Protein Interaction Quantification
Protein Interaction Quantification - Related Videos
00:07
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Cited by 14 •
2019
Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.
14:26
In vivo Quantification of G Protein Coupled Receptor Interactions using Spectrally Resolved Two-photon Microscopy
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Cited by 6 •
2011
By employing a spectrally resolved two-photon microscopy imaging system, pixel-level maps of Förster Resonance Energy Transfer (FRET) efficiencies are obtained for cells expressing membrane receptors hypothesized to form homo-oligomeric complexes. From the FRET efficiency maps, we are able to estimate stoichiometric information about the oligomer complex under study.
14:08
Study of Protein-protein Interactions in Autophagy Research
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Cited by 3 •
2017
Presented here are two antibody-based protein-protein interaction research techniques: immunofluorescence and immunoprecipitation. These techniques are suitable for studying physical interactions between proteins for the discovery of novel components of cellular signaling pathways and for understanding protein dynamics.
11:15
Imaging Protein-protein Interactions in vivo
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Cited by 5 •
2010
This protocol describes how to image protein-protein interactions using a FRET-based proximity assay.
07:42
In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces
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Cited by 7 •
2010
This video shows experiments with subsequent analysis of protein-protein interactions by the use of micro-patterned surfaces. The approach offers the possibility to detect protein interactions in living cells and combines high throughput capabilities with the possibility to extract quantitative information.
FAQs
An assay first produces a measurable signal, such as a binding response, fluorescence, or estimate of complex abundance. Calibration links the signal to interaction strength or concentration, while controlled conditions make measurements comparable. This conversion turns detection of association into data that can support direct comparisons among protein interactions.
Interaction strength indicates how strongly proteins associate under the tested conditions, whereas complex abundance reflects how much associated material is detected. Considering both dimensions helps distinguish differences in binding behavior from differences in the amount of complex present. That distinction improves interpretation of molecular mechanisms and protein function.
Controlled conditions provide a consistent basis for interpreting changes in measured interaction strength, concentration, or complex abundance. Without that consistency, a different signal may reflect altered experimental conditions rather than a genuine difference between protein associations. Careful control therefore supports reliable comparisons across samples, mutations, treatments, or cellular pathway states.
The workflow begins by capturing or detecting interacting protein partners. Researchers then measure a signal associated with the interaction, such as binding response, fluorescence, or complex abundance. Calibration converts that signal into an estimate of interaction strength or concentration, and controlled conditions help make the resulting measurements suitable for comparison.
Researchers can measure the same interaction across samples containing different mutations or treatments, then compare the resulting strength, concentration, or complex-abundance estimates. A changed value can indicate that the perturbation affects protein association. These comparisons help connect molecular interaction changes with altered signaling, regulatory networks, or cellular pathway behavior.
The resulting measurements can characterize protein complexes, map signaling and regulatory networks, and evaluate changes in cellular pathways. In broader biological research, they support mechanistic studies of protein function and investigation of disease processes. They can also contribute to therapeutic target development by revealing interaction changes relevant to biological regulation.