Skip to main content

Evaluation of Protein–Ligand Docking by Cyscore

  • Protocol
  • First Online:
Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1762))

Abstract

Protein–ligand docking is a powerful method in drug discovery. The reliability of docking can be quantified by RMSD between a docking structure and an experimentally determined one. However, most experimentally determined structures are not available in practice. Evaluation by scoring functions is an alternative for assessing protein–ligand docking results. This chapter first provides a brief introduction to scoring methods used in docking. Then details are provided on how to use Cyscore programs. Finally it describes a case study for evaluation of protein–ligand docking.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
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. Glaab E (2016) Building a virtual ligand screening pipeline using free software: a survey. Brief Bioinform 17(2):352–366

    Article  CAS  PubMed  Google Scholar 

  2. Sousa SF, Ribeiro AJ, Coimbra JT, Neves RP, Martins SA, Moorthy NS, Fernandes PA, Ramos MJ (2013) Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. Curr Med Chem 20(18):2296–2314

    Article  CAS  PubMed  Google Scholar 

  3. Blundell TL (1996) Structure-based drug design. Nature 384(6604 Suppl):23–26

    CAS  PubMed  Google Scholar 

  4. Grinter SZ, Zou X (2014) Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules 19(7):10150–10176

    Article  PubMed  Google Scholar 

  5. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905–919

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li H, Leung KS, Ballester PJ, Wong MH (2014) istar: a web platform for large-scale protein-ligand docking. PLoS One 9(1):e85678

    Article  PubMed  PubMed Central  Google Scholar 

  7. Allen WJ, Balius TE, Mukherjee S, Brozell SR, Moustakas DT, Lang PT, Case DA, Kuntz ID, Rizzo RC (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36(15):1132–1156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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(2):455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang C, Zhang Y (2017) Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J Comput Chem 38(3):169–177

    Article  PubMed  Google Scholar 

  10. Danishuddin M, Khan AU (2015) Structure based virtual screening to discover putative drug candidates: necessary considerations and successful case studies. Methods 71:135–145

    Article  CAS  PubMed  Google Scholar 

  11. Li C, Wang Z, Cao Y, Wang L, Ji J, Chen Z, Deng T, Jiang T, Cheng G, Qin FX-F (2017) Screening for novel small-molecule inhibitors targeting the assembly of influenza virus polymerase complex by a bimolecular luminescence complementation-based reporter system. J Virol 91:e02282-16

    Article  PubMed  PubMed Central  Google Scholar 

  12. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38

    Article  CAS  PubMed  Google Scholar 

  13. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612

    Article  CAS  PubMed  Google Scholar 

  14. Pencheva T, Soumana OS, Pajeva I, Miteva MA (2010) Post-docking virtual screening of diverse binding pockets: comparative study using DOCK, AMMOS, X-Score and FRED scoring functions. Eur J Med Chem 45(6):2622–2628

    Article  CAS  PubMed  Google Scholar 

  15. Cao Y, Li L (2014) Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinformatics 30(12):1674–1680

    Article  CAS  PubMed  Google Scholar 

  16. Velec HF, Gohlke H, Klebe G (2005) DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48(20):6296–6303

    Article  CAS  PubMed  Google Scholar 

  17. Grinter SZ, Yan C, Huang SY, Jiang L, Zou X (2013) Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 community structure-activity resource benchmark. J Chem Inf Model 53(8):1905–1914

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 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(1):11–26

    Article  CAS  PubMed  Google Scholar 

  19. Li H, Leung KS, Wong MH, Ballester PJ (2014) Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinformatics 15:291

    Article  PubMed  PubMed Central  Google Scholar 

  20. Huang SY, Zou X (2010) Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions. J Chem Inf Model 50(2):262–273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang R, Lu Y, Fang X, Wang S (2004) An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J Chem Inf Comput Sci 44(6):2114–2125

    Article  CAS  PubMed  Google Scholar 

  22. Jackson MB (2016) The hydrophobic effect in solute partitioning and interfacial tension. Sci Rep 6:19265

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ball P (2008) Water as an active constituent in cell biology. Chem Rev 108(1):74–108

    Article  CAS  PubMed  Google Scholar 

  24. Chandler D (2005) Interfaces and the driving force of hydrophobic assembly. Nature 437(7059):640–647

    Article  CAS  PubMed  Google Scholar 

  25. Tanford C (1979) Interfacial free energy and the hydrophobic effect. Proc Natl Acad Sci U S A 76(9):4175–4176

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tolman RC (1949) The effect of droplet size on surface tension. J Chem Phys 3(17):5

    Google Scholar 

  27. Nicholls A, Sharp KA, Honig B (1991) Protein folding and association: insights from the interfacial and thermodynamic properties of hydrocarbons. Proteins 11(4):281–296

    Article  CAS  PubMed  Google Scholar 

  28. Sharp KA, Nicholls A, Fine RF, Honig B (1991) Reconciling the magnitude of the microscopic and macroscopic hydrophobic effects. Science 252(5002):106–109

    Article  CAS  PubMed  Google Scholar 

  29. Bohm HJ (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 8(3):243–256

    Article  CAS  PubMed  Google Scholar 

  30. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749

    Article  CAS  PubMed  Google Scholar 

  31. Salaniwal S, Manas ES, Alvarez JC, Unwalla RJ (2007) Critical evaluation of methods to incorporate entropy loss upon binding in high-throughput docking. Proteins 66(2):422–435

    Article  CAS  PubMed  Google Scholar 

  32. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD Jr (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We gratefully thank Dr. Shuang Chen for the help with critical editing of the manuscript. The work was supported by the National Natural Science Foundation of China (#31401130 to Y.C).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yang Cao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Cao, Y., Dai, W., Miao, Z. (2018). Evaluation of Protein–Ligand Docking by Cyscore. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7756-7_12

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7755-0

  • Online ISBN: 978-1-4939-7756-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics