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Advanced Activity-Based Protein Profiling Application Strategies for Drug Development
Drug targets and modes of action remain two of the biggest challenges in drug development. To address these problems, chemical proteomic approaches have been introduced to profile targets in complex proteomes. Activity-based protein profiling (ABPP) is one of a growing number chemical proteomic appr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5900428/ https://www.ncbi.nlm.nih.gov/pubmed/29686618 http://dx.doi.org/10.3389/fphar.2018.00353 |
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author | Wang, Shan Tian, Yu Wang, Min Wang, Min Sun, Gui-bo Sun, Xiao-bo |
author_facet | Wang, Shan Tian, Yu Wang, Min Wang, Min Sun, Gui-bo Sun, Xiao-bo |
author_sort | Wang, Shan |
collection | PubMed |
description | Drug targets and modes of action remain two of the biggest challenges in drug development. To address these problems, chemical proteomic approaches have been introduced to profile targets in complex proteomes. Activity-based protein profiling (ABPP) is one of a growing number chemical proteomic approaches that uses small-molecule chemical probes to understand the interaction mechanisms between compounds and targets. ABPP can be used to identify the protein targets of small molecules and even the active sites of target proteins. This review focuses on the overall workflow of the ABPP technology and on additional advanced strategies for target identification and/or drug discovery. Herein, we mainly describe the design strategies for small-molecule probes and discuss the ways in which these probes can be used to identify targets and even validate the interactions of small molecules with targets. In addition, we discuss some basic strategies that have been developed to date, such as click chemistry-ABPP, competitive strategies and, recently, more advanced strategies, including isoTOP-ABPP, fluoPol-ABPP, and qNIRF-ABPP. The isoTOP-ABPP strategy has been coupled with quantitative proteomics to identify the active sites of proteins and explore whole proteomes with specific amino acid profiling. FluoPol-ABPP combined with HTS can be used to discover new compounds for some substrate-free enzymes. The qNIRF-ABPP strategy has a number of applications for in vivo imaging. In this review, we will further discuss the applications of these advanced strategies. |
format | Online Article Text |
id | pubmed-5900428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59004282018-04-23 Advanced Activity-Based Protein Profiling Application Strategies for Drug Development Wang, Shan Tian, Yu Wang, Min Wang, Min Sun, Gui-bo Sun, Xiao-bo Front Pharmacol Pharmacology Drug targets and modes of action remain two of the biggest challenges in drug development. To address these problems, chemical proteomic approaches have been introduced to profile targets in complex proteomes. Activity-based protein profiling (ABPP) is one of a growing number chemical proteomic approaches that uses small-molecule chemical probes to understand the interaction mechanisms between compounds and targets. ABPP can be used to identify the protein targets of small molecules and even the active sites of target proteins. This review focuses on the overall workflow of the ABPP technology and on additional advanced strategies for target identification and/or drug discovery. Herein, we mainly describe the design strategies for small-molecule probes and discuss the ways in which these probes can be used to identify targets and even validate the interactions of small molecules with targets. In addition, we discuss some basic strategies that have been developed to date, such as click chemistry-ABPP, competitive strategies and, recently, more advanced strategies, including isoTOP-ABPP, fluoPol-ABPP, and qNIRF-ABPP. The isoTOP-ABPP strategy has been coupled with quantitative proteomics to identify the active sites of proteins and explore whole proteomes with specific amino acid profiling. FluoPol-ABPP combined with HTS can be used to discover new compounds for some substrate-free enzymes. The qNIRF-ABPP strategy has a number of applications for in vivo imaging. In this review, we will further discuss the applications of these advanced strategies. Frontiers Media S.A. 2018-04-09 /pmc/articles/PMC5900428/ /pubmed/29686618 http://dx.doi.org/10.3389/fphar.2018.00353 Text en Copyright © 2018 Wang, Tian, Wang, Wang, Sun and Sun. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Wang, Shan Tian, Yu Wang, Min Wang, Min Sun, Gui-bo Sun, Xiao-bo Advanced Activity-Based Protein Profiling Application Strategies for Drug Development |
title | Advanced Activity-Based Protein Profiling Application Strategies for Drug Development |
title_full | Advanced Activity-Based Protein Profiling Application Strategies for Drug Development |
title_fullStr | Advanced Activity-Based Protein Profiling Application Strategies for Drug Development |
title_full_unstemmed | Advanced Activity-Based Protein Profiling Application Strategies for Drug Development |
title_short | Advanced Activity-Based Protein Profiling Application Strategies for Drug Development |
title_sort | advanced activity-based protein profiling application strategies for drug development |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5900428/ https://www.ncbi.nlm.nih.gov/pubmed/29686618 http://dx.doi.org/10.3389/fphar.2018.00353 |
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