Modeling Structure–Activity Relationship of AMPK Activation

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and a...

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Autores principales:	Jürgen Drewe, Ernst Küsters, Felix Hammann, Matthias Kreuter, Philipp Boss, Verena Schöning
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	AMPK activator machine learning random forest support vector machine logistic regression deep learning Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/d5c06952b0134553af2176989661a101
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spelling	oai:doaj.org-article:d5c06952b0134553af2176989661a1012021-11-11T18:30:19ZModeling Structure–Activity Relationship of AMPK Activation10.3390/molecules262165081420-3049https://doaj.org/article/d5c06952b0134553af2176989661a1012021-10-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/21/6508https://doaj.org/toc/1420-3049The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.Jürgen DreweErnst KüstersFelix HammannMatthias KreuterPhilipp BossVerena SchöningMDPI AGarticleAMPK activatormachine learningrandom forestsupport vector machinelogistic regressiondeep learningOrganic chemistryQD241-441ENMolecules, Vol 26, Iss 6508, p 6508 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AMPK activator machine learning random forest support vector machine logistic regression deep learning Organic chemistry QD241-441
spellingShingle	AMPK activator machine learning random forest support vector machine logistic regression deep learning Organic chemistry QD241-441 Jürgen Drewe Ernst Küsters Felix Hammann Matthias Kreuter Philipp Boss Verena Schöning Modeling Structure–Activity Relationship of AMPK Activation
description	The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.
format	article
author	Jürgen Drewe Ernst Küsters Felix Hammann Matthias Kreuter Philipp Boss Verena Schöning
author_facet	Jürgen Drewe Ernst Küsters Felix Hammann Matthias Kreuter Philipp Boss Verena Schöning
author_sort	Jürgen Drewe
title	Modeling Structure–Activity Relationship of AMPK Activation
title_short	Modeling Structure–Activity Relationship of AMPK Activation
title_full	Modeling Structure–Activity Relationship of AMPK Activation
title_fullStr	Modeling Structure–Activity Relationship of AMPK Activation
title_full_unstemmed	Modeling Structure–Activity Relationship of AMPK Activation
title_sort	modeling structure–activity relationship of ampk activation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d5c06952b0134553af2176989661a101
work_keys_str_mv	AT jurgendrewe modelingstructureactivityrelationshipofampkactivation AT ernstkusters modelingstructureactivityrelationshipofampkactivation AT felixhammann modelingstructureactivityrelationshipofampkactivation AT matthiaskreuter modelingstructureactivityrelationshipofampkactivation AT philippboss modelingstructureactivityrelationshipofampkactivation AT verenaschoning modelingstructureactivityrelationshipofampkactivation
_version_	1718431853108527104

Modeling Structure–Activity Relationship of AMPK Activation

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