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PhyteByte: identification of foods containing compounds with specific pharmacological properties
BACKGROUND: Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for com...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288679/ https://www.ncbi.nlm.nih.gov/pubmed/32522154 http://dx.doi.org/10.1186/s12859-020-03582-7 |
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author | Westerman, Kenneth E. Harrington, Sean Ordovas, Jose M. Parnell, Laurence D. |
author_facet | Westerman, Kenneth E. Harrington, Sean Ordovas, Jose M. Parnell, Laurence D. |
author_sort | Westerman, Kenneth E. |
collection | PubMed |
description | BACKGROUND: Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds. RESULTS: PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator. CONCLUSIONS: PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning. |
format | Online Article Text |
id | pubmed-7288679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72886792020-06-12 PhyteByte: identification of foods containing compounds with specific pharmacological properties Westerman, Kenneth E. Harrington, Sean Ordovas, Jose M. Parnell, Laurence D. BMC Bioinformatics Software BACKGROUND: Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds. RESULTS: PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator. CONCLUSIONS: PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning. BioMed Central 2020-06-10 /pmc/articles/PMC7288679/ /pubmed/32522154 http://dx.doi.org/10.1186/s12859-020-03582-7 Text en © The Author(s) 2020 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Westerman, Kenneth E. Harrington, Sean Ordovas, Jose M. Parnell, Laurence D. PhyteByte: identification of foods containing compounds with specific pharmacological properties |
title | PhyteByte: identification of foods containing compounds with specific pharmacological properties |
title_full | PhyteByte: identification of foods containing compounds with specific pharmacological properties |
title_fullStr | PhyteByte: identification of foods containing compounds with specific pharmacological properties |
title_full_unstemmed | PhyteByte: identification of foods containing compounds with specific pharmacological properties |
title_short | PhyteByte: identification of foods containing compounds with specific pharmacological properties |
title_sort | phytebyte: identification of foods containing compounds with specific pharmacological properties |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288679/ https://www.ncbi.nlm.nih.gov/pubmed/32522154 http://dx.doi.org/10.1186/s12859-020-03582-7 |
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