PocketOptimizer and the Design of Ligand Binding Sites

  • Andre C. Stiel
  • Mehdi Nellen
  • Birte HöckerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1414)


PocketOptimizer is a computational method to design protein binding pockets that has been recently developed. Starting from a protein structure an existing small molecule binding pocket is optimized for the recognition of a new ligand. The modular program predicts mutations that will improve the affinity of a target small molecule to the protein of interest using a receptor–ligand scoring function to estimate the binding free energy. PocketOptimizer has been tested in a comprehensive benchmark and predicted mutations have also been used in experimental tests. In this chapter, we will provide general recommendations for usage as well as an in-depth description of all individual PocketOptimizer modules.

Key words

Computational protein design Protein–small molecule interaction Ligand binding design Enzyme engineering PocketOptimizer 



Financial support from the German Research Foundation (DFG grant HO 4022/2-3) is acknowledged. M.N. was supported by the Erasmus+ mobility program. The authors like to thank Steffen Schmidt for comments on the manuscript.


  1. 1.
    Kohlbacher O (2012) CADDSuite – a workflow-enabled suite of open-source tools for drug discovery. J Cheminform 4:O2. doi: 10.1186/1758-2946-4-S1-O2 PubMedCentralGoogle Scholar
  2. 2.
    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 31:455–61. doi: 10.1002/jcc.21334 PubMedPubMedCentralGoogle Scholar
  3. 3.
    Malisi C, Schumann M, Toussaint NC et al (2012) Binding pocket optimization by computational protein design. PLoS One 7, e52505. doi: 10.1371/journal.pone.0052505 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Smith CA, Kortemme T (2008) Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. J Mol Biol 380:742–56. doi: 10.1016/j.jmb.2008.05.023 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Georgiev I, Keedy D, Richardson JS et al (2008) Algorithm for backrub motions in protein design. Bioinformatics 24:i196–204. doi: 10.1093/bioinformatics/btn169 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Richter F, Leaver-Fay A, Khare SD et al (2011) De novo enzyme design using Rosetta3. PLoS One 6, e19230. doi: 10.1371/journal.pone.0019230 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Leite TB, Gomes D, Miteva MA et al (2007) Frog: a FRee Online druG 3D conformation generator. Nucleic Acids Res 35:W568–72. doi: 10.1093/nar/gkm289 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    O’Boyle NM, Vandermeersch T, Flynn CJ et al (2011) Confab - Systematic generation of diverse low-energy conformers. J Cheminform 3:8. doi: 10.1186/1758-2946-3-8 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    wwPDB (2008) Chemical Component Dictionary. Accessed 17 Feb 2016
  10. 10.
    Höcker Lab (2015) Algorithms and software. Accessed 17 Feb 2016
  11. 11.
    AMBER (2015) The amber molecular dynamics package. Accessed 17 Feb 2016
  12. 12.
    Jay Ponder Lab (2015) TINKER molecular modeling package. Accessed 17 Feb 2016
  13. 13.
    Sontag D, Choe DK, Li Y (2012) Efficiently searching for frustrated cycles in MAP inference. arXiv preprint arXiv:1210.4902Google Scholar
  14. 14.
    DOCKER (2015) Docker software. Accessed 17 Feb 2016

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Max Planck Institute for Developmental BiologyTübingenGermany
  2. 2.Lehrstuhl für BiochemieUniversität BayreuthBayreuthGermany

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