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Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution

[Image: see text] In-cell NMR can investigate protein conformational changes at atomic resolution, such as those changes induced by drug binding or chemical modifications, directly in living human cells, and therefore has great potential in the context of drug development as it can provide an early...

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Autores principales: Luchinat, Enrico, Barbieri, Letizia, Campbell, Timothy F., Banci, Lucia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735651/
https://www.ncbi.nlm.nih.gov/pubmed/32551584
http://dx.doi.org/10.1021/acs.analchem.0c01677
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author Luchinat, Enrico
Barbieri, Letizia
Campbell, Timothy F.
Banci, Lucia
author_facet Luchinat, Enrico
Barbieri, Letizia
Campbell, Timothy F.
Banci, Lucia
author_sort Luchinat, Enrico
collection PubMed
description [Image: see text] In-cell NMR can investigate protein conformational changes at atomic resolution, such as those changes induced by drug binding or chemical modifications, directly in living human cells, and therefore has great potential in the context of drug development as it can provide an early assessment of drug potency. NMR bioreactors can greatly improve the cell sample stability over time and, more importantly, allow for recording in-cell NMR data in real time to monitor the evolution of intracellular processes, thus providing unique insights into the kinetics of drug-target interactions. However, current implementations are limited by low cell viability at >24 h times, the reduced sensitivity compared to “static” experiments and the lack of protocols for automated and quantitative analysis of large amounts of data. Here, we report an improved bioreactor design which maintains human cells alive and metabolically active for up to 72 h, and a semiautomated workflow for quantitative analysis of real-time in-cell NMR data relying on Multivariate Curve Resolution. We apply this setup to monitor protein–ligand interactions and protein oxidation in real time. High-quality concentration profiles can be obtained from noisy 1D and 2D NMR data with high temporal resolution, allowing further analysis by fitting with kinetic models. This unique approach can therefore be applied to investigate complex kinetic behaviors of macromolecules in a cellular setting, and could be extended in principle to any real-time NMR application in live cells.
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spelling pubmed-77356512020-12-15 Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution Luchinat, Enrico Barbieri, Letizia Campbell, Timothy F. Banci, Lucia Anal Chem [Image: see text] In-cell NMR can investigate protein conformational changes at atomic resolution, such as those changes induced by drug binding or chemical modifications, directly in living human cells, and therefore has great potential in the context of drug development as it can provide an early assessment of drug potency. NMR bioreactors can greatly improve the cell sample stability over time and, more importantly, allow for recording in-cell NMR data in real time to monitor the evolution of intracellular processes, thus providing unique insights into the kinetics of drug-target interactions. However, current implementations are limited by low cell viability at >24 h times, the reduced sensitivity compared to “static” experiments and the lack of protocols for automated and quantitative analysis of large amounts of data. Here, we report an improved bioreactor design which maintains human cells alive and metabolically active for up to 72 h, and a semiautomated workflow for quantitative analysis of real-time in-cell NMR data relying on Multivariate Curve Resolution. We apply this setup to monitor protein–ligand interactions and protein oxidation in real time. High-quality concentration profiles can be obtained from noisy 1D and 2D NMR data with high temporal resolution, allowing further analysis by fitting with kinetic models. This unique approach can therefore be applied to investigate complex kinetic behaviors of macromolecules in a cellular setting, and could be extended in principle to any real-time NMR application in live cells. American Chemical Society 2020-06-18 2020-07-21 /pmc/articles/PMC7735651/ /pubmed/32551584 http://dx.doi.org/10.1021/acs.analchem.0c01677 Text en This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
spellingShingle Luchinat, Enrico
Barbieri, Letizia
Campbell, Timothy F.
Banci, Lucia
Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution
title Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution
title_full Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution
title_fullStr Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution
title_full_unstemmed Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution
title_short Real-Time Quantitative In-Cell NMR: Ligand Binding and Protein Oxidation Monitored in Human Cells Using Multivariate Curve Resolution
title_sort real-time quantitative in-cell nmr: ligand binding and protein oxidation monitored in human cells using multivariate curve resolution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735651/
https://www.ncbi.nlm.nih.gov/pubmed/32551584
http://dx.doi.org/10.1021/acs.analchem.0c01677
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