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Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors
The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual scre...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3699586/ https://www.ncbi.nlm.nih.gov/pubmed/23844000 http://dx.doi.org/10.1371/journal.pone.0067302 |
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author | Nguyen, Elizabeth Dong Norn, Christoffer Frimurer, Thomas M. Meiler, Jens |
author_facet | Nguyen, Elizabeth Dong Norn, Christoffer Frimurer, Thomas M. Meiler, Jens |
author_sort | Nguyen, Elizabeth Dong |
collection | PubMed |
description | The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10(3) fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes. |
format | Online Article Text |
id | pubmed-3699586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36995862013-07-10 Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors Nguyen, Elizabeth Dong Norn, Christoffer Frimurer, Thomas M. Meiler, Jens PLoS One Research Article The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10(3) fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes. Public Library of Science 2013-07-02 /pmc/articles/PMC3699586/ /pubmed/23844000 http://dx.doi.org/10.1371/journal.pone.0067302 Text en © 2013 Nguyen et al http://creativecommons.org/licenses/by/4.0/ 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 author and source are properly credited. |
spellingShingle | Research Article Nguyen, Elizabeth Dong Norn, Christoffer Frimurer, Thomas M. Meiler, Jens Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors |
title | Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors |
title_full | Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors |
title_fullStr | Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors |
title_full_unstemmed | Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors |
title_short | Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors |
title_sort | assessment and challenges of ligand docking into comparative models of g-protein coupled receptors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3699586/ https://www.ncbi.nlm.nih.gov/pubmed/23844000 http://dx.doi.org/10.1371/journal.pone.0067302 |
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