Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Manoliu, Laura C. E., Martin, Eliza C., Milac, Adina L., Spiridon, Laurentiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783745158143016960
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
work_keys_str_mv AT manoliulaurace effectiveuseofempiricaldataforvirtualscreeningagainstapjrgpcrreceptor
AT martinelizac effectiveuseofempiricaldataforvirtualscreeningagainstapjrgpcrreceptor
AT milacadinal effectiveuseofempiricaldataforvirtualscreeningagainstapjrgpcrreceptor
AT spiridonlaurentiu effectiveuseofempiricaldataforvirtualscreeningagainstapjrgpcrreceptor