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  • Open Access

Guiding protein-ligand docking with different experimental NMR-data

  • 1Email author,
  • 1,
  • 2,
  • 1,
  • 1,
  • 2,
  • 3 and
  • 1, 4
Journal of Cheminformatics20124 (Suppl 1) :P39

  • Published:


  • Peptide
  • Local Optimization
  • Experimental Information
  • Peptide Ligand
  • Performance Reason

Today's scoring functions are one of the main reasons that state-of-the-art protein-ligand dockings fail in about 20 % to 40 % of the targets due to the sometimes severe approximations they make. However these approximations are necessary for performance reasons. One possibility to overcome these problems is the inclusion of additional, preferably experimental information in the docking process. Especially ligand-based NMR experiments that are far less demanding than the solution of the whole complex structure are helpful.

Here we present the inclusion of three different types of NMR-data into the ChemPLP [1] scoring function of our docking tool PLANTS [2]. First, STD and intra-ligand trNOE spectra were used to obtain distant constraints between ligand and protein atoms. This approach proved beneficial for the docking of larger peptide ligands i. e. the epitope of MUC-1 glycoprotein to the SM3 antibody [3].

In the second part the usefulness of INPHARMA data [4, 5] is shown by combinig a score, evaluating the agreement between simulated and measured INPHARMA spectra, with the PLANTS ChemPLP scoring function. First results from rescoring after local optimization of the poses and full docking experiments are shown.

Authors’ Affiliations

Department of Chemistry, University Konstanz, 78457 Konstanz, Germany
Department of NMR-Based Structural Biology, MPIbpc, 37077 Göttingen, Germany
Structural and Computational Biology Unit, EMBL, 69117 Heidelberg, Germany
Zukunftskolleg, University Konstanz, 78457 Konstanz, Germany


  1. Korb O, Stützle T, Exner TE: . J Chem Inf Model. 2009, 49: 84-96. 10.1021/ci800298z.View ArticleGoogle Scholar
  2. Korb O, Stützle T, Exner TE: . Swarm Intell. 2007, 1: 115-134. 10.1007/s11721-007-0006-9.View ArticleGoogle Scholar
  3. Korb O, Möller HM, Exner TE: . ChemMedChem. 2010, 5: 1001-1006. 10.1002/cmdc.201000090.View ArticleGoogle Scholar
  4. Orts J, Tuma J, Reese M, Grimm SK, Monecke P, Bartoschek S, Schier A, Wendt KU, Griesinger C, Carlomagno T: . Angew Chem Int Ed. 2008, 47: 7736-7740. 10.1002/anie.200801792.View ArticleGoogle Scholar
  5. Sanchez-Pedregal VM, Reese M, Meiler J, Blommers MJJ, Griesinger C, Carlomagno T: . Angew Chem Int Ed. 2005, 44: 4172-4175. 10.1002/anie.200500503.View ArticleGoogle Scholar


© ten Brink et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.