Skip to main content
Springer Nature Link
Log in

Computation of 3D queries for ROCS based virtual screens

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Rapid overlay of chemical structures (ROCS) is a method that aligns molecules based on shape and/or chemical similarity. It is often used in 3D ligand-based virtual screening. Given a query consisting of a single conformation of an active molecule ROCS can generate highly enriched hit lists. Typically the chosen query conformation is a minimum energy structure. Can better enrichment be obtained using conformations other than the minimum energy structure? To answer this question a methodology has been developed called CORAL (COnformational analysis, Rocs ALignment). For a given set of molecule conformations it computes optimized conformations for ROCS screening. It does so by clustering all conformations of a chosen molecule set using pairwise ROCS combo scores. The best representative conformation is that which has the highest average overlap with the rest of the conformations in the cluster. It is these best representative conformations that are then used for virtual screening. CORAL was tested by performing virtual screening experiments with the 40 DUD (Directory of Useful Decoys) data sets. Both CORAL and minimum energy queries were used. The recognition capability of each query was quantified as the area under the ROC curve (AUC). Results show that the CORAL AUC values are on average larger than the minimum energy AUC values. This demonstrates that one can indeed obtain better ROCS enrichments with conformations other than the minimum energy structure. As a result, CORAL analysis can be a valuable first step in virtual screening workflows using ROCS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Rai BK, Tawa GJ, Katz AH, Humblet C (2009) Modeling G protein-coupled receptors for structure-based drug discovery using low-frequency normal modes for refinement of homology models: application to H3 antagonist. Proteins (accepted for publication)

  2. Palczewski K, Kumasaka T, Hori T, Behnke CA, Motoshima H, Fox BA, Trong IL, Teller DC, Okada T, Stenkamp RE, Yamamoto M, Miyano M (2000) Crystal structure of rhodopsin: A G. protein-coupled receptor. Science 289:739–745

    Article  CAS  Google Scholar 

  3. Cherezov V, Rosenbaum DM, Hanson MA, Rasmussen SG, Thian FS, Kobilka TS, Choi HJ, Kuhn P, Weis WI, Kobilka BK, Stevens RC (2007) High-resolution crystal structure of an engineered human B2-adrenergic G protein-coupled receptor. Science 318:1258–1265

    Article  CAS  Google Scholar 

  4. Jaakola V-P, Griffith MT, Hanson MA, Cherezov V, Chien EYT, Lane JR, Ijzerman AP, Stevens RC (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322:1211–1217

    Article  CAS  Google Scholar 

  5. Kim D, Xu D, Guo JT, Ellrott K, Xu Y (2003) PROSPECT II: protein structure prediction program for genome-scale applications. Protein Eng 16:641–650

    Article  CAS  Google Scholar 

  6. Petrey D, Xiang Z, Tang CL, Xie L, Gimpelev M et al (2003) Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling. Proteins 53(6):430–435

    Article  CAS  Google Scholar 

  7. Simons KT, Kooperberg C, Huang E, Baker D (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268:209–225

    Article  CAS  Google Scholar 

  8. Tresadern G, Bemporad D, Howe TA (2009) Comparison of ligand based virtual screening methods and application to corticotrophin releasing factor 1 receptor. J Mol Graph Model 27:860–870

    Article  CAS  Google Scholar 

  9. ROCS 2.3.1, OpenEye Scientific Software, Santa Fe, NM, 2007. http://www.eyesopen.com

  10. Grant JA, Gallard MA, Pickup BG (1996) A fast method of molecular shape comparison: a simple application of a Gaussian description of molecular shape. J Comput Chem 17:1653–1666

    Article  CAS  Google Scholar 

  11. Nicholls A, Grant JA (2005) Molecular shape and electrostatics in the encoding of relevant chemical information. J Comput-Aided Mol Des 19:661–686

    Article  CAS  Google Scholar 

  12. Freitas RF, Oprea TI, Montanari CA (2008) Two-dimensional QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L. Bioorg Med Chem 16:838–853

    Article  CAS  Google Scholar 

  13. Bostrom J, Greenwood JR, Gottfries J (2003) Assessing the performance of OMEGA with respect to retrieving bioactive conformations. J Mol Graph Model 21:449–462

    Article  CAS  Google Scholar 

  14. Bostrom J (2001) Reproducing the conformations of protein-bound ligands: a critical evaluation of several popular conformational searching tools. J Comput Aided Mol Des 15:1137–1152

    Article  CAS  Google Scholar 

  15. Diller DD, Merz KM Jr (2002) Can we separate active from inactive conformations? J Comput Aided Mol Des 16:105–112

    Article  CAS  Google Scholar 

  16. Hawkins PCD, Skillman GA, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50:74–82

    Article  CAS  Google Scholar 

  17. Kirchmair J, Distinto S, Markt P, Schuster D, Spitzer GM, Liedl KR, Wolber G (2009) How to optimize shape-based virtual screening: choosing the right query and including chemical information. J Chem Inf Model 49:678–692

    Article  CAS  Google Scholar 

  18. Perola E, Charifson PS (2004) Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem 45:2499–2510

    Article  Google Scholar 

  19. Putta S, Landrum GA, Penzotti JE (2005) Conformation mining: an algorithm for finding biologically relevant conformations. J Med Chem 48:3313–3318

    Article  CAS  Google Scholar 

  20. Rush TA (2005) Shaped-based 3-D scaffold hopping method and its application to a bacterial protein–protein interaction. J Med Chem 48:1489–1495

    Article  CAS  Google Scholar 

  21. Huang N, Shoichet B, Irwin J (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801

    Article  CAS  Google Scholar 

  22. 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. Applications to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547

    Article  CAS  Google Scholar 

  23. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    CAS  Google Scholar 

  24. OMEGA 2.2.1, OpenEye Scientific Software, Santa Fe, NM, 2007. http://www.eyesopen.com

  25. Bostrom J (2002) Reproducing the conformations of protein-bound ligands: a critical evaluation of several popular conformational searching tools. J Comput Aided Mol Des 15:1137

    Article  Google Scholar 

  26. Hawkins PCD, Warren GL, Skillman AG, Nicholls A (2008) How to do an evaluation: pitfalls and traps. J Comput Aided Mol Des 22:179–190

    Article  CAS  Google Scholar 

  27. Sokal RR, Rohlf FJ (1995) Biometry: the principles and practice of statistics in biological research. W.H. Freeman, New York

    Google Scholar 

  28. Turner DB, Tyrell SM, Willett P (1997) Rapid quantification of molecular diversity for selective database acquisition. J Chem Inf Comput Sci 37:18–22

    CAS  Google Scholar 

  29. 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

    Article  CAS  Google Scholar 

  30. Bostrom J, Hogner A, Schmitt S (2006) Do structurally similar ligands bind in a similar fashion? J Med Chem 49:6716–6725

    Article  Google Scholar 

  31. OEChem-C++ theory manual, OEMCSSEARCH. OpenEye Scientific Software: Santa Fe, NM, 2006. http://www.eyesopen.com

  32. Nicholls A (2008) What do we know and when do we know it? J Comput Aided Mol Des 22:239–255

    Article  CAS  Google Scholar 

  33. Hassan M, Brown RD, Varna-O’Brien S, Rogers D (2006) Cheminformatics analysis and learning in a data pipelining environment. Mol Divers 10:283–299

    Article  CAS  Google Scholar 

  34. Scitegic Inc, Pipeline Pilot Version 7.5.2.300, 2009. http://www.scitegic.com

Download references

Acknowledgments

The authors would like to thank Will Somers and Tarek Mansour of Wyeth Chemical Sciences for their support, Dave Diller for manuscript suggestions, Ramaswamy Nilikantan for help with the diversity analysis and Youping Huang for help in performing the statistical analysis.

Author information

Authors and Affiliations

  1. Chemical Sciences, Wyeth Research, CN 8000, Princeton, NJ, USA

    Gregory J. Tawa & Christine Humblet

  2. Chemical Sciences, Wyeth Research, 200 Cambridge Park Drive, Cambridge, MA, 02140, USA

    J. Christian Baber

Authors
  1. Gregory J. Tawa
    View author publications

    Search author on:PubMed Google Scholar

  2. J. Christian Baber
    View author publications

    Search author on:PubMed Google Scholar

  3. Christine Humblet
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Gregory J. Tawa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tawa, G.J., Baber, J.C. & Humblet, C. Computation of 3D queries for ROCS based virtual screens. J Comput Aided Mol Des 23, 853–868 (2009). https://doi.org/10.1007/s10822-009-9302-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-009-9302-3

Keywords