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Unlocking latent kinetic information from label-free binding
Transient affinity binding interactions are central to life, composing the fundamental elements of biological networks including cell signaling, cell metabolism and gene regulation. Assigning a defined reaction mechanism to affinity binding interactions is critical to our understanding of the associ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6895076/ https://www.ncbi.nlm.nih.gov/pubmed/31804511 http://dx.doi.org/10.1038/s41598-019-54485-4 |
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author | Quinn, John G. Steffek, Micah Bruning, John M. Frommlet, Alexandra Mulvihill, Melinda M. |
author_facet | Quinn, John G. Steffek, Micah Bruning, John M. Frommlet, Alexandra Mulvihill, Melinda M. |
author_sort | Quinn, John G. |
collection | PubMed |
description | Transient affinity binding interactions are central to life, composing the fundamental elements of biological networks including cell signaling, cell metabolism and gene regulation. Assigning a defined reaction mechanism to affinity binding interactions is critical to our understanding of the associated structure-function relationship, a cornerstone of biophysical characterization. Transient kinetics are currently measured using low throughput methods such as nuclear magnetic resonance, or stop-flow spectrometry-based techniques, which are not practical in many settings. In contrast, label-free biosensors measure reaction kinetics through direct binding, and with higher throughout, impacting life sciences with thousands of publications each year. Here we have developed a methodology enabling label-free biosensors to measure transient kinetic interactions towards providing a higher throughput approach suitable for mechanistic understanding of these processes. The methodology relies on hydrodynamic dispersion modeling of a smooth analyte gradient under conditions that maintain the quasi-steady-state boundary layer assumption. A transient peptide-protein interaction of relevance to drug discovery was analyzed thermodynamically using transition state theory and numerical simulations validated the approach over a wide range of operating conditions. The data establishes the technical feasibility of this approach to transient kinetic analyses supporting further development towards higher throughput applications in life science. |
format | Online Article Text |
id | pubmed-6895076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68950762019-12-11 Unlocking latent kinetic information from label-free binding Quinn, John G. Steffek, Micah Bruning, John M. Frommlet, Alexandra Mulvihill, Melinda M. Sci Rep Article Transient affinity binding interactions are central to life, composing the fundamental elements of biological networks including cell signaling, cell metabolism and gene regulation. Assigning a defined reaction mechanism to affinity binding interactions is critical to our understanding of the associated structure-function relationship, a cornerstone of biophysical characterization. Transient kinetics are currently measured using low throughput methods such as nuclear magnetic resonance, or stop-flow spectrometry-based techniques, which are not practical in many settings. In contrast, label-free biosensors measure reaction kinetics through direct binding, and with higher throughout, impacting life sciences with thousands of publications each year. Here we have developed a methodology enabling label-free biosensors to measure transient kinetic interactions towards providing a higher throughput approach suitable for mechanistic understanding of these processes. The methodology relies on hydrodynamic dispersion modeling of a smooth analyte gradient under conditions that maintain the quasi-steady-state boundary layer assumption. A transient peptide-protein interaction of relevance to drug discovery was analyzed thermodynamically using transition state theory and numerical simulations validated the approach over a wide range of operating conditions. The data establishes the technical feasibility of this approach to transient kinetic analyses supporting further development towards higher throughput applications in life science. Nature Publishing Group UK 2019-12-05 /pmc/articles/PMC6895076/ /pubmed/31804511 http://dx.doi.org/10.1038/s41598-019-54485-4 Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Quinn, John G. Steffek, Micah Bruning, John M. Frommlet, Alexandra Mulvihill, Melinda M. Unlocking latent kinetic information from label-free binding |
title | Unlocking latent kinetic information from label-free binding |
title_full | Unlocking latent kinetic information from label-free binding |
title_fullStr | Unlocking latent kinetic information from label-free binding |
title_full_unstemmed | Unlocking latent kinetic information from label-free binding |
title_short | Unlocking latent kinetic information from label-free binding |
title_sort | unlocking latent kinetic information from label-free binding |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6895076/ https://www.ncbi.nlm.nih.gov/pubmed/31804511 http://dx.doi.org/10.1038/s41598-019-54485-4 |
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