Computational detection of protein complexes in AP-MS experiments

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

Tandem Immunoaffinity Purification Using Anti-FLAG and Anti-HA Antibodies

The immunoaffinity purification of target proteins followed by the identification and characterization of associated proteins by mass spectrometry is a widely used technique. An immunoaffinity purification bears resemblance to a standard immunoprecipitation; however, the end product for mass spectrometric analysis in the femtomole (10^-15) to attomole (10^-18) range is required to be of exceptional purity. This high degree of sensitivity in detection renders it of extreme importance to eliminate most if not all of the nonspecific background proteins and can be achieved by performing a tandem affinity purification (TAP). In TAP, the cDNA of the target protein is engineered to contain at least two different epitope tags, and the target protein is extracted under nondenaturing conditions upon expression using an appropriate protein expression platform (CHO cells, HEK 293 cells, or yeast). The expressed protein is initially immunoprecipitated using an antibody against one epitope tag and is eluted in the presence of excess peptide by competition for antibody-binding sites, before being reimmunoprecipitated using an antibody that specifically recognizes the second epitope. These sequential immunoprecipitations significantly reduce the presence of associated nonspecific proteins. Numerous combinations of epitope tags have been applied for tandem affinity purification, and this protocol illustrates the use of tandem hemagglutinin (HA) and FLAG epitope tags. The first immunoprecipitation uses an anti-FLAG antibody followed by the elution in the presence of a competing FLAG peptide before the reimmunoprecipitation of the protein using an anti-HA antibody. Numerous high-quality antiepitope tag antibodies are commercially available from different antibody manufacturers.

A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks

Background

Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist.

Methods

In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes.

Results

Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer.

Conclusions

Our proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

Electronic supplementary material

The online version of this article (doi:10.1186/s13015-015-0044-6) contains supplementary material, which is available to authorized users.

Protein networks and activation of lymphocytes

The signal transduction pathways initiated by lymphocyte activation play a critical role in regulating host immunity. High-resolution mass spectrometry has accelerated the investigation of these complex and dynamic pathways by enabling the qualitative and quantitative investigation of thousands of proteins and phosphoproteins simultaneously. In addition, the unbiased and wide-scale identification of protein-protein interaction networks and protein kinase substrates in lymphocyte signaling pathways can be achieved by mass spectrometry-based approaches. Critically, the integration of these discovery-driven strategies with single-cell analysis using mass cytometry can facilitate the understanding of complex signaling phenotypes in distinct immunophenotypes.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Protein-protein interaction networks: probing disease mechanisms using model systems

Protein-protein interactions (PPIs) and multi-protein complexes perform central roles in the cellular systems of all living organisms. In humans, disruptions of the normal patterns of PPIs and protein complexes can be causative or indicative of a disease state. Recent developments in the biological applications of mass spectrometry (MS)-based proteomics have expanded the horizon for the application of systematic large-scale mapping of physical interactions to probe disease mechanisms. In this review, we examine the application of MS-based approaches for the experimental analysis of PPI networks and protein complexes, focusing on the different model systems (including human cells) used to study the molecular basis of common diseases such as cancer, cardiomyopathies, diabetes, microbial infections, and genetic and neurodegenerative disorders.