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Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

[Image: see text] Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of the...

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Autores principales: Duran-Frigola, Miquel, Cigler, Marko, Winter, Georg E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912273/
https://www.ncbi.nlm.nih.gov/pubmed/36706315
http://dx.doi.org/10.1021/jacs.2c11098
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author Duran-Frigola, Miquel
Cigler, Marko
Winter, Georg E.
author_facet Duran-Frigola, Miquel
Cigler, Marko
Winter, Georg E.
author_sort Duran-Frigola, Miquel
collection PubMed
description [Image: see text] Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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spelling pubmed-99122732023-02-11 Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence Duran-Frigola, Miquel Cigler, Marko Winter, Georg E. J Am Chem Soc [Image: see text] Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities. American Chemical Society 2023-01-27 /pmc/articles/PMC9912273/ /pubmed/36706315 http://dx.doi.org/10.1021/jacs.2c11098 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Duran-Frigola, Miquel
Cigler, Marko
Winter, Georg E.
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
title Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
title_full Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
title_fullStr Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
title_full_unstemmed Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
title_short Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
title_sort advancing targeted protein degradation via multiomics profiling and artificial intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912273/
https://www.ncbi.nlm.nih.gov/pubmed/36706315
http://dx.doi.org/10.1021/jacs.2c11098
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