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Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor
Alzheimer’s disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In si...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399775/ https://www.ncbi.nlm.nih.gov/pubmed/34443478 http://dx.doi.org/10.3390/molecules26164894 |
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author | Manoliu, Laura C. E. Martin, Eliza C. Milac, Adina L. Spiridon, Laurentiu |
author_facet | Manoliu, Laura C. E. Martin, Eliza C. Milac, Adina L. Spiridon, Laurentiu |
author_sort | Manoliu, Laura C. E. |
collection | PubMed |
description | Alzheimer’s disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand—binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs. |
format | Online Article Text |
id | pubmed-8399775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83997752021-08-29 Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor Manoliu, Laura C. E. Martin, Eliza C. Milac, Adina L. Spiridon, Laurentiu Molecules Article Alzheimer’s disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand—binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs. MDPI 2021-08-12 /pmc/articles/PMC8399775/ /pubmed/34443478 http://dx.doi.org/10.3390/molecules26164894 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Manoliu, Laura C. E. Martin, Eliza C. Milac, Adina L. Spiridon, Laurentiu Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor |
title | Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor |
title_full | Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor |
title_fullStr | Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor |
title_full_unstemmed | Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor |
title_short | Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor |
title_sort | effective use of empirical data for virtual screening against apjr gpcr receptor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399775/ https://www.ncbi.nlm.nih.gov/pubmed/34443478 http://dx.doi.org/10.3390/molecules26164894 |
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