A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling

Mass spectrometry-based proteomics can reveal protein-protein interactions on a large scale, but it has been difficult to separate background binding from functionally important interactions and still preserve weak binders. To investigate the epidermal growth factor receptor (EGFR) pathway, we employ stable isotopic amino acids in cell culture (SILAC) to differentially label proteins in EGF-stimulated versus unstimulated cells. Combined cell lysates were affinity-purified over the SH2 domain of the adapter protein Grb2 (GST-SH2 fusion protein) that specifically binds phosphorylated EGFR and Src homologous and collagen (Shc) protein. We identified 228 proteins, of which 28 were selectively enriched upon stimulation. EGFR and Shc, which interact directly with the bait, had large differential ratios. Many signaling molecules specifically formed complexes with the activated EGFR-Shc, as did plectin, epiplakin, cytokeratin networks, histone H3, the glycosylphosphatidylinositol (GPI)-anchored molecule CD59, and two novel proteins. SILAC combined with modification-based affinity purification is a useful approach to detect specific and functional protein-protein interactions.     

蛋白质相互作用研究的新技术与新方法

目前,蛋白质相互作用已成为蛋白质组学研究的热点. 新方法的建立及对已有技术的改进标志着蛋白质相互作用研究的不断发展和完善．在技术改进方面,本文介绍了弥补酵母双杂交的蛋白定位受限等缺陷的细菌双杂交系统;根据目标蛋白特性设计和修饰TAP标签来满足复合体研究要求的串联亲和纯化技术,以及在双分子荧光互补基础上发展的动态检测多个蛋白质间瞬时、弱相互作用的多分子荧光互补技术．还综述了近两年建立的新方法：与免疫共沉淀相比,寡沉淀技术直接研究具有活性的蛋白质复合体;减量式定量免疫沉淀方法排除了蛋白质复合体中非特异性相互作用的干扰;原位操作的多表位－配基绘图法避免了样品间差异的影响,以及利用多点吸附和交联加固研究弱蛋白质相互作用的固相蛋白质组学方法.     

Identifying dynamic interactors of protein complexes by quantitative mass spectrometry

Dynamically interacting proteins associate and dissociate with their binding partners at high on/off rates. Although their identification is of great significance to proteomics research, lack of an efficient strategy to distinguish stable and dynamic interactors has hampered the efforts toward this goal. In this work, we developed a new method, MAP (mixing after purification)-SILAC (stable isotope labeling of amino acids in cell culture), to quantitatively investigate the interactions of protein complexes by mass spectrometry. In combination with the original SILAC approach, stable and dynamic components were effectively distinguished by the differences in their relative abundance ratio changes. We applied the newly developed strategies to decipher the dynamics of the human 26 S proteasome-interacting proteins. A total of 67 putative human proteasome-interacting proteins were identified by the MAP-SILAC method among which 14 proteins would have been misidentified as background proteins due to low relative abundance ratios in standard SILAC experiments and 57 proteins have not been reported previously. In addition, 35 of the 67 proteins were classified as stable interactors of the proteasome complex, whereas 16 of them were identified as dynamic interactors. The methods reported here provide a valuable expansion of proteomics technologies for identification of important but previously unidentifiable interacting proteins.     

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of 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. Low affinity interactions can be preserved through the use of less-stringent buffer conditions and remain readily identifiable. This protocol discusses the labeling of tissue culture cells with stable isotope labeled amino acids, transfection and immunoprecipitation of an affinity tagged protein of interest, followed by the preparation for submission to a mass spectrometry facility. This protocol then discusses how to analyze and interpret the data returned from the mass spectrometer in order to identify cellular partners interacting with a protein of interest. As an example this technique is applied to identify proteins binding to the eukaryotic translation initiation factors: eIF4AI and eIF4AII.     

Targeted tandem affinity purification of PSD‐95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins

The molecular complexity of mammalian proteomes demands new methods for mapping the organization of multiprotein complexes. Here, we combine mouse genetics and proteomics to characterize synapse protein complexes and interaction networks. New tandem affinity purification (TAP) tags were fused to the carboxyl terminus of PSD‐95 using gene targeting in mice. Homozygous mice showed no detectable abnormalities in PSD‐95 expression, subcellular localization or synaptic electrophysiological function. Analysis of multiprotein complexes purified under native conditions by mass spectrometry defined known and new interactors: 118 proteins comprising crucial functional components of synapses, including glutamate receptors, K⁺ channels, scaffolding and signaling proteins, were recovered. Network clustering of protein interactions generated five connected clusters, with two clusters containing all the major ionotropic glutamate receptors and one cluster with voltage‐dependent K⁺ channels. Annotation of clusters with human disease associations revealed that multiple disorders map to the network, with a significant correlation of schizophrenia within the glutamate receptor clusters. This targeted TAP tagging strategy is generally applicable to mammalian proteomics and systems biology approaches to disease.     

Extracting gene function from protein-protein interactions using Quantitative BAC InteraCtomics (QUBIC)

Large-scale proteomic screens are increasingly employed for placing genes into specific pathways. Therefore generic methods providing a physiological context for protein-protein interaction studies are of great interest. In recent years many protein-protein interactions have been determined by affinity purification followed by mass spectrometry (AP-MS). Among many different AP-MS approaches, the recently developed Quantitative BAC InteraCtomics (QUBIC) approach is particularly attractive as it uses tagged, full-length baits that are expressed under endogenous control. For QUBIC large cell line collections expressing tagged proteins from BAC transgenes or gene trap loci have been developed and are freely available. Here we describe detailed workflows on how to obtain specific protein binding partners with high confidence under physiological conditions. The methods are based on fast, streamlined and generic purification procedures followed by single run liquid chromatography-mass spectrometric analysis. Quantification is achieved either by the stable isotope labeling of amino acids in cell culture (SILAC) method or by a 'label-free' procedure. In either case data analysis is performed by using the freely available MaxQuant environment. The QUBIC approach enables biologists with access to high resolution mass spectrometry to perform small and large-scale protein interactome mappings.     

A randomized,double-blind,placebo-controlled phase 2 study evaluating the efficacy and safety of romiplostim treatment of patients with low or intermediate-1 risk myelodysplastic syndrome receiving lenalidomide

Mitochondria are the most complex and the most important organelles of eukaryotic cells, which are involved in many cellular processes, including energy metabolism, apoptosis, and aging. And mitochondria have been identified as the "hot spot" by researchers for exploring relevant associated dysfunctions in many fields. The emergence of comparative proteomics enables us to have a close look at the mitochondrial proteome in a comprehensive and effective manner under various conditions and cellular circumstances. Two-dimensional electrophoresis combined with mass spectrometry is still the most popular techniques to study comparative mitochondrial proteomics. Furthermore, many new techniques, such as ICAT, MudPIT, and SILAC, equip researchers with more flexibilities inselecting proper methods. This article also reviews the recent development of comparative mitochondrial proteomics on diverse human diseases. And the results of mitochondrial proteomics enhance a better understanding of the pathogenesis associated with mitochondria and provide promising therapeutic targets.     

Subunit architecture of multimeric complexes isolated directly from cells

Recent developments in purification strategies, together with mass spectrometry (MS)-based proteomics, have identified numerous in vivo protein complexes and suggest the existence of many others. Standard proteomics techniques are, however, unable to describe the overall stoichiometry, subunit interactions and organization of these assemblies, because many are heterogeneous, are present at relatively low cellular abundance and are frequently difficult to isolate. We combine two existing methodologies to tackle these challenges: tandem affinity purification to isolate sufficient quantities of highly pure native complexes, and MS of the intact assemblies and subcomplexes to determine their structural organization. We optimized our protocol with two protein assemblies from Saccharomyces cerevisiae (scavenger decapping and nuclear cap-binding complexes), establishing subunit stoichiometry and identifying substoichiometric binding. We then targeted the yeast exosome, a nuclease with ten different subunits, and found that by generating subcomplexes, a three-dimensional interaction map could be derived, demonstrating the utility of our approach for large, heterogeneous cellular complexes.     

Analysis of interaction partners of H4 histone by a new proteomics approach

We describe a modification of the tandem affinity purification method for purification and analysis of multiprotein complexes, termed here DEF‐TAP (for differential elution fractionation after tandem affinity purification). Its essential new feature is the use for last purification step of 6×His‐Ni⁺⁺ interaction, which is resistant to a variety of harsh washing conditions, including high ionic strength and the presence of organic solvents. This allows us to use various fractionation schemes before the protease digestion, which is expected to improve the coverage of the analyzed protein mixture and also to provide an additional insight into the structure of the purified macromolecular complex and the nature of protein–protein interactions involved. We illustrate our new approach by analysis of soluble nuclear complexes containing histone H4 purified from HeLa cells. In particular, we observed different fractionation patterns of HAT1 and RbAp46 proteins as compared with RbAp48 protein, all identified as interaction partners of H4 histone. In addition, we report all components of the licensing MCM2‐7 complex and the apoptosis‐related DAXX protein among the interaction partners of the soluble H4. Finally, we show that HAT1 requires N‐terminal tail of H4 for its stable association with this histone.     

Plant proteome analysis: a 2006 update

This 2006 'Plant Proteomics Update' is a continuation of the two previously published in 'Proteomics' by 2004 (Canovas et al., Proteomics 2004, 4, 285-298) and 2006 (Rossignol et al., Proteomics 2006, 6, 5529-5548) and it aims to bring up-to-date the contribution of proteomics to plant biology on the basis of the original research papers published throughout 2006, with references to those appearing last year. According to the published papers and topics addressed, we can conclude that, as observed for the three previous years, there has been a quantitative, but not qualitative leap in plant proteomics. The full potential of proteomics is far from being exploited in plant biology research, especially if compared to other organisms, mainly yeast and humans, and a number of challenges, mainly technological, remain to be tackled. The original papers published last year numbered nearly 100 and deal with the proteome of at least 26 plant species, with a high percentage for Arabidopsis thaliana (28) and rice (11). Scientific objectives ranged from proteomic analysis of organs/tissues/cell suspensions (57) or subcellular fractions (29), to the study of plant development (12), the effect of hormones and signalling molecules (8) and response to symbionts (4) and stresses (27). A small number of contributions have covered PTMs (8) and protein interactions (4). 2-DE (specifically IEF-SDS-PAGE) coupled to MS still constitutes the almost unique platform utilized in plant proteome analysis. The application of gel-free protein separation methods and 'second generation' proteomic techniques such as multidimensional protein identification technology (MudPIT), and those for quantitative proteomics including DIGE, isotope-coded affinity tags (ICAT), iTRAQ and stable isotope labelling by amino acids in cell culture (SILAC) still remains anecdotal. This review is divided into seven sections: Introduction, Methodology, Subcellular proteomes, Development, Responses to biotic and abiotic stresses, PTMs and Protein interactions. Section 8 summarizes the major pitfalls and challenges of plant proteomics.     

Computational detection of protein complexes in AP-MS experiments

Protein complex identification is an important goal of protein-protein interaction analysis. To date, development of computational methods for detecting protein complexes has been largely motivated by genome-scale interaction data sets from high-throughput assays such as yeast two-hybrid or tandem affinity purification coupled with mass spectrometry (TAP-MS). However, due to the popularity of small to intermediate-scale affinity purification-mass spectrometry (AP-MS) experiments, protein complex detection is increasingly discussed in local network analysis. In such data sets, protein complexes cannot be detected using binary interaction data alone because the data contain interactions with tagged proteins only and, as a result, interactions between all other proteins remain unobserved, limiting the scope of existing algorithms. In this article, we provide a pragmatic review of network graph-based computational algorithms for protein complex analysis in global interactome data, without requiring any computational background. We discuss the practical gap in applying these algorithms to recently surging small to intermediate-scale AP-MS data sets, and review alternative clustering algorithms using quantitative proteomics data and their limitations.