Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/72592 |
| Acceso en línea: | http://hdl.handle.net/10810/72592 |
| Access Level: | acceso abierto |
| Palabra clave: | Human brain GPCRs [35S]GTPγS binding assays signaling pathways 5-HT2A receptors ChEMBL perturbation theory machine learning |
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Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding AssaysDíez Alarcia, RebecaYáñez Pérez, VíctorMuneta Arrate, ItziarArrasate Gil, SoniaLete Expósito, María EstherMeana Martínez, José JavierGonzález Díaz, HumbertoHuman brainGPCRs[35S]GTPγS binding assayssignaling pathways5-HT2A receptorsChEMBLperturbation theorymachine learningG-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5- HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.This work was funded by the Basque Government (ELKARTEK Programme, KK-2017/00023). The authors also acknowledge research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P and SAF-2017-88126R) and Basque Government (IT1045-16 and IT1211-19).ACS202520252019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/72592reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MINECO/CTQ2016-74881-P/info:eu-repo/grantAgreement/MINECO/SAF-2017-88126R/https://doi.org/10.1021/acschemneuro.9b00302info:eu-repo/semantics/openAccess© 2019 American Chemical Societyoai:addi.ehu.eus:10810/725922026-06-18T09:23:17Z |
| dc.title.none.fl_str_mv |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| title |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| spellingShingle |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays Díez Alarcia, Rebeca Human brain GPCRs [35S]GTPγS binding assays signaling pathways 5-HT2A receptors ChEMBL perturbation theory machine learning |
| title_short |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| title_full |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| title_fullStr |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| title_full_unstemmed |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| title_sort |
Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays |
| dc.creator.none.fl_str_mv |
Díez Alarcia, Rebeca Yáñez Pérez, Víctor Muneta Arrate, Itziar Arrasate Gil, Sonia Lete Expósito, María Esther Meana Martínez, José Javier González Díaz, Humberto |
| author |
Díez Alarcia, Rebeca |
| author_facet |
Díez Alarcia, Rebeca Yáñez Pérez, Víctor Muneta Arrate, Itziar Arrasate Gil, Sonia Lete Expósito, María Esther Meana Martínez, José Javier González Díaz, Humberto |
| author_role |
author |
| author2 |
Yáñez Pérez, Víctor Muneta Arrate, Itziar Arrasate Gil, Sonia Lete Expósito, María Esther Meana Martínez, José Javier González Díaz, Humberto |
| author2_role |
author author author author author author |
| dc.subject.none.fl_str_mv |
Human brain GPCRs [35S]GTPγS binding assays signaling pathways 5-HT2A receptors ChEMBL perturbation theory machine learning |
| topic |
Human brain GPCRs [35S]GTPγS binding assays signaling pathways 5-HT2A receptors ChEMBL perturbation theory machine learning |
| description |
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5- HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds. |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2019 2025 2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10810/72592 |
| url |
http://hdl.handle.net/10810/72592 |
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Inglés |
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Inglés |
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info:eu-repo/grantAgreement/MINECO/CTQ2016-74881-P/ info:eu-repo/grantAgreement/MINECO/SAF-2017-88126R/ https://doi.org/10.1021/acschemneuro.9b00302 |
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info:eu-repo/semantics/openAccess © 2019 American Chemical Society |
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openAccess |
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© 2019 American Chemical Society |
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application/pdf |
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ACS |
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ACS |
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