Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials

Hoppe, Christian; Steinbeck, Christoph; Wohlfahrt, Gerd

doi:10.1016/j.jmgm.2005.09.013

Cited by 21 publications

(24 citation statements)

References 50 publications

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“…23 This becomes evident for flexible surface areas like the coactivator site of NRs, but has less influence on the comparison of the buried ligand-binding sites. Using 890 Wohlfahrt, Sipilä, and Pietilä conserved fields from several structures of the same receptor can suppress noise originating from inaccuracies in superposition or from different conformations of flexible side chains, but sometimes important interactions in conformationally flexible regions are not detected after averaging.…”

Section: Discussionmentioning

confidence: 99%

“…In an earlier example we analyzed field-based differences of PPARs, 23 which are mostly explained by steric hindrance. In the present work examples are shown where differences are less obvious from direct structure comparison and mainly result from opposite preferences for polar or lipophilic groups.…”

Section: Discussionmentioning

confidence: 99%

“…22,23 The parametrization and field calculations implemented in MOE were used as such. In this implementation the probability density is given as a product of three one-dimensional probability densities…”

Section: Knowledge-based Fieldsmentioning

confidence: 99%

“…In addition to previously developed tools for similarity and difference field calculations 23 also average fields were added. A new combination tool was used to analyze fields where a certain portion of receptors in a group shares grid points with field values over a given threshold.…”

Section: Clustering and Postprocessing Of Fieldsmentioning

confidence: 99%

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Field‐based comparison of ligand and coactivator binding sites of nuclear receptors

2009

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A structure-based comparison of the ligand-binding domains of 35 nuclear receptors from five different subfamilies is presented. Their ligand and coactivator binding sites are characterized using knowledge-based contact preference fields for hydrophobic and hydrophilic interactions implemented in the MOE modeling environment. Additionally, for polar knowledge-based field points the preference for negative or positive electrostatic interactions is estimated using the Poisson-Boltzmann equation. These molecular-interaction fields are used to cluster the nuclear receptor family based on similarities of their binding sites. By analyzing the similarities and differences of hydrophobic and polar fields in binding pockets of related receptors it is possible to identify conserved interactions in ligand and coactivator binding pockets, which support e.g. design of specific ligands during lead optimization or virtual screening as docking filter. Examples of remarkable similarities between ligand binding sites of members from phylogenetically different nuclear receptor families (RXR, RAR, HNF4, NR5) and differences between closely related subtypes (LXR, RAR, TR) are discussed in more detail. Significant similarities and differences of coactivator binding sites are shown for NR3Cs, LXRs and PPARs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Knowledge-based Fieldsmentioning

confidence: 99%

Section: Clustering and Postprocessing Of Fieldsmentioning

confidence: 99%

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Field‐based comparison of ligand and coactivator binding sites of nuclear receptors

2009

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“…Since then, there have been a number of Chemogenomics efforts that have primarily focused on kinases [Vieth et al, 2004;Hu et al, 2005;Birault et al, 2006;Kellenberger et al, 2006;Hoppe et al, 2006], and GPCRs [Jacoby et al, 1999;Jacoby, 2001;Frimurer et al, 2005;Surgand et al, 2006]. Some of these approaches identify the right subset of family members using similarity search, either with respect to sequence [Frimurer et al, 2005;Surgand et al, 2006] or structure [Hu et al, 2005;Kellenberger et al, 2006;Hoppe et al, 2006], whereas other approaches employ machine-learning techniques to estimate and analyze the ligand-target affinity within each family Gough, 2002, 2005;Vieth et al, 2004;Jacob and Vert, 2008]. Even though chemogenomics-based approaches have been successfully used to identify lead compounds Eguchi et al, 2003;Klabunde and Jger, 2006;Martin et al, 2007], the methods that were developed are to a large extent specific to kinases and GPCRs and have a significant manual component.…”

Section: Chemogenomicsmentioning

confidence: 99%

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

Ning

Karypis

2010

Drug Development Research

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In this article, we review the recent development for in silico Structure-Activity-Relationship (SAR) models using machine-learning techniques. The review focuses on the following topics: machine-learning algorithms for computational SAR models, single-target-oriented SAR methodologies, Chemogenomics, and future trends. We try to provide the state-of-the-art SAR methods as well as the most up-to-date advancement, in order for the researchers to have a general overview at this area. Drug Dev Res 72:138-146, 2011.

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Method for comparing the structures of protein ligand‐binding sites and application for predicting protein–drug interactions

et al. 2008

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Many drugs, even ones that are designed to act selectively on a target protein, bind unintended proteins. These unintended bindings can explain side effects or indicate additional mechanisms for a drug's medicinal properties. Structural similarity between binding sites is one of the reasons for binding to multiple targets. We developed a method for the structural alignment of atoms in the solvent-accessible surface of proteins that uses similarities in the local atomic environment, and carried out all-against-all structural comparisons for 48,347 potential ligand-binding regions from a nonredundant protein structure subset (nrPDB, provided by NCBI). The relationships between the similarity of ligand-binding regions and the similarity of the global structures of the proteins containing the binding regions were examined. We found 10,403 known ligand-binding region pairs whose structures were similar despite having different global folds. Of these, we detected 281 region pairs that had similar ligands with similar binding modes. These proteins are good examples of convergent evolution. In addition, we found a significant correlation between Z-score of structural similarity and true positive rate of "active" entries in the PubChem BioAssay database. Moreover, we confirmed the interaction between ibuprofen and a new target, porcine pancreatic elastase, by NMR experiment. Finally, we used this method to predict new drug-target protein interactions. We obtained 540 predictions for 105 drugs (e.g., captopril, lovastatin, flurbiprofen, metyrapone, and salicylic acid), and calculated the binding affinities using AutoDock simulation. The results of these structural comparisons are available at http://www.tsurumi.yokohama-cu.ac.jp/fold/database.html.

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Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials

Cited by 21 publications

References 50 publications

Field‐based comparison of ligand and coactivator binding sites of nuclear receptors

Field‐based comparison of ligand and coactivator binding sites of nuclear receptors

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

Method for comparing the structures of protein ligand‐binding sites and application for predicting protein–drug interactions

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