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...
Autores principales: | , , , , , |
---|---|
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 |