Skip to main content
SpringerLink
Log in

Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

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

Abstract

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ ~ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ ~ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Koes D et al (2012) Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists. PLoS ONE 7(3):e32839

    Article  CAS  Google Scholar 

  2. Domling A, Wang W, Wang K (2012) Chemistry and biology of multicomponent reactions. Chem Rev 112(6):3083–3135

    Article  CAS  Google Scholar 

  3. Gathiaka S et al (2016) D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 30(9):651–668

    Article  CAS  Google Scholar 

  4. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3(11):935–949

    Article  CAS  Google Scholar 

  5. Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53(8):1893–1904

    Article  CAS  Google Scholar 

  6. Trott O, Olson AJ (2009) Software news and update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    Google Scholar 

  7. Koes DR, Camacho CJ (2012) ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40(W1):W409–W414

    Article  CAS  Google Scholar 

  8. Koes DR, Pabon NA, Deng X, Phillips MA, Camacho CJ, Wang S (2015) A Teach-Discover-Treat application of ZincPharmer: an online interactive pharmacophore modeling and virtual screening tool. PLoS ONE 10(8):e0134697

    Article  Google Scholar 

  9. Ye Z, Baumgartner MP, Wingert BM, Camacho CJ (2016) Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R grand challenge. J Comput Aided Mol Des 30(9):695–706

    Article  CAS  Google Scholar 

  10. Smith RD et al (2016) CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model 56(6):1022–1031

    Article  CAS  Google Scholar 

  11. Temiz NA, Camacho CJ (2009) Experimentally based contact energies decode interactions responsible for protein? DNA affinity and the role of molecular waters at the binding interface. Nucleic Acids Res 37(12):4076–4088

    Article  CAS  Google Scholar 

  12. O’boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33

    Article  Google Scholar 

  13. Berman HM et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  Google Scholar 

  14. The PyMOL molecular graphics system, version 1.8 Schrödinger, LLC. [Online]. https://www.pymol.org/citing. Accessed 02 May 2017

  15. Chen X, Liu M, Gilson MK (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4(8):719–725

    Article  CAS  Google Scholar 

  16. Enyedy IJ et al (2016) Discovery of biaryls as RORγ inverse agonists by using structure-based design. Bioorg Med Chem Lett 26(10):2459–2463

    Article  CAS  Google Scholar 

  17. René O et al (2015) Minor structural change to tertiary sulfonamide RORc ligands led to opposite mechanisms of action. ACS Med Chem Lett 6(3):276–281

    Article  Google Scholar 

  18. van Niel MB et al (2014) A reversed sulfonamide series of selective RORc inverse agonists. Bioorg Med Chem Lett 24(24):5769–5776

    Article  Google Scholar 

  19. Hawkins P. C. D., Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J Chem Inf Model 50(4):572–584

    Article  CAS  Google Scholar 

  20. Tosco P, Balle T, Shiri F (2011) Open3DALIGN: an open-source software aimed at unsupervised ligand alignment. J Comput Aided Mol Des 25(8):777–783

    Article  CAS  Google Scholar 

  21. Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260

    Article  Google Scholar 

  22. Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, Cheatham TE, Darden TA, Duke RE, Gohlke H, Goetz AW, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossváry I, Kovalenko A, Lee TS, LeGrand S, Luchko T, Luo R, Madej B, Merz KM, Paesani F, Roe DR, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling CL, Smith W, Swails J, Walker, Wang J, Wolf RM, Wu X, Kollman PA (2014) Amber 14. University of California, San Francisco

  23. Temiz NA, Trapp A, Prokopyev OA, Camacho CJ (2009) Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting. Bioinformatics 26(3):319–325

    Article  Google Scholar 

  24. Kolář M, Hobza P (2012) On extension of the current biomolecular empirical force field for the description of halogen bonds. J Chem Theory Comput 8(4):1325–1333

    Article  Google Scholar 

  25. Harder E et al (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296

    Article  CAS  Google Scholar 

  26. Trott O, Olson AJ (2009) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461

  27. Richter HGF et al (2011) Optimization of a novel class of benzimidazole-based farnesoid X receptor (FXR) agonists to improve physicochemical and ADME properties. Bioorg Med Chem Lett 21(4):1134–1140

    Article  CAS  Google Scholar 

  28. Mi LZ et al Structural basis for bile acid binding and activation of the nuclear receptor FXR. Mol Cell 11:1093–1100

  29. Akwabi-Ameyaw A et al (2011) Conformationally constrained farnesoid X receptor (FXR) agonists: alternative replacements of the stilbene. Bioorg Med Chem Lett 21(20):6154–6160

    Article  CAS  Google Scholar 

  30. Smith RD et al (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51(9):2115–2131

    Article  CAS  Google Scholar 

  31. Misini Ignjatovic M, Caldararu O, Dong G, Munoz-Gutierrez C, Adasme-Carreno F, Ryde U (2016) Binding-affinity predictions of HSP90 in the D3R grand challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. J Comput Aided Mol Des 30(9):707–730

    Article  CAS  Google Scholar 

  32. Deng N et al (2016) Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R grand challenge 2015. J Comput Aided Mol Des 30(9):743–751

    Article  CAS  Google Scholar 

  33. Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  34. Cole DJ, Tirado-Rives J, Jorgensen WL (2014) Enhanced Monte Carlo sampling through replica exchange with solute tempering. J Chem Theory Comput 10:565–571

    Article  CAS  Google Scholar 

  35. Kang YN, Stuckey JA, Heat shock protein 90 bound to CS319. http://www.rcsb.org/pdb/explore.do?structureId=4yky

  36. Crawford TD et al (2014) Discovery of selective 4-amino-pyridopyrimidine inhibitors of MAP4K4 using fragment-based lead identification and optimization. J Med Chem 57(8):3484–3493

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank OpenEye Scientific for providing an academic license for their software. The work was funded by U.S. National Institutes of Health Grant Numbers GM097082 and T32EB009403.

Author information

Authors and Affiliations

  1. Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Bentley M. Wingert & Carlos J. Camacho

  2. Department of Drug Design, University of Groningen, Groningen, The Netherlands

    Rick Oerlemans

Authors
  1. Bentley M. Wingert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rick Oerlemans
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos J. Camacho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos J. Camacho.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 15 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wingert, B.M., Oerlemans, R. & Camacho, C.J. Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges. J Comput Aided Mol Des 32, 287–297 (2018). https://doi.org/10.1007/s10822-017-0065-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-017-0065-y

Keywords

Search

Navigation