Cargando…

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling

PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called “Iterative Stochastic Elimination” (ISE) was applied to const...

Descripción completa

Detalles Bibliográficos
Autores principales: Da’adoosh, Benny, Marcus, David, Rayan, Anwar, King, Fred, Che, Jianwei, Goldblum, Amiram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355875/
https://www.ncbi.nlm.nih.gov/pubmed/30705343
http://dx.doi.org/10.1038/s41598-019-38508-8
_version_ 1783391407984082944
author Da’adoosh, Benny
Marcus, David
Rayan, Anwar
King, Fred
Che, Jianwei
Goldblum, Amiram
author_facet Da’adoosh, Benny
Marcus, David
Rayan, Anwar
King, Fred
Che, Jianwei
Goldblum, Amiram
author_sort Da’adoosh, Benny
collection PubMed
description PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called “Iterative Stochastic Elimination” (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC(50) between 4–19 nM and 14 others with EC(50) below 10 µM. Most of the nanomolar agonists were found to be highly selective for PPAR-δ and are structurally different than agonists used for model building.
format Online
Article
Text
id pubmed-6355875
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-63558752019-02-04 Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling Da’adoosh, Benny Marcus, David Rayan, Anwar King, Fred Che, Jianwei Goldblum, Amiram Sci Rep Article PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called “Iterative Stochastic Elimination” (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC(50) between 4–19 nM and 14 others with EC(50) below 10 µM. Most of the nanomolar agonists were found to be highly selective for PPAR-δ and are structurally different than agonists used for model building. Nature Publishing Group UK 2019-01-31 /pmc/articles/PMC6355875/ /pubmed/30705343 http://dx.doi.org/10.1038/s41598-019-38508-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Da’adoosh, Benny
Marcus, David
Rayan, Anwar
King, Fred
Che, Jianwei
Goldblum, Amiram
Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
title Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
title_full Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
title_fullStr Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
title_full_unstemmed Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
title_short Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
title_sort discovering highly selective and diverse ppar-delta agonists by ligand based machine learning and structural modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355875/
https://www.ncbi.nlm.nih.gov/pubmed/30705343
http://dx.doi.org/10.1038/s41598-019-38508-8
work_keys_str_mv AT daadooshbenny discoveringhighlyselectiveanddiverseppardeltaagonistsbyligandbasedmachinelearningandstructuralmodeling
AT marcusdavid discoveringhighlyselectiveanddiverseppardeltaagonistsbyligandbasedmachinelearningandstructuralmodeling
AT rayananwar discoveringhighlyselectiveanddiverseppardeltaagonistsbyligandbasedmachinelearningandstructuralmodeling
AT kingfred discoveringhighlyselectiveanddiverseppardeltaagonistsbyligandbasedmachinelearningandstructuralmodeling
AT chejianwei discoveringhighlyselectiveanddiverseppardeltaagonistsbyligandbasedmachinelearningandstructuralmodeling
AT goldblumamiram discoveringhighlyselectiveanddiverseppardeltaagonistsbyligandbasedmachinelearningandstructuralmodeling