Modeling Structure–Activity Relationship of AMPK Activation

Abstract

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.

Machine Learning-Based Analysis Reveals Triterpene Saponins and Their Aglycones in Cimicifuga racemosa as Critical Mediators of AMPK Activation

“…As we have previously demonstrated [ 12 ], the positive controls within our dataset, which serve as activators, exhibit a notable structural diversity. This diversity arises from the fact that a significant proportion of activators exert their effects indirectly.…”

“…They achieve this by either influencing upstream kinases that subsequently phosphorylate AMPK or by reducing cellular ATP levels, leading to AMPK phosphorylation and subsequent activation. In particular, a variety of plant extracts or isolated plant constituents have been described in the literature to activate the enzyme [ 10 , 11 , 12 ].…”

“…Recently, we have published research about sensitive and accurate machine learning models for the classification of AMPK activators [ 12 ]. In the present study, an extended and updated version of this applied database of known activators and controls has been used to classify all chemically characterized constituents of the Cimicifuga extract Ze 450 to estimate its ability to activate AMPK.…”

N-Octyl Caffeamide, a Caffeic Acid Amide Derivative, Prevents Progression of Diabetes and Hepatic Steatosis in High-Fat Diet Induced Obese Mice

“…AMPK is a key master that regulates many cellular functions, such as lipid, glucose, and protein metabolism; cellular growth; and mitochondrial biogenesis and autophagy [ 31 , 32 ]. There is growing interest in developing AMPK activators because AMPK has been suggested to have beneficial clinical effects in various metabolic and degenerative diseases, aging, diabetes, cancer, and viral infection [ 17 , 18 , 32 , 33 ]. Various direct and indirect AMPK activators have been reported to maintain the homeostasis of glucose and lipid metabolism and may improve insulin sensitivity [ 18 ].…”