Advertisement

Journal of Molecular Modeling

, Volume 11, Issue 6, pp 516–524 | Cite as

Exploring 3D-QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists+

  • Prosenjit Bhattacharya
  • J. Thomas Leonard
  • Kunal Roy
Original Paper

Abstract

Binding affinity data [Bioorg Med Chem (2004) 12:613–623] of thiazole and thiadiazole derivatives (n = 30) for the human adenosine A3 receptor subtype have been subjected to 3D-QSAR (Quantitative structure–activity relationships) analyses by molecular shape analysis (MSA) and molecular field analysis (MFA) techniques using Cerius2 Version 4.8. In the case of the MSA, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape-reference compound (based on the active conformation); (4) performing pairwise molecular superimposition using the maximum common subgroup (MCSG) method; (5) measuring molecular shape commonality using MSA descriptors; (6) determining other molecular features by calculating spatial, electronic and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by genetic function algorithm (GFA) or stepwise regression. The best 3D-QSAR equation (MSA) obtained from GFA technique shows 70.0% predicted variance (leave-one-out) and 77.7% explained variance. This equation shows the importance of Jurs descriptors (atomic charge weighted positive surface area, relative negative charge and relative positive charge surface area), partial moment of inertia, energy of the most stable conformer and the ratio of common overlap steric volume to volume of individual molecules. In the case of stepwise regression, the best relation showed 46.1% predicted variance and 72.3% explained variance. In the case of MFA, the major steps were (1) generating conformers and energy minimization; (2) matching atoms using a maximum common substructure (MCS) search and aligning molecules using the default options; (3) setting MFA preferences (rectangular grid with 2 Å step size, charges by the Gasteiger algorithm, H+ and CH3 as probes); (4) creating the field; (5) analysis by the Genetic partial least squares (G/PLS) method. The equation obtained was of excellent statistical quality: 96.1% explained variance and 71.6% predicted variance. Statistically reliable 3D-QSAR models obtained from this study suggest that these techniques could be useful to design potent A3 receptor antagonists.

Figure Adenosine A3 binding affinity data of thiazole and thiadiazole derivatives have been subjected to 3D-QSAR study using molecular shape analysis and molecular field analysis.

Keywords

QSAR MSA MFA Thiazole Thiadiazole Adenosine A3 receptor 

Abbreviations

QSAR

Quantitative structure–activity relationships

GFA

Genetic function approximation

G/PLS

Genetic partial least squares

MSA

Molecular shape analysis

MFA

Molecular field analysis

Notes

Acknowledgements

One of the authors (JTL) thanks the AICTE, New Delhi for a QIP fellowship. KR thanks the AICTE, New Delhi for a financial grant under the Career Award for Young Teachers scheme. The authors are thankful for the valuable suggestions of the Referee, which helped the authors to improve the manuscript substantially.

References

  1. 1.
    Palmer TM, Ferguson G, Watterson KR (2003) Drug Dev Res 58:302–314CrossRefGoogle Scholar
  2. 2.
    Song Y, Coupar IM, Iskander MN (2001) Quant Struct Act Relat 20:23–30CrossRefGoogle Scholar
  3. 3.
    Nakazawa T, Koshiba M, Kosaka H, Tsuji G, Nakamachi Y, Saura R, Kurosaka M, Tanaka Y, Kumagai S (2003) Drug Dev Res 58:368–376CrossRefGoogle Scholar
  4. 4.
    Inbe H, Watanabe S, Miyawaki M, Tanabe E, Encinas JA (2004) J Biol Chem 279:19790–19799PubMedCrossRefGoogle Scholar
  5. 5.
    Shneyvays V, Safran N, Halili-Rutman I, Shainberg A (2000) Drug Dev Res 50:324–337CrossRefGoogle Scholar
  6. 6.
    Lee HT, Gallos G, Nasr SH, Emala CW (2004) J Am Soc Nephrol 15:102–111PubMedCrossRefGoogle Scholar
  7. 7.
    Gutierrez-de-Teran H, Centeno NB, Pastor M, Sanz F (2004) Proteins 54:705–715PubMedCrossRefGoogle Scholar
  8. 8.
    Merighi S, Mirandola P, Milani D, Varani K, Gessi S, Klotz KN, Leung E, Baraldi PG, Borea PA (2003) Drug Dev Res 58:377–385CrossRefGoogle Scholar
  9. 9.
    Chen JF, Schwarzschild MA (2003) Drug Dev Res 58:354–367CrossRefGoogle Scholar
  10. 10.
    Phillis JW (2001) Drug Dev Res 52:331–336CrossRefGoogle Scholar
  11. 11.
    Feoktistov I, Goldstein A, Sheller JR, Schwartz LB, Biaggioni I (2003) Drug Dev Res 58:461–471CrossRefGoogle Scholar
  12. 12.
    Baraldi PG, Tabrizi MA, Fruttarolo F, Bovero A, Avitabile B, Preti D, Romagnoli R, Merighi S, Gessi S, Varani K, Borea PA (2003) Drug Dev Res 58:315–329CrossRefGoogle Scholar
  13. 13.
    de Mendonca A, Ribeiro JA (2001) Drug Dev Res 52:283–290CrossRefGoogle Scholar
  14. 14.
    Doytchinova I (2001) J Comput Aided Mol Des 15:29–39CrossRefPubMedGoogle Scholar
  15. 15.
    Baraldi PG, Borea PA, Bergonzoni M, Cacclari B, Ongini E, Recanatini M, Spalluto G (1999) Drug Dev Res 46:126–133CrossRefGoogle Scholar
  16. 16.
    Doytchinova I, Valkova I, Natcheva R (2001) Quant Struct Act Relat 20:124–129CrossRefGoogle Scholar
  17. 17.
    El-Taher S, El-Sawy KM, Hilal R (2002) J Chem Inf Comput Sci 42:386–392PubMedCrossRefGoogle Scholar
  18. 18.
    Roy K (2003) Indian J Chem 42B:1485–1496Google Scholar
  19. 19.
    Roy K (2003) QSAR Comb Sci 22:614–621CrossRefGoogle Scholar
  20. 20.
    Roy K, Leonard JT, Sengupta C (2004) Bioorg Med Chem Lett 14:3705–3709PubMedCrossRefGoogle Scholar
  21. 21.
    Jung K-Y, Kim S-K, Gao Z-G, Gross AS, Melman N, Jacobson KA, Kim Y-C (2004) Bioorg Med Chem 12:613–623PubMedCrossRefGoogle Scholar
  22. 22.
    Cerius2 Version 4.8 (2002) Accelrys Inc, San Diego, USA; http://www.accelrys.com/cerius2
  23. 23.
    Hopfinger AJ, Tokarsi JS (1997) In: Charifson PS (ed) Practical applications of computer-aided drug design. Marcel Dekker Inc., New York, pp 105–163Google Scholar
  24. 24.
    Rogers D, Hopfinger AJ (1994) J Chem Inf Comput Sci 34:854–866CrossRefGoogle Scholar
  25. 25.
    Fan Y, Shi LM, Kohn KW, Pommier Y, Weinstein JN (2001) J Med Chem 44:3254–3263CrossRefPubMedGoogle Scholar
  26. 26.
    Wold S (1995) In: van de Waterbeemd H (ed) Chemometric methods in molecular design. VCH, Weinheim, pp 195–218Google Scholar
  27. 27.
    Snedecor GW, Cochran WG (1967) Statistical methods. Oxford& IBH Publishing Co. Pvt. Ltd, New Delhi, pp 381–418Google Scholar
  28. 28.
    Wold S, Eriksson L (1995) In: van de Waterbeemd H (ed) Chemometric methods in molecular design. VCH, Weinheim, pp 312–317Google Scholar
  29. 29.
    Debnath AK (2001) Combinatorial library design and evaluation. Marcel Dekker Inc., New York, pp 73–129Google Scholar
  30. 30.
    Bhattacharya P, Leonard JT, Roy K (2005) Bioorg Med Chem 13:1159–1165. Doi http://dx.doi.org/10.1016/j.bmc.2004.11.022 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Prosenjit Bhattacharya
    • 1
  • J. Thomas Leonard
    • 1
  • Kunal Roy
    • 1
  1. 1.Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical TechnologyJadavpur UniversityKolkataIndia

Personalised recommendations