Inference of drug off-target effects on cellular signaling using interactome-based deep learning
Summary: Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell’s transcriptional response...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224007302 |
| _version_ | 1827302722012971008 |
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| author | Nikolaos Meimetis Douglas A. Lauffenburger Avlant Nilsson |
| author_facet | Nikolaos Meimetis Douglas A. Lauffenburger Avlant Nilsson |
| author_sort | Nikolaos Meimetis |
| collection | DOAJ |
| description | Summary: Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell’s transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors’ activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design. |
| first_indexed | 2024-04-24T16:50:04Z |
| format | Article |
| id | doaj.art-e27deefd60e146d69deed574cc746216 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-24T16:50:04Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-e27deefd60e146d69deed574cc7462162024-03-29T05:51:02ZengElsevieriScience2589-00422024-04-01274109509Inference of drug off-target effects on cellular signaling using interactome-based deep learningNikolaos Meimetis0Douglas A. Lauffenburger1Avlant Nilsson2Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden; Corresponding authorSummary: Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell’s transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors’ activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.http://www.sciencedirect.com/science/article/pii/S2589004224007302BioinformaticsBiological sciencesHealth informaticsHealth sciencesMedical informaticsNatural sciences |
| spellingShingle | Nikolaos Meimetis Douglas A. Lauffenburger Avlant Nilsson Inference of drug off-target effects on cellular signaling using interactome-based deep learning iScience Bioinformatics Biological sciences Health informatics Health sciences Medical informatics Natural sciences |
| title | Inference of drug off-target effects on cellular signaling using interactome-based deep learning |
| title_full | Inference of drug off-target effects on cellular signaling using interactome-based deep learning |
| title_fullStr | Inference of drug off-target effects on cellular signaling using interactome-based deep learning |
| title_full_unstemmed | Inference of drug off-target effects on cellular signaling using interactome-based deep learning |
| title_short | Inference of drug off-target effects on cellular signaling using interactome-based deep learning |
| title_sort | inference of drug off target effects on cellular signaling using interactome based deep learning |
| topic | Bioinformatics Biological sciences Health informatics Health sciences Medical informatics Natural sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224007302 |
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