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Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP).

ABSTRACT: The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research is deciphering how these dynamic protein complexes act to integrate signals from multiple sources in order to direct a specific biological response, and how this becomes deregulated in many disease settings. Despite the importance of this key biochemical mechanism, there is a lack of experimental techniques that can facilitate the specific and sensitive deconvolution of these multi-molecular signaling complexes. Here this shortcoming is addressed through the combination of a protein complementation assay with a conformation-specific nanobody, which we have termed Bimolecular Complementation Affinity Purification (BiCAP). This novel technique facilitates the specific isolation and downstream proteomic characterization of any pair of interacting proteins, to the exclusion of un-complexed individual proteins and complexes formed with competing binding partners. The BiCAP technique is adaptable to a wide array of downstream experimental assays, and the high degree of specificity afforded by this technique allows more nuanced investigations into the mechanics of protein complex assembly than is currently possible using standard affinity purification techniques.

SUBMITTER: Hastings JF

PROVIDER: S-EPMC6101743 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP).

Hastings Jordan F JF Han Jeremy Z R JZR Shearer Robert F RF Kennedy Sean P SP Iconomou Mary M Saunders Darren N DN Croucher David R DR

Journal of visualized experiments : JoVE 20180615 136

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research is deciphering how these dynamic protein complexes act to integrate signals from multiple sources in order to direct a specific biological response, and how this becomes deregulated in many disease settings. Despite the importance of this key biochemical mechanism, there is a lack of experimental techniques that can facilitate the specific and s ...[more]

PMID: 29985350

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Project description:BACKGROUND: Recent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored. METHODS: This paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation. RESULTS: Experimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes. CONCLUSIONS: Our study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.

Project description:Bimolecular Fluorescence Complementation (BiFC) assay is a method used to directly visualize protein-protein interaction in vivo using live-cell imaging or fixed cells. This protocol described here is based on our recent paper describing the functional association of human chromatin adaptor and transcription cofactor Brd4 with p53 tumor suppressor protein (Wu et al., 2013). BiFC was first described by Hu et al. (2002) using two non-fluorescent protein fragments of enhanced yellow fluorescent protein (EYFP), which is an Aequorea victoria GFP variant protein, fused respectively to a Rel family protein and a bZIP family transcription factor to investigate interactions between these two family members in living cells. The YFP was later improved by introducing mutations to reduce its sensitivity to pH and chloride ions, thus generating a super-enhanced YFP, named Venus fluorescent protein, without showing diminished fluorescence at 37 °C as typically observed with EYFP (Nagai et al., 2006). The fluorescence signal is regenerated by complementation of two non-fluorescent fragments (e.g., the Venus N-terminal 1-158 amino acid residues, called Venus-N, and its C-terminal 159-239 amino acid residues, named Venus-C; see Figure 1A and Gully et al., 2012; Ding et al., 2006; Kerppola, 2006) that are brought together by interaction between their respective fusion partners (e.g., Venus-N to p53, and Venus-C to the PDID domain of human Brd4; see Figure 1B and 1C). The intensity and cellular location of the regenerated fluorescence signals can be detected by fluorescence microscope. The advantages of the proximity-based BiFC assay are: first, it allows a direct visualization of spatial and temporal interaction between two partner proteins in vivo; second, the fluorescence signal provides a sensitive readout for detecting protein-protein interaction even at a low expression level comparable to that of the endogenous proteins; third, the intensity of the fluorescence signal is proportional to the strength of protein-protein interaction (Morell et al., 2008); and fourth, the BiFC signals are derived from intrinsic protein-protein interaction, rather than from extrinsic fluorophores that may not reflect true protein-protein interaction due to their nonspecific association with cellular macromolecules or subcellular compartments. However, some limitations of BiFC include slow maturation (T1/2 ~ 1 h) of an eventually stable BiFC complex (Hu et al., 2002), making it unsuitable for real-time observation of transient interaction that disappears prior to BiFC detection, and enhanced BiFC background at high expression levels due to fusion-independent association between two non-fluorescent fragments association. BiFC signals generated by in vivo protein-protein interaction can be validated by amino acid mutation introduced at the protein-protein contact surfaces. This imaging technique has been widely used in different cell types and organisms (Kerppola, 2006).

Dataset Information

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP).

Publications

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP).

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