Skip to main content
Log in

Conformational ensemble comparison for small molecules in drug discovery

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Quantification of three-dimensional similarity between small molecules is a fundamental tool of rational drug design. However, there are no widely-adopted scoring approaches for comparing whole conformational ensembles between molecules. Such scores would be desirable for scenarios in which properties of a molecule have been measured (e.g. activity against a target) but the relevant three dimensional structure is not known. In this study, a set of three complementary ensemble comparison scores is proposed. These are the maximum similarity between any pair of conformations; the proportion of the whole set of the conformations that are matched to within a threshold 3D similarity score; and the average value over these matched conformations of the molecular shape descriptor ‘σ-fct’, introduced by Ballester et al. The utility of this scoring set is demonstrated in three case studies. The first is an attempt to discriminate between the conformational behaviours of a series of compounds with varying types of cyclisations and other conformationally-significant modifications; the second is an analysis of the more and less active members of a series of GPR119 agonists plus an analysis of a series of orexin-1 antagonists; and the third case study is an attempt to obtain enrichment of active against inactive compounds for a subset of the DUD·E dataset, by ensemble comparison against an active reference compound.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Shin W-H et al (2015) Three-dimensional compound comparison methods and their application in drug discovery. Molecules 20(7):12841–12862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ballester PJ, Richards WG (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. J Comput Chem 28(10):1711–1723

    Article  CAS  PubMed  Google Scholar 

  3. Klabunde T, Giegerich C, Evers A (2012) MARS: computing three-dimensional alignments for multiple ligands using pairwise similarities. J Chem Inf Model 52(8):2022–2030

    Article  CAS  PubMed  Google Scholar 

  4. Cresset BioMolecular Discovery Ltd., Forge. version 10.5.0 (2017)

  5. Dixon SL, Smondyrev AM, Rao SN (2006) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67(5):370–372

    Article  CAS  PubMed  Google Scholar 

  6. Tiberti M et al (2015) ENCORE: Software for Quantitative ensemble comparison. PLoS Comput Biol 11(10):e1004415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang S, Salmon L, Al-Hashimi HM (2014) Measuring similarity between dynamic ensembles of biomolecules. Nat Methods 11(5):552–554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wolfe KC, Chirikjian GS (2012) Quantitative comparison of conformational ensembles. Entropy 14(2):213–232

    Article  CAS  Google Scholar 

  9. Lindorff-Larsen K, Ferkinghoff-Borg J (2009) Similarity measures for protein ensembles. PLoS ONE 4(1):e4203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jahn A et al (2011) 4D Flexible Atom-Pairs: an efficient probabilistic conformational space comparison for ligandbased virtual screening. J Cheminf 3(1):23

    Article  CAS  Google Scholar 

  11. 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 47(10):2499–2510

    Article  CAS  PubMed  Google Scholar 

  12. Avgy-David HH, Senderowitz H (2015) Toward focusing conformational ensembles on bioactive conformations: a molecular mechanics/quantum mechanics study. J Chem Inf Model 55(10):2154–2167

    Article  CAS  PubMed  Google Scholar 

  13. Habgood M (2017) Bioactive focus in conformational ensembles: a pluralistic approach. J Comput Aided Mol Des 31(12):1073–1083

    Article  CAS  PubMed  Google Scholar 

  14. Sato K et al (2014) Discovery of a novel series of indoline carbamate and indolinylpyrimidine derivatives as potent GPR119 agonists. Bioorg Med Chem 22(5):1649–1666

    Article  CAS  PubMed  Google Scholar 

  15. Coleman PJ et al (2012) Discovery of [(2R,5R)-5-{[(5-fluoropyridin-2-yl)oxy]methyl}-2-methylpiperidin-1-yl][5-methyl-2-(pyrimidin-2-yl)phenyl]methanone (MK-6096): a dual orexin receptor antagonist with potent sleep-promoting properties. ChemMedChem 7(3):415–424

    Article  CAS  PubMed  Google Scholar 

  16. Blundell CD, Nowak T, Watson MJ (2016) Measurement, interpretation and use of free ligand solution conformations in drug discovery. Prog Med Chem 55(Chap 2):45–147

    Article  PubMed  Google Scholar 

  17. Mysinger MM et al (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cicero DO, Barbato G, Bazzo R (1995) NMR analysis of molecular flexibility in solution: a new method for the study of complex distributions of rapidly exchanging conformations. Application to a 13-residue peptide with an 8-residue loop. J Am Chem Soc 117(3):1027–1033

    Article  CAS  Google Scholar 

  19. Blundell CD, Packer MJ, Almond A (2013) Quantification of free ligand conformational preferences by NMR and their relationship to the bioactive conformation. Bioorg Med Chem 21(17):4976–4987

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kirchmair J et al (2006) Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model 46(4):1848–1861

    Article  CAS  PubMed  Google Scholar 

  21. Agrafiotis DK et al (2007) Conformational sampling of bioactive molecules: a comparative study. J Chem Inf Model 47(3):1067–1086

    Article  CAS  PubMed  Google Scholar 

  22. Ebejer J-P, Morris GM, Deane CM (2012) Freely available conformer generation methods: how good are they? J Chem Inf Model 52(5):1146–1158

    Article  CAS  PubMed  Google Scholar 

  23. Chen I-J, Foloppe N (2013) Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery. Bioorg Med Chem 21(24):7898–7920

    Article  CAS  PubMed  Google Scholar 

  24. Foloppe N, Chen I-J (2016) Towards understanding the unbound state of drug compounds: implications for the intramolecular reorganization energy upon binding. Bioorg Med Chem 24(10):2159–2189

    Article  CAS  PubMed  Google Scholar 

  25. Hawkins PCD (2017) Conformation generation: the state of the art. J Chem Inf Model 57(8):1747–1756

    Article  CAS  PubMed  Google Scholar 

  26. Labute P (2010) LowModeMD: implicit low-mode velocity filtering applied to conformational search of macrocycles and protein loops. J Chem Inf Model 50(5):792–800

    Article  CAS  PubMed  Google Scholar 

  27. Chemical Computing Group Inc., Molecular Operating Environment, version 2016.08

  28. Wojciechowski M, Lesyng B (2004) Generalized Born model: analysis, refinement and applications to proteins. J Phys Chem B 108(47):18368–18376

    Article  CAS  Google Scholar 

  29. Hawkins PCD, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52(11):2919–2936

    Article  CAS  PubMed  Google Scholar 

  30. OpenEye Scientific Software Inc., ROCS, version 3.2.1.4 (2015)

  31. Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82

    Article  CAS  PubMed  Google Scholar 

  32. ROCS. OpenEye Scientific Software

  33. Knime: Konstanz Information Miner (2016) Knime GmbH

  34. Steinbeck C et al (2006) Recent developments of the chemistry development kit (CDK)—an open-source Java library for chemo- and bioinformatics. Curr Pharm Des 12(17):2111–2120

    Article  CAS  PubMed  Google Scholar 

  35. Beisken S et al (2013) KNIME-​CDK: workflow-​driven cheminformatics. BMC Bioinform 14(257):1–4

    Google Scholar 

  36. Groom CR et al (2016) The Cambridge structural database. Acta Crystallogr B 72(2):171–179

    Article  CAS  Google Scholar 

  37. DUD·E: A database of useful decoys: enhanced. http://dude.docking.org/. Accessed 1 March 2018

  38. Bento AP et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 32(D1):D1083–D1090

    Article  CAS  Google Scholar 

  39. Kruger DM, Evers A (2010) Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 5(1):148–158

    Article  CAS  PubMed  Google Scholar 

  40. Nicholls A (2008) What do we know and when do we know it? J Comput Aided Mol Des 22(3–4):239–255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22(3–4):133–139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Michelle Southey and Mike Bodkin for comments on the manuscript, and for general computational chemistry insight.

Funding

The author is an employee of Evotec (UK) Ltd.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matthew Habgood.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 16 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Habgood, M. Conformational ensemble comparison for small molecules in drug discovery. J Comput Aided Mol Des 32, 841–852 (2018). https://doi.org/10.1007/s10822-018-0132-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-018-0132-z

Keywords

Navigation