Skip to main content
SpringerLink
Log in

Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening

  • Protocol
  • First Online:
Protein-Ligand Interactions and Drug Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2266))

  • 1927 Accesses

Abstract

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein’s ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Meng X-Y, Zhang H-X, Mezei M et al (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7:146–157

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  PubMed  Google Scholar 

  4. Plewczynski D, Łaźniewski M, Augustyniak R et al (2011) Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J Comput Chem 32:742–755

    Article  CAS  PubMed  Google Scholar 

  5. Wang R, Lu Y, Wang S (2003) Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 46:2287–2303

    Article  CAS  PubMed  Google Scholar 

  6. Ahinko M, Niinivehmas S, Jokinen E et al (2018) Suitability of MMGBSA for the selection of correct ligand binding modes from docking results. Chem Biol Drug Des:1–17

    Google Scholar 

  7. Pohorille A and Chipot C (2007) Free Energy Calculations: Theory and Applications in Chemistry and Biology - Foreword

    Google Scholar 

  8. Kolb P, Irwin J (2009) Docking screens: right for the right reasons? Curr Top Med Chem 9:755–770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11–26

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  12. Kirchmair J, Distinto S, Markt P et al (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  PubMed  Google Scholar 

  13. Kurkinen ST, Niinivehmas S, Ahinko M et al (2018) Improving docking performance using negative image-based rescoring. Front Pharmacol 9:1–15

    Article  CAS  Google Scholar 

  14. Niinivehmas SP, Salokas K, Lätti S et al (2015) Ultrafast protein structure-based virtual screening with panther. J Comput Aided Mol Des 29:989–1006

    Article  CAS  PubMed  Google Scholar 

  15. Korb O, Stützle T, Exner TE (2009) Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 49:84–96

    Article  CAS  PubMed  Google Scholar 

  16. Vainio MJ, Puranen JS, Johnson MS (2009) ShaEP: molecular overlay based on shape and electrostatic potential. J Chem Inf Model 49:492–502

    Article  CAS  PubMed  Google Scholar 

  17. Allen WJ, Balius TE, Mukherjee S et al (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36:1132–1156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Morris G, Huey R (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Friesner RA, Banks JL, Murphy RB 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  PubMed  Google Scholar 

  21. Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  PubMed  Google Scholar 

  22. Kurkinen ST, Lätti S, Pentikäinen OT et al (2019) Getting Docking into Shape Using Negative Image-Based Rescoring. J Chem Inf Model:acs.jcim.9b00383

    Google Scholar 

  23. Virtanen SI, Pentikäinen OT (2010) Efficient virtual screening using multiple protein conformations described as negative images of the ligand-binding site. J Chem Inf Model 50:1005–1011

    Article  CAS  PubMed  Google Scholar 

  24. Niinivehmas SP, Virtanen SI, Lehtonen JV et al (2011) Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors. J Chem Inf Model 51:1353–1363

    Article  CAS  PubMed  Google Scholar 

  25. Ahinko M, Kurkinen ST, Niinivehmas SP et al (2019) A practical perspective: the effect of ligand conformers on the negative image-based screening. Int J Mol Sci 20:2779

    Article  CAS  PubMed Central  Google Scholar 

  26. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Burley SK, Berman HM, Kleywegt GJ et al (2017) Protein data Bank (PDB): the single global macromolecular structure archive. Methods Mol Biol 1607:627–641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. In: Current protocols in bioinformatics. John Wiley & Sons, Inc., Hoboken, pp 5.6.1–5.6.37

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:1100–1107

    Article  CAS  Google Scholar 

  32. Lehtonen JV, Still D-J, Rantanen V-V et al (2004) BODIL: a molecular modeling environment for structure-function analysis and drug design. J Comput Aided Mol Des 18:401–419

    Article  CAS  PubMed  Google Scholar 

  33. Word JM, Lovell SC, Richardson JS et al (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285:1735–1747

    Article  CAS  PubMed  Google Scholar 

  34. Dolinsky TJ, Czodrowski P, Li H et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:522–525

    Article  Google Scholar 

  35. Jurrus E, Engel D, Star K et al (2018) Improvements to the APBS biomolecular solvation software suite. Protein Sci 27:112–128

    Article  CAS  PubMed  Google Scholar 

  36. O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:1–14

    CAS  Google Scholar 

  37. Zoete V, Schuepbach T, Bovigny C et al (2016) Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape. J Comput Chem 37:437–447

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  39. Halgren TA (2009) Identifying and characterizing binding sites and assessing Druggability. J Chem Inf Model 49:377–389

    Article  CAS  PubMed  Google Scholar 

  40. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69:146–148

    Article  CAS  PubMed  Google Scholar 

  41. Durrant JD, Augusto C, Oliveira F De, et al (2011) Journal of molecular graphics and modelling short communication POVME : an algorithm for measuring binding-pocket volumes. J Mol Graph Model 29:773–776

    Google Scholar 

  42. Durrant JD, Votapka L, and Amaro RE (2014) POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics

    Google Scholar 

  43. Wagner JR, Sørensen J, Hensley N et al (2017) POVME 3.0: software for mapping binding pocket flexibility. J Chem Theory Comput 13:4584–4592

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Guilloux V, Le Schmidtke P, Tuffery P (2009) Fpocket : An open source platform for ligand pocket detection. 11:1–11

    Google Scholar 

  45. Schmidtke P, Bidon-Chanal A, Luque FJ et al (2011) MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. 27:3276–3285

    Google Scholar 

  46. Kleywegt GJ, Jones TA (1994) Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr Sect D Biol Crystallogr 50:178–185

    Article  CAS  Google Scholar 

  47. Chovancova E, Pavelka A, Benes P et al (2012) CAVER 3 . 0 : A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures. 8:23–30

    Google Scholar 

  48. Lätti S, Niinivehmas S, Pentikäinen OT (2016) Rocker: open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization. J Cheminform 8:1–5

    Article  Google Scholar 

  49. Chaput L, Martinez-Sanz J, Saettel N et al (2016) Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance. J Cheminform 8:1–17

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Liu Z, Wang R, Li X et al (2010) Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J Comput Chem 31:2109–2125

    Article  PubMed  CAS  Google Scholar 

  51. Wang Z, Sun H, Yao X et al (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964–12975

    Article  CAS  PubMed  Google Scholar 

  52. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565

    Article  CAS  PubMed  Google Scholar 

  53. Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767

    Article  CAS  PubMed  Google Scholar 

  54. Wang JL, Limburg D, Graneto MJ et al (2010) The novel benzopyran class of selective cyclooxygenase-2 inhibitors. Part 2: the second clinical candidate having a shorter and favorable human half-life. Bioorganic Med Chem Lett 20:7159–7163

    Article  CAS  Google Scholar 

  55. Finley JB, Atigadda VR, Duarte F et al (1999) Novel aromatic inhibitors of influenza virus neuraminidase make selective interactions with conserved residues and water molecules in the active site. 4071:1107–1119

    Google Scholar 

Download references

Acknowledgments

The Finnish IT Center for Science (CSC) is acknowledged for generous computational resources.

Author information

Authors and Affiliations

  1. Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, Turku, Finland

    Olli T. Pentikäinen & Pekka A. Postila

  2. Aurlide Ltd., Turku, Finland

    Olli T. Pentikäinen & Pekka A. Postila

Authors
  1. Olli T. Pentikäinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pekka A. Postila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pekka A. Postila .

Editor information

Editors and Affiliations

  1. Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

    Flavio Ballante

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Pentikäinen, O.T., Postila, P.A. (2021). Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening. In: Ballante, F. (eds) Protein-Ligand Interactions and Drug Design. Methods in Molecular Biology, vol 2266. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1209-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1209-5_8

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1208-8

  • Online ISBN: 978-1-0716-1209-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation