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Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs h...

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Autores principales: Singh, Nivedita, Khan, Faiz M, Bala, Lakshmi, Vera, Julio, Wolkenhauer, Olaf, Pützer, Brigitte, Logotheti, Stella, Gupta, Shailendra K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666365/
https://www.ncbi.nlm.nih.gov/pubmed/37993971
http://dx.doi.org/10.1186/s13065-023-01082-2
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author Singh, Nivedita
Khan, Faiz M
Bala, Lakshmi
Vera, Julio
Wolkenhauer, Olaf
Pützer, Brigitte
Logotheti, Stella
Gupta, Shailendra K.
author_facet Singh, Nivedita
Khan, Faiz M
Bala, Lakshmi
Vera, Julio
Wolkenhauer, Olaf
Pützer, Brigitte
Logotheti, Stella
Gupta, Shailendra K.
author_sort Singh, Nivedita
collection PubMed
description Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13065-023-01082-2.
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spelling pubmed-106663652023-11-22 Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression Singh, Nivedita Khan, Faiz M Bala, Lakshmi Vera, Julio Wolkenhauer, Olaf Pützer, Brigitte Logotheti, Stella Gupta, Shailendra K. BMC Chem Research Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13065-023-01082-2. Springer International Publishing 2023-11-22 /pmc/articles/PMC10666365/ /pubmed/37993971 http://dx.doi.org/10.1186/s13065-023-01082-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Research
Singh, Nivedita
Khan, Faiz M
Bala, Lakshmi
Vera, Julio
Wolkenhauer, Olaf
Pützer, Brigitte
Logotheti, Stella
Gupta, Shailendra K.
Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
title Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
title_full Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
title_fullStr Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
title_full_unstemmed Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
title_short Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression
title_sort logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against e2f1-driven melanoma progression
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666365/
https://www.ncbi.nlm.nih.gov/pubmed/37993971
http://dx.doi.org/10.1186/s13065-023-01082-2
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