Improving virtual screening of G protein-coupled receptors via ligand-directed modeling.
G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse a...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-11-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5708846?pdf=render |
| _version_ | 1819101023813238784 |
|---|---|
| author | Thomas Coudrat John Simms Arthur Christopoulos Denise Wootten Patrick M Sexton |
| author_facet | Thomas Coudrat John Simms Arthur Christopoulos Denise Wootten Patrick M Sexton |
| author_sort | Thomas Coudrat |
| collection | DOAJ |
| description | G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state. |
| first_indexed | 2024-12-22T01:12:05Z |
| format | Article |
| id | doaj.art-4e7ed5d171f84e11b0c5527b7366249d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-22T01:12:05Z |
| publishDate | 2017-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-4e7ed5d171f84e11b0c5527b7366249d2022-12-21T18:43:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-11-011311e100581910.1371/journal.pcbi.1005819Improving virtual screening of G protein-coupled receptors via ligand-directed modeling.Thomas CoudratJohn SimmsArthur ChristopoulosDenise WoottenPatrick M SextonG protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state.http://europepmc.org/articles/PMC5708846?pdf=render |
| spellingShingle | Thomas Coudrat John Simms Arthur Christopoulos Denise Wootten Patrick M Sexton Improving virtual screening of G protein-coupled receptors via ligand-directed modeling. PLoS Computational Biology |
| title | Improving virtual screening of G protein-coupled receptors via ligand-directed modeling. |
| title_full | Improving virtual screening of G protein-coupled receptors via ligand-directed modeling. |
| title_fullStr | Improving virtual screening of G protein-coupled receptors via ligand-directed modeling. |
| title_full_unstemmed | Improving virtual screening of G protein-coupled receptors via ligand-directed modeling. |
| title_short | Improving virtual screening of G protein-coupled receptors via ligand-directed modeling. |
| title_sort | improving virtual screening of g protein coupled receptors via ligand directed modeling |
| url | http://europepmc.org/articles/PMC5708846?pdf=render |
| work_keys_str_mv | AT thomascoudrat improvingvirtualscreeningofgproteincoupledreceptorsvialiganddirectedmodeling AT johnsimms improvingvirtualscreeningofgproteincoupledreceptorsvialiganddirectedmodeling AT arthurchristopoulos improvingvirtualscreeningofgproteincoupledreceptorsvialiganddirectedmodeling AT denisewootten improvingvirtualscreeningofgproteincoupledreceptorsvialiganddirectedmodeling AT patrickmsexton improvingvirtualscreeningofgproteincoupledreceptorsvialiganddirectedmodeling |