chem 39:3049–3059. doi: 10.1021/ci025635r Google Scholar
  36. 36.
    Whittle M, Gillet VJ, Willett P, Loesel J (2006) Analysis of data fusion methods in virtual screening: similarity and group fusion. J Chem Inf Model 46:2206–2219. doi: 10.1016/S1367-5931(00)00110-1 Google Scholar
  37. 37.
    Whittle M, Gillet VJ, Willett P, Loesel J (2006) Analysis of data fusion methods in virtual screening: theoretical model. J Chem Inf Model 46:2193–2205. doi: 10.1016/S1367-5931(00)00110-1

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Austin B. Yongye
    • 1
  • Clemencia Pinilla
    • 2
  • Jose L. Medina-Franco
    • 1
  • Marc A. Giulianotti
    • 1
  • Colette T. Dooley
    • 1
  • Jon R. Appel
    • 2
  • Adel Nefzi
    • 1
  • Thomas Scior
    • 3
  • Richard A. Houghten
    • 1
    • 2
  • Karina Martínez-Mayorga
    • 1
    Email author
  1. 1.Torrey Pines Institute for Molecular StudiesPort St LucieUSA
  2. 2.Torrey Pines Institute for Molecular StudiesSan DiegoUSA
  3. 3.Departamento de Farmacia, Facultad de Ciencias QuímicasBenemérita Universidad Autónoma de PueblaPueblaMexico

Personalised recommendations