Related Experiment Video
Updated: Oct 13, 2025
Oligopeptide Competition Assay for Phosphorylation Site Determination
Published on: May 18, 2017
Modeling Structure-Activity Relationship of AMPK Activation
Jürgen Drewe1, Ernst Küsters2, Felix Hammann3
1Medical Department, Max Zeller Söhne AG, CH-8590 Romanshorn, Switzerland.
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.
Insights
Machine learning models can now rapidly screen compounds for activating adenosine monophosphate activated protein kinase (AMPK). This tool aids in identifying potential therapeutic agents for diseases like cancer and diabetes.
Area of Science:
- Biochemistry
- Computational Biology
- Pharmacology
Background:
- Adenosine monophosphate activated protein kinase (AMPK) regulates key cellular functions including metabolism and growth.
- AMPK activation is therapeutically beneficial in diseases such as metabolic syndrome, obesity, diabetes, and cancer.
- Natural compounds often activate AMPK indirectly, posing challenges due to complex mixtures and limited availability of pure substances.
Purpose of the Study:
- To develop a computational screening tool for identifying adenosine monophosphate activated protein kinase (AMPK) activators.
- To address the heterogeneity in AMPK activation pathways caused by complex natural compound mixtures.
- To enable rapid in silico screening of compounds and mixtures for potential therapeutic applications.
Main Methods:
- Machine learning algorithms including random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural networks were applied.
- Models were trained and validated using a database of 904 activating and 799 neutral or inhibiting compounds.
- Data was sourced from extensive PubMed literature searches and the PubChem Bioassay database.
Main Results:
- All machine learning models demonstrated high classification accuracy during training.
- Crucially, the models exhibited strong predictive performance on unseen test data.
- The developed models are effective for rapid in silico screening of diverse chemical entities.
Conclusions:
- Machine learning provides a robust method for screening compounds that modulate adenosine monophosphate activated protein kinase (AMPK) activity.
- These computational tools can accelerate the identification of novel AMPK activators from complex mixtures.
- The validated models offer a valuable resource for discovering compounds for further experimental testing and therapeutic development.
More Related Videos
10:41Visualizing Protein Kinase A Activity In Head-fixed Behaving Mice Using In Vivo Two-photon Fluorescence Lifetime Imaging Microscopy
Published on: June 7, 2019
08:03Live-Cell Förster Resonance Energy Transfer Imaging of Metabolically Regulated Akt Activation Dynamics in HepG2 Cells
Published on: May 23, 2025
Related Concept Videos
01:25cAMP-dependent Protein Kinase Pathways
01:22PI3K/mTOR/AKT Signaling Pathway
01:07MAPK Signaling Cascades
01:22Amplifying Signals via Enzymatic Cascade
01:16Calmodulin-dependent Signaling
The Ca2+-CaM complex does not have enzymatic activity by itself. Instead, the complex binds downstream target proteins, including membrane proteins or enzymes,...
01:19Interactions Between Signaling Pathways
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...