Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome

Drosophila Tel2 is expressed as a translational fusion with EpsinR and is a regulator of wingless signaling

Tel2, a protein conserved from yeast to vertebrates, is an essential regulator of diverse cellular processes including telomere maintenance, DNA damage checkpoints, DNA repair, biological clocks, and cell signaling. The Drosophila Tel2 protein is produced as a translational fusion with EpsinR, a Clathrin adapter that facilitates vesicle trafficking between the Golgi and endosomes. EpsinR and Tel2 are encoded by a Drosophila gene called lqfR. lqfR is required for viability, and its specific roles include cell growth, proliferation, and planar cell polarity. We find that all of these functions of lqfR are attributed entirely to Tel2, not EpsinR. In addition, we find that Drosophila LqfR/Tel2 is a component of one or more protein complexes that contain E-cadherin and Armadillo. Moreover, Tel2 modulates E-cadherin and Armadillo cellular dynamics. We propose that at least one of the functions of Drosophila Tel2 is regulation of Wingless signaling.

Mass spectrometry approaches to study mammalian kinase and phosphatase associated proteins

Reversible phosphorylation events regulate critical aspects of cellular biology by affecting protein conformation, cellular localization, enzymatic activity and associations with interaction partners. Kinases and phosphatases interact not only with their substrates but also with regulatory subunits and other proteins, including scaffolds. In recent years, affinity purification coupled to mass spectrometry (AP-MS) has proven to be a powerful tool to identify protein-protein interactions (PPIs) involving kinases and phosphatases. In this review we outline general considerations for successful AP-MS, and describe strategies that we have used to characterize the interactions of kinases and phosphatases in human cells.

Beyond hairballs: The use of quantitative mass spectrometry data to understand protein-protein interactions

The past 10 years have witnessed a dramatic proliferation in the availability of protein interaction data. However, for interaction mapping based on affinity purification coupled with mass spectrometry (AP-MS), there is a wealth of information present in the datasets that often goes unrecorded in public repositories, and as such remains largely unexplored. Further, how this type of data is represented and used by bioinformaticians has not been well established. Here, we point out some common mistakes in how AP-MS data are handled, and describe how protein complex organization and interaction dynamics can be inferred using quantitative AP-MS approaches.

Autophagosomal protein dynamics and influenza virus infection

Autophagy is a constitutive, catabolic process leading to the lysosomal degradation of cytosolic proteins and organelles. However, it is also induced under stress conditions, remodeling the eukaryotic cell by regulating energy, protein, and lipid homeostasis. It is likely that the autophagosomal/lysosomal pathway evolved primordially to recycle cell components, but further functionally developed as to become part of the immune system to defend against invading pathogens. Likewise, pathogenic, foreign agents developed strategies to fight back and even to employ the autophagy machinery to their own benefit. Hence, the regulation of autophagy has many implications on human health and disease. This review summarizes the molecular dynamics of autophagosome formation, maturation, and target selection. Membrane dynamics, as well as protein–protein and protein–membrane interactions are particularly addressed. In addition, it recapitulates current knowledge of the influences of influenza virus infection on the process.

SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments

We present a statistical method SAINT-MS1 for scoring protein-protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range.

Mass spectrometry-based proteomics for systems biology

Mass spectrometry (MS)-based proteomics has significantly contributed to the development of systems biology, a new paradigm for the life sciences in which biological processes are addressed in terms of dynamic networks of interacting molecules. Because of its advanced analytical capabilities, MS-based proteomics has been used extensively to identify the components of biological systems, and it is the method of choice to consistently quantify the effects of network perturbation in time and space. Herein, we review recent contributions of MS to systems biology and discuss several examples that illustrate the importance of mass spectrometry to elucidate the components and interactions of molecular networks.