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Hierarchical Graph Representation of Pharmacophore Models
For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793842/ https://www.ncbi.nlm.nih.gov/pubmed/33425991 http://dx.doi.org/10.3389/fmolb.2020.599059 |
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author | Arthur, Garon Oliver, Wieder Klaus, Bareis Thomas, Seidel Gökhan, Ibis Sharon, Bryant Isabelle, Theret Pierre, Ducrot Thierry, Langer |
author_facet | Arthur, Garon Oliver, Wieder Klaus, Bareis Thomas, Seidel Gökhan, Ibis Sharon, Bryant Isabelle, Theret Pierre, Ducrot Thierry, Langer |
author_sort | Arthur, Garon |
collection | PubMed |
description | For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns. |
format | Online Article Text |
id | pubmed-7793842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77938422021-01-09 Hierarchical Graph Representation of Pharmacophore Models Arthur, Garon Oliver, Wieder Klaus, Bareis Thomas, Seidel Gökhan, Ibis Sharon, Bryant Isabelle, Theret Pierre, Ducrot Thierry, Langer Front Mol Biosci Molecular Biosciences For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns. Frontiers Media S.A. 2020-12-14 /pmc/articles/PMC7793842/ /pubmed/33425991 http://dx.doi.org/10.3389/fmolb.2020.599059 Text en Copyright © 2020 Arthur, Oliver, Klaus, Thomas, Gökhan, Sharon, Isabelle, Pierre and Thierry. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Arthur, Garon Oliver, Wieder Klaus, Bareis Thomas, Seidel Gökhan, Ibis Sharon, Bryant Isabelle, Theret Pierre, Ducrot Thierry, Langer Hierarchical Graph Representation of Pharmacophore Models |
title | Hierarchical Graph Representation of Pharmacophore Models |
title_full | Hierarchical Graph Representation of Pharmacophore Models |
title_fullStr | Hierarchical Graph Representation of Pharmacophore Models |
title_full_unstemmed | Hierarchical Graph Representation of Pharmacophore Models |
title_short | Hierarchical Graph Representation of Pharmacophore Models |
title_sort | hierarchical graph representation of pharmacophore models |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793842/ https://www.ncbi.nlm.nih.gov/pubmed/33425991 http://dx.doi.org/10.3389/fmolb.2020.599059 |
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