Journal of Computer-Aided Molecular Design

, Volume 31, Issue 7, pp 653–666 | Cite as

GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking

  • Minkyung Baek
  • Woong-Hee Shin
  • Hwan Won Chung
  • Chaok Seok
Article
  • 433 Downloads

Abstract

Protein–ligand docking is a useful tool for providing atomic-level understanding of protein functions in nature and design principles for artificial ligands or proteins with desired properties. The ability to identify the true binding pose of a ligand to a target protein among numerous possible candidate poses is an essential requirement for successful protein–ligand docking. Many previously developed docking scoring functions were trained to reproduce experimental binding affinities and were also used for scoring binding poses. However, in this study, we developed a new docking scoring function, called GalaxyDock BP2 Score, by directly training the scoring power of binding poses. This function is a hybrid of physics-based, empirical, and knowledge-based score terms that are balanced to strengthen the advantages of each component. The performance of the new scoring function exhibits significant improvement over existing scoring functions in decoy pose discrimination tests. In addition, when the score is used with the GalaxyDock2 protein–ligand docking program, it outperformed other state-of-the-art docking programs in docking tests on the Astex diverse set, the Cross2009 benchmark set, and the Astex non-native set. GalaxyDock BP2 Score and GalaxyDock2 with this score are freely available at http://galaxy.seoklab.org/softwares/galaxydock.html.

Keywords

Molecular docking Docking scoring function Decoy discrimination Energy parameter optimization Hybrid scoring function 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Ministry of Science, ICT & Future Planning (Nos. 2016R1A2A1A05005485 and 2016M3C4A7952630) and by the Korea Institute of Science and Technology Information supercomputing center (KSC-2015-C2-057). We thank Andrew Beaven for his careful reading of the manuscript.

Supplementary material

10822_2017_30_MOESM1_ESM.docx (66 kb)
Supplementary material 1 (DOCX 66 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of ChemistrySeoul National UniversitySeoulRepublic of Korea
  2. 2.Interdisciplinary Program in BioinformaticsSeoul National UniversitySeoulRepublic of Korea
  3. 3.Computational Science Research CenterKorea Institute of Science and TechnologySeoulRepublic of Korea
  4. 4.Department of Biological SciencesPurdue UniversityWest LafayetteUSA

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