Cargando…
A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset
The recent publication of the σ1R crystal structure is an important cornerstone for the derivation of more accurate activity prediction models. We report here a comparative study involving a set of more than 25,000 structures from our internal database that had been screened for σ1R affinity. Using...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555132/ https://www.ncbi.nlm.nih.gov/pubmed/31214020 http://dx.doi.org/10.3389/fphar.2019.00519 |
_version_ | 1783425100054265856 |
---|---|
author | Pascual, Rosalia Almansa, Carmen Plata-Salamán, Carlos Vela, José Miguel |
author_facet | Pascual, Rosalia Almansa, Carmen Plata-Salamán, Carlos Vela, José Miguel |
author_sort | Pascual, Rosalia |
collection | PubMed |
description | The recent publication of the σ1R crystal structure is an important cornerstone for the derivation of more accurate activity prediction models. We report here a comparative study involving a set of more than 25,000 structures from our internal database that had been screened for σ1R affinity. Using the recently published crystal structure, 5HK1, two new pharmacophore models were generated. The first one, 5HK1–Ph.A, was obtained by an algorithm that identifies the most important receptor-ligand interactions including volume restrictions enforced by the atomic structure of the recognition site. The second, 5HK1–Ph.B, resulted from a manual edition of the first one by the fusion of two hydrophobic (HYD) features. Finally, we also docked the database using a high throughput docking technique and scored the resulting poses with seven different scoring functions. Statistical performance measures were obtained for the two models, comparing them with previously published σ1R pharmacophores (Hit Rate, sensitivity, specificity, and Receiver Operator Characteristic) and 5HK1–Ph.B emerged as the best one in discriminating between active and inactive compounds, with a ROC-AUC value above 0.8 and enrichment values above 3 at different fractions of screened samples. 5HK1–Ph.B also showed better results than the direct docking, which may be due to the rigidity of the crystal structure in the docking process (i.e., feature tolerances in the pharmacophore model). Additionally, the impact of the HYD interactions and the penalty for desolvating ligands with polar atoms may be not adequately captured by scoring functions, whereas HYD groups filling up such regions of the binding site are entailed in the pharmacophore model. Altogether, using annotated data from a large and diverse compound collection together with crystal structure information provides a sound basis for the generation and validation of predictive models to design new molecules. |
format | Online Article Text |
id | pubmed-6555132 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65551322019-06-18 A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset Pascual, Rosalia Almansa, Carmen Plata-Salamán, Carlos Vela, José Miguel Front Pharmacol Pharmacology The recent publication of the σ1R crystal structure is an important cornerstone for the derivation of more accurate activity prediction models. We report here a comparative study involving a set of more than 25,000 structures from our internal database that had been screened for σ1R affinity. Using the recently published crystal structure, 5HK1, two new pharmacophore models were generated. The first one, 5HK1–Ph.A, was obtained by an algorithm that identifies the most important receptor-ligand interactions including volume restrictions enforced by the atomic structure of the recognition site. The second, 5HK1–Ph.B, resulted from a manual edition of the first one by the fusion of two hydrophobic (HYD) features. Finally, we also docked the database using a high throughput docking technique and scored the resulting poses with seven different scoring functions. Statistical performance measures were obtained for the two models, comparing them with previously published σ1R pharmacophores (Hit Rate, sensitivity, specificity, and Receiver Operator Characteristic) and 5HK1–Ph.B emerged as the best one in discriminating between active and inactive compounds, with a ROC-AUC value above 0.8 and enrichment values above 3 at different fractions of screened samples. 5HK1–Ph.B also showed better results than the direct docking, which may be due to the rigidity of the crystal structure in the docking process (i.e., feature tolerances in the pharmacophore model). Additionally, the impact of the HYD interactions and the penalty for desolvating ligands with polar atoms may be not adequately captured by scoring functions, whereas HYD groups filling up such regions of the binding site are entailed in the pharmacophore model. Altogether, using annotated data from a large and diverse compound collection together with crystal structure information provides a sound basis for the generation and validation of predictive models to design new molecules. Frontiers Media S.A. 2019-05-31 /pmc/articles/PMC6555132/ /pubmed/31214020 http://dx.doi.org/10.3389/fphar.2019.00519 Text en Copyright © 2019 Pascual, Almansa, Plata-Salamán and Vela. 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 | Pharmacology Pascual, Rosalia Almansa, Carmen Plata-Salamán, Carlos Vela, José Miguel A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset |
title | A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset |
title_full | A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset |
title_fullStr | A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset |
title_full_unstemmed | A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset |
title_short | A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset |
title_sort | new pharmacophore model for the design of sigma-1 ligands validated on a large experimental dataset |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555132/ https://www.ncbi.nlm.nih.gov/pubmed/31214020 http://dx.doi.org/10.3389/fphar.2019.00519 |
work_keys_str_mv | AT pascualrosalia anewpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT almansacarmen anewpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT platasalamancarlos anewpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT velajosemiguel anewpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT pascualrosalia newpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT almansacarmen newpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT platasalamancarlos newpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset AT velajosemiguel newpharmacophoremodelforthedesignofsigma1ligandsvalidatedonalargeexperimentaldataset |