Protein Interaction Partners
Protein Interaction Partners - Related Videos
10:02
Identification of Protein Interacting Partners Using Tandem Affinity Purification
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Cited by 20 •
2012
Tandem affinity purification is a robust approach for the identification of protein binding partners. As proof of concept, this methodology was applied to the well-characterized translation initiation factor eIF4E to co-precipitate the host cell factors involved in translation initiation. This method is easily adapted to any cellular or viral protein.
12:53
Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics
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Cited by 28 •
2014
SILAC immunoprecipitation experiments represent a powerful means for discovering novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants, and low affinity interactions preserved through use of less-stringent buffer conditions.
04:05
Competition Binding Assay to Study Competing GTPase-Binding Protein Partners
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2025
This video demonstrates a competition assay to study GTPase-binding protein partners. Utilizing nucleotide-bound GTPase protein immobilized on magnetic beads, the competitive binding between two interacting protein partners for the same binding site on the GTPase can be studied to assess the binding affinities of the protein partners.
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.
13:56
A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Cited by 4 •
2013
This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.
FAQs
Selective binding depends on complementary molecular surfaces rather than on protein abundance alone. Binding domains and short sequence motifs provide recognition features, while electrostatic forces and hydrophobic contacts help stabilize the association. Because proteins can contain multiple interaction regions, these molecular features may also help organize larger protein complexes and connect related cellular processes.
Cofactors and post-translational modifications can strengthen, weaken, or otherwise alter associations between proteins. Their effects may change which interaction partners are present in a complex or signaling pathway. This regulation helps explain why an interaction network can differ across cellular conditions, making interaction studies important for understanding context-dependent protein function.
A protein’s role often becomes clearer when its interaction partners are considered within a broader network. Associations can connect proteins involved in cellular structure, signaling, transport, or metabolism, revealing relationships that may not be apparent from one protein alone. Changes or disruptions within these networks can also provide insight into how altered interactions contribute to disease.
Researchers can combine affinity purification, co-immunoprecipitation, and two-hybrid assays to investigate protein associations. These experimental approaches provide complementary ways to examine which proteins occur together or interact under the conditions being studied. Computational analysis can then support interpretation by organizing the observed associations into protein complexes and larger interaction networks.
These methods address protein association through distinct experimental approaches, so their results can provide complementary evidence rather than a single definitive view. Affinity purification and co-immunoprecipitation support investigation of proteins associated in experimental samples, whereas two-hybrid assays examine interaction through a separate assay format. Combining approaches can strengthen interpretation of candidate partners.
Mapping interaction partners can help researchers identify protein complexes, connect components within signaling pathways, and interpret functions related to transport, metabolism, or cellular structure. When computational analysis is combined with experimental findings, the resulting interaction network can offer a broader view of cellular organization and help highlight associations whose disruption may be relevant to disease.