Literature DB >> 18041759

Modeling of metal interaction geometries for protein-ligand docking.

Birte Seebeck1, Ingo Reulecke, Andreas Kämper, Matthias Rarey.   

Abstract

The accurate modeling of metal coordination geometries plays an important role for structure-based drug design applied to metalloenzymes. For the development of a new metal interaction model, we perform a statistical analysis of metal interaction geometries that are relevant to protein-ligand complexes. A total of 43,061 metal sites of the Protein Data Bank (PDB), containing amongst others magnesium, calcium, zinc, iron, manganese, copper, cadmium, cobalt, and nickel, were evaluated according to their metal coordination geometry. Based on statistical analysis, we derived a model for the automatic calculation and definition of metal interaction geometries for the purpose of molecular docking analyses. It includes the identification of the metal-coordinating ligands, the calculation of the coordination geometry and the superposition of ideal polyhedra to identify the optimal positions for free coordination sites. The new interaction model was integrated in the docking software FlexX and evaluated on a data set of 103 metalloprotein-ligand complexes, which were extracted from the PDB. In a first step, the quality of the automatic calculation of the metal coordination geometry was analyzed. In 74% of the cases, the correct prediction of the coordination geometry could be determined on the basis of the protein structure alone. Secondly, the new metal interaction model was tested in terms of predicting protein-ligand complexes. In the majority of test cases, the new interaction model resulted in an improved docking accuracy of the top ranking placements. 2007 Wiley-Liss, Inc.

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Year:  2008        PMID: 18041759     DOI: 10.1002/prot.21818

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  24 in total

1.  Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function.

Authors:  Nadine Schneider; Sally Hindle; Gudrun Lange; Robert Klein; Jürgen Albrecht; Hans Briem; Kristin Beyer; Holger Claußen; Marcus Gastreich; Christian Lemmen; Matthias Rarey
Journal:  J Comput Aided Mol Des       Date:  2011-12-27       Impact factor: 3.686

2.  The linker region in receptor guanylyl cyclases is a key regulatory module: mutational analysis of guanylyl cyclase C.

Authors:  Sayanti Saha; Kabir Hassan Biswas; Chandana Kondapalli; Nishitha Isloor; Sandhya S Visweswariah
Journal:  J Biol Chem       Date:  2009-07-31       Impact factor: 5.157

3.  Comparative evaluation of several docking tools for docking small molecule ligands to DC-SIGN.

Authors:  Gregor Jug; Marko Anderluh; Tihomir Tomašič
Journal:  J Mol Model       Date:  2015-06-04       Impact factor: 1.810

4.  A consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes: methods behind the HYDE scoring function.

Authors:  Nadine Schneider; Gudrun Lange; Sally Hindle; Robert Klein; Matthias Rarey
Journal:  J Comput Aided Mol Des       Date:  2012-12-27       Impact factor: 3.686

5.  Drawing the PDB: Protein-Ligand Complexes in Two Dimensions.

Authors:  Katrin Stierand; Matthias Rarey
Journal:  ACS Med Chem Lett       Date:  2010-08-31       Impact factor: 4.345

6.  Prediction of structures of zinc-binding proteins through explicit modeling of metal coordination geometry.

Authors:  Chu Wang; Robert Vernon; Oliver Lange; Michael Tyka; David Baker
Journal:  Protein Sci       Date:  2010-03       Impact factor: 6.725

7.  Automated site preparation in physics-based rescoring of receptor ligand complexes.

Authors:  Chaya S Rapp; Cheryl Schonbrun; Matthew P Jacobson; Chakrapani Kalyanaraman; Niu Huang
Journal:  Proteins       Date:  2009-10

8.  Crystal structure of the guanylyl cyclase Cya2.

Authors:  Annika Rauch; Martina Leipelt; Michael Russwurm; Clemens Steegborn
Journal:  Proc Natl Acad Sci U S A       Date:  2008-10-07       Impact factor: 11.205

9.  Divalent cations slow activation of EAG family K+ channels through direct binding to S4.

Authors:  Xiaofei Zhang; Badry Bursulaya; Christian C Lee; Bihan Chen; Kendra Pivaroff; Timothy Jegla
Journal:  Biophys J       Date:  2009-07-08       Impact factor: 4.033

10.  Variability in docking success rates due to dataset preparation.

Authors:  Christopher R Corbeil; Christopher I Williams; Paul Labute
Journal:  J Comput Aided Mol Des       Date:  2012-05-08       Impact factor: 3.686

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