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Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders
[Image: see text] E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features nece...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448689/ https://www.ncbi.nlm.nih.gov/pubmed/37636935 http://dx.doi.org/10.1021/acsomega.3c02803 |
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author | Karki, Reagon Gadiya, Yojana Gribbon, Philip Zaliani, Andrea |
author_facet | Karki, Reagon Gadiya, Yojana Gribbon, Philip Zaliani, Andrea |
author_sort | Karki, Reagon |
collection | PubMed |
description | [Image: see text] E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity. While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, we present a fast and an inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multi-class machine learning classification model. This model can assign the correct E3 ligase binder to its known E3 ligase and predict the probability of each molecule to bind to different E3 ligases. Practical applications of this approach are demonstrated on commercial libraries such as Asinex for the rational design of E3 ligase binders. The scripts and data associated with this study can be found on GitHub at https://github.com/Fraunhofer-ITMP/E3_binder_Model. |
format | Online Article Text |
id | pubmed-10448689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104486892023-08-25 Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders Karki, Reagon Gadiya, Yojana Gribbon, Philip Zaliani, Andrea ACS Omega [Image: see text] E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity. While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, we present a fast and an inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multi-class machine learning classification model. This model can assign the correct E3 ligase binder to its known E3 ligase and predict the probability of each molecule to bind to different E3 ligases. Practical applications of this approach are demonstrated on commercial libraries such as Asinex for the rational design of E3 ligase binders. The scripts and data associated with this study can be found on GitHub at https://github.com/Fraunhofer-ITMP/E3_binder_Model. American Chemical Society 2023-08-09 /pmc/articles/PMC10448689/ /pubmed/37636935 http://dx.doi.org/10.1021/acsomega.3c02803 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Karki, Reagon Gadiya, Yojana Gribbon, Philip Zaliani, Andrea Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders |
title | Pharmacophore-Based
Machine Learning Model To Predict
Ligand Selectivity for E3 Ligase Binders |
title_full | Pharmacophore-Based
Machine Learning Model To Predict
Ligand Selectivity for E3 Ligase Binders |
title_fullStr | Pharmacophore-Based
Machine Learning Model To Predict
Ligand Selectivity for E3 Ligase Binders |
title_full_unstemmed | Pharmacophore-Based
Machine Learning Model To Predict
Ligand Selectivity for E3 Ligase Binders |
title_short | Pharmacophore-Based
Machine Learning Model To Predict
Ligand Selectivity for E3 Ligase Binders |
title_sort | pharmacophore-based
machine learning model to predict
ligand selectivity for e3 ligase binders |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448689/ https://www.ncbi.nlm.nih.gov/pubmed/37636935 http://dx.doi.org/10.1021/acsomega.3c02803 |
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