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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 |
Sumario: | 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. |
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