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Journal of Computer-Aided Molecular Design

, Volume 26, Issue 2, pp 185–197 | Cite as

Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?

  • Oliver Korb
  • Tim ten Brink
  • Fredrick Robin Devadoss Victor Paul Raj
  • Matthias Keil
  • Thomas E. ExnerEmail author
Article

Abstract

Due to the large number of different docking programs and scoring functions available, researchers are faced with the problem of selecting the most suitable one when starting a structure-based drug discovery project. To guide the decision process, several studies comparing different docking and scoring approaches have been published. In the context of comparing scoring function performance, it is common practice to use a predefined, computer-generated set of ligand poses (decoys) and to reevaluate their score using the set of scoring functions to be compared. But are predefined decoy sets able to unambiguously evaluate and rank different scoring functions with respect to pose prediction performance? This question arose when the pose prediction performance of our piecewise linear potential derived scoring functions (Korb et al. in J Chem Inf Model 49:84–96, 2009) was assessed on a standard decoy set (Cheng et al. in J Chem Inf Model 49:1079–1093, 2009). While they showed excellent pose identification performance when they were used for rescoring of the predefined decoy conformations, a pronounced degradation in performance could be observed when they were directly applied in docking calculations using the same test set. This implies that on a discrete set of ligand poses only the rescoring performance can be evaluated. For comparing the pose prediction performance in a more rigorous manner, the search space of each scoring function has to be sampled extensively as done in the docking calculations performed here. We were able to identify relative strengths and weaknesses of three scoring functions (ChemPLP, GoldScore, and Astex Statistical Potential) by analyzing the performance for subsets of the complexes grouped by different properties of the active site. However, reasons for the overall poor performance of all three functions on this test set compared to other test sets of similar size could not be identified.

Keywords

Docking Ranking Conformational space Sampling Active-site properties 

Notes

Acknowledgment

The authors thank Renxiao Wang for providing the diverse test set of 195 protein–ligand complexes as well as Colin Groom and John Liebeschuetz for helpful discussions. The work was supported by the Konstanz Research School Chemical Biology (KoRS-CB), the Zukunftskolleg and the Young Scholar Fund of the Universität Konstanz. O.K. acknowledges support of the Landesgraduiertenförderung Baden-Württemberg and the Postdoc-Programme of the German Academic Exchange Service (DAAD). Additionally, we thank the Common Ulm Stuttgart Server (CUSS) and the Baden-Württemberg grid (bwGRiD), which is part of the D-Grid system, for providing the computer resources making the computations possible.

Supplementary material

10822_2011_9539_MOESM1_ESM.pdf (1.4 mb)
Success rates for the 16 different scoring functions of the original study and the 4 scoring functions described in this paper can be found in the supporting information. Binding scores and rmsd values for the best-identified decoy as well as the best-ranked poses of the full docking for each individual complex are also given. Finally, plots showing rmsd values versus the total surface area of the ligand and the binding affinities are available. (PDF 1419 kb)

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Oliver Korb
    • 1
    • 2
  • Tim ten Brink
    • 1
  • Fredrick Robin Devadoss Victor Paul Raj
    • 1
  • Matthias Keil
    • 3
  • Thomas E. Exner
    • 1
    Email author
  1. 1.Department of Chemistry and ZukunftskollegUniversity of KonstanzKonstanzGermany
  2. 2.The Cambridge Crystallographic Data CentreCambridgeUK
  3. 3.Chemical Computing Group Inc.MontrealCanada

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