Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics. Proc Natl Acad Sci U S A. 2008;105:1454-1459. [PMID: 18218781 DOI: 10.1073/pnas.0706983105]

High-throughput analysis of peptide-binding modules

Modular protein interaction domains (PIDs) that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some PIDs such as SH2, 14-3-3, Chromo, and Bromo domains serve to recognize posttranslational modification (PTM) of amino acids (such as phosphorylation, acetylation, methylation, etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PSD-95/Discs-large/ZO-1 (PDZ) domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate-specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High-throughput (HTP) analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry, or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls associated with HTP analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of PIDs and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions.

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.

Affinity-purification coupled to mass spectrometry: basic principles and strategies

Identifying the interactions established by a protein of interest can be a critical step in understanding its function. This is especially true when an unknown protein of interest is demonstrated to physically interact with proteins of known function. While many techniques have been developed to characterize protein-protein interactions, one strategy that has gained considerable momentum over the past decade for identification and quantification of protein-protein interactions, is affinity-purification followed by mass spectrometry (AP-MS). Here, we briefly review the basic principles used in affinity-purification coupled to mass spectrometry, with an emphasis on tools (both biochemical and computational), which enable the discovery and reporting of high quality protein-protein interactions.

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.

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

Targeted proteomics of the secretory pathway reveals the secretome of mouse embryonic fibroblasts and human embryonic stem cells

Proteins endogenously secreted by human embryonic stem cells (hESCs) and those present in hESC culture medium are critical regulators of hESC self-renewal and differentiation. Current MS-based approaches for identifying secreted proteins rely predominantly on MS analysis of cell culture supernatants. Here we show that targeted proteomics of secretory pathway organelles is a powerful alternate approach for interrogating the cellular secretome. We have developed procedures to obtain subcellular fractions from mouse embryonic fibroblasts (MEFs) and hESCs that are enriched in secretory pathway organelles while ensuring retention of the secretory cargo. MS analysis of these fractions from hESCs cultured in MEF conditioned medium (MEF-CM) or MEFs exposed to hESC medium revealed 99 and 129 proteins putatively secreted by hESCs and MEFs, respectively. Of these, 53 and 62 proteins have been previously identified in cell culture supernatants of MEFs and hESCs, respectively, thus establishing the validity of our approach. Furthermore, 76 and 37 putatively secreted proteins identified in this study in MEFs and hESCs, respectively, have not been reported in previous MS analyses. The identification of low abundance secreted proteins via MS analysis of cell culture supernatants typically necessitates the use of altered culture conditions such as serum-free medium. However, an altered medium formulation might directly influence the cellular secretome. Indeed, we observed significant differences between the abundances of several secreted proteins in subcellular fractions isolated from hESCs cultured in MEF-CM and those exposed to unconditioned hESC medium for 24 h. In contrast, targeted proteomics of secretory pathway organelles does not require the use of customized media. We expect that our approach will be particularly valuable in two contexts highly relevant to hESC biology: obtaining a temporal snapshot of proteins secreted in response to a differentiation trigger, and identifying proteins secreted by cells that are isolated from a heterogeneous population.

Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific "bait" protein and its associated "prey" proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.

Mass spectrometry-based proteomics and network biology

In the life sciences, a new paradigm is emerging that places networks of interacting molecules between genotype and phenotype. These networks are dynamically modulated by a multitude of factors, and the properties emerging from the network as a whole determine observable phenotypes. This paradigm is usually referred to as systems biology, network biology, or integrative biology. Mass spectrometry (MS)-based proteomics is a central life science technology that has realized great progress toward the identification, quantification, and characterization of the proteins that constitute a proteome. Here, we review how MS-based proteomics has been applied to network biology to identify the nodes and edges of biological networks, to detect and quantify perturbation-induced network changes, and to correlate dynamic network rewiring with the cellular phenotype. We discuss future directions for MS-based proteomics within the network biology paradigm.

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.

ROCS: a reproducibility index and confidence score for interaction proteomics studies

Background

Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.

Methods

To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.

Results

We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.

Conclusions

Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.

Proteomic analysis of mitotic RNA polymerase II reveals novel interactors and association with proteins dysfunctional in disease

RNA polymerase II (RNAPII) transcribes protein-coding genes in eukaryotes and interacts with factors involved in chromatin remodeling, transcriptional activation, elongation, and RNA processing. Here, we present the isolation of native RNAPII complexes using mild extraction conditions and immunoaffinity purification. RNAPII complexes were extracted from mitotic cells, where they exist dissociated from chromatin. The proteomic content of native complexes in total and size-fractionated extracts was determined using highly sensitive LC-MS/MS. Protein associations with RNAPII were validated by high-resolution immunolocalization experiments in both mitotic cells and in interphase nuclei. Functional assays of transcriptional activity were performed after siRNA-mediated knockdown. We identify >400 RNAPII associated proteins in mitosis, among these previously uncharacterized proteins for which we show roles in transcriptional elongation. We also identify, as novel functional RNAPII interactors, two proteins involved in human disease, ALMS1 and TFG, emphasizing the importance of gene regulation for normal development and physiology.

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.

Mapping the protein interaction network of the human COP9 signalosome complex using a label-free QTAX strategy

The COP9 signalosome (CSN) is a multi-subunit protein complex that performs critical roles in controlling diverse cellular and developmental processes. Aberrant regulation of the CSN complex has been shown to lead to tumorigenesis. Despite its biological significance, our current knowledge of the function and regulation of the CSN complex is very limited. To explore CSN biology, we have developed and employed a new version of the tag team-based QTAX strategy (quantitative analysis of tandem affinity purified in vivo cross-linked (X) protein complexes) by incorporating a label-free MS method for quantitation. Coupled with protein interaction network analysis, this strategy produced a comprehensive and detailed assessment of the protein interaction network of the human CSN complex. In total, we quantitatively characterized 825 putative CSN-interacting proteins, with 270 classified as core interactors (captured by all three bait purifications). Biochemical validation further confirms the validity of selected identified interactors. This work presents the most complete analysis of the CSN interaction network to date, providing an inclusive set of physical interaction data consistent with physiological roles for the CSN. Moreover, the methodology described here is a general proteomic tool for the comprehensive study of protein interaction networks.

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.

Rab GTPase-activating proteins in autophagy: regulation of endocytic and autophagy pathways by direct binding to human ATG8 modifiers

Autophagy is an evolutionarily conserved degradation pathway characterized by dynamic rearrangement of membranes that sequester cytoplasm, protein aggregates, organelles, and pathogens for delivery to the vacuole and lysosome, respectively. The ability of autophagosomal membranes to act selectively toward specific cargo is dependent on the small ubiquitin-like modifier ATG8/LC3 and the LC3-interacting region (LIR) present in autophagy receptors. Here, we describe a comprehensive protein-protein interaction analysis of TBC (Tre2, Bub2, and Cdc16) domain-containing Rab GTPase-activating proteins (GAPs) as potential autophagy adaptors. We identified 14 TBC domain-containing Rab GAPs that bind directly to ATG8 modifiers and that colocalize with LC3-positive autophagy membranes in cells. Intriguingly, one of our screening hits, TBC1D5, contains two LIR motifs. The N-terminal LIR was critical for interaction with the retromer complex and transport of cargo. Direct binding of the retromer component VPS29 to TBC1D5 could be titrated out by LC3, indicating a molecular switch between endosomes and autophagy. Moreover, TBC1D5 could bridge the endosome and autophagosome via its C-terminal LIR motif. During starvation-induced autophagy, TBC1D5 was relocalized from endosomal localization to the LC3-positive autophagosomes. We propose that LC3-interacting Rab GAPs are implicated in the reprogramming of the endocytic trafficking events under starvation-induced autophagy.

HSP90 and the R2TP co-chaperone complex: building multi-protein machineries essential for cell growth and gene expression

HSP90 (Heat Shock Protein 90) is an essential chaperone involved in the last folding steps of client proteins. It has many clients, and these are often recognized through specific adaptors. Recently, the conserved R2TP complex was identified as a key HSP90 co-chaperone. Current evidences indicate that the HSP90/R2TP system assembles multi-molecular protein complexes. Strikingly, these comprise basic machineries of gene expression: (1) nuclear RNA polymerases; (2) the snoRNPs, essential to produce ribosomes; and (3) mTOR Complex 1 and 2, which control translational activity and cell growth. Another important substrate is the telomerase RNP, required for continuous cell proliferation. We discuss here the assembly of RNA polymerases in bacteria and eukaryotes, the role of HSP90/R2TP in this process and in the assembly of snoRNPs and the PIKK family of TORC1 kinase. Finally, we speculate on the roles of R2TP as a master regulator of cell growth under normal or pathological conditions.

Biogenesis of multisubunit RNA polymerases

Gene transcription in the nucleus of eukaryotic cells is carried out by three related multisubunit RNA polymerases, Pol I, Pol II and Pol III. Although the structure and function of the polymerases have been studied extensively, little is known about their biogenesis and their transport from the cytoplasm (where the subunits are synthesized) to the nucleus. Recent studies have revealed polymerase assembly intermediates and putative assembly factors, as well as factors required for Pol II nuclear import. In this review, we integrate the available data into a model of Pol II biogenesis that provides a framework for future analysis of the biogenesis of all RNA polymerases.

Examining the complexity of human RNA polymerase complexes using HaloTag technology coupled to label free quantitative proteomics

Efficient determination of protein interactions and cellular localization remains a challenge in higher order eukaryotes and creates a need for robust technologies for functional proteomics studies. To address this, the HaloTag technology was developed for highly efficient and rapid isolation of intracellular complexes and correlative in vivo cellular imaging. Here we demonstrate the strength of this technology by simultaneous capture of human eukaryotic RNA polymerases (RNAP) I, II, and III using a shared subunit, POLR2H, fused to the HaloTag. Affinity purifications showed successful isolation, as determined using quantitative proteomics, of all RNAP core subunits, even at expression levels near endogenous. Transient known RNAP II interacting partners were identified as well as three previously uncharacterized interactors. These interactions were validated and further functionally characterized using cellular imaging. The multiple capabilities of the HaloTag technology demonstrate the ability to efficiently isolate highly challenging multiprotein complexes, discover new interactions, and characterize cellular localization.

Dual modification of Alzheimer's disease PHF-tau protein by lysine methylation and ubiquitylation: a mass spectrometry approach

In sporadic Alzheimer’s disease (AD), neurofibrillary lesion formation is preceded by extensive post-translational modification of the microtubule associated protein tau. To identify the modification signature associated with tau lesion formation at single amino acid resolution, immunopurified paired helical filaments were isolated from AD brain and subjected to nanoflow liquid chromatography–tandem mass spectrometry analysis. The resulting spectra identified monomethylation of lysine residues as a new tau modification. The methyl-lysine was distributed among seven residues located in the projection and microtubule binding repeat regions of tau protein, with one site, K254, being a substrate for a competing lysine modification, ubiquitylation. To characterize methyl lysine content in intact tissue, hippocampal sections prepared from post mortem late-stage AD cases were subjected to double-label confocal fluorescence microscopy using anti-tau and anti-methyl lysine antibodies. Anti-methyl lysine immunoreactivity colocalized with 78 ± 13% of neurofibrillary tangles in these specimens. Together these data provide the first evidence that tau in neurofibrillary lesions is post-translationally modified by lysine methylation.

Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.

Characterization of a highly conserved histone related protein, Ydl156w, and its functional associations using quantitative proteomic analyses

A significant challenge in biology is to functionally annotate novel and uncharacterized proteins. Several approaches are available for deducing the function of proteins in silico based upon sequence homology and physical or genetic interaction, yet this approach is limited to proteins with well-characterized domains, paralogs and/or orthologs in other species, as well as on the availability of suitable large-scale data sets. Here, we present a quantitative proteomics approach extending the protein network of core histones H2A, H2B, H3, and H4 in Saccharomyces cerevisiae, among which a novel associated protein, the previously uncharacterized Ydl156w, was identified. In order to predict the role of Ydl156w, we designed and applied integrative bioinformatics, quantitative proteomics and biochemistry approaches aiming to infer its function. Reciprocal analysis of Ydl156w protein interactions demonstrated a strong association with all four histones and also to proteins strongly associated with histones including Rim1, Rfa2 and 3, Yku70, and Yku80. Through a subsequent combination of the focused quantitative proteomics experiments with available large-scale genetic interaction data and Gene Ontology functional associations, we provided sufficient evidence to associate Ydl156w with multiple processes including chromatin remodeling, transcription and DNA repair/replication. To gain deeper insights into the role of Ydl156w in histone biology we investigated the effect of the genetic deletion of ydl156w on H4 associated proteins, which lead to a dramatic decrease in the association of H4 with RNA polymerase III proteins. The implication of a role for Ydl156w in RNA Polymerase III mediated transcription was consequently verified by RNA-Seq experiments. Finally, using these approaches we generated a refined network of Ydl156w-associated proteins.

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.

Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows

Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-to-day variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS- PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs.

Comparison of stable-isotope labeling with amino acids in cell culture and spectral counting for relative quantification of protein expression

Protein quantification is one of the principal goals of mass spectrometry (MS)-based proteomics, and many strategies exist to achieve it. Several approaches involve the incorporation of a stable-isotope label using either chemical derivatization, enzymatically catalyzed incorporation of (18)O, or metabolic labeling in a cell or tissue culture. These techniques can be cost or time prohibitive or not amenable to the biological system of interest. Label-free techniques including those utilizing integrated ion abundance and spectral counting offer an alternative to stable-isotope-based methodologies. Herein, we present the comparison of stable-isotope labeling of amino acids in cell culture (SILAC) with spectral counting for the quantification of human embryonic stem cells as they differentiate toward the trophectoderm at three time points. Our spectral counting experimental strategy resulted in the identification of 2641 protein groups across three time points with an average sequence coverage of 30.3%, of which 1837 could be quantified with more than five spectral counts. SILAC quantification was able to identify 1369 protein groups with an average coverage of 24.7%, of which 1027 could be quantified across all time points. Within this context we further explore the capacity of each strategy for proteome coverage, variation in quantification, and the relative sensitivity of each technique to the detection of change in relative protein expression.

Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes

Despite the availability of several large-scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild-type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well-studied complexes, Spt-Ada-Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high-resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants.

A cost-benefit analysis of multidimensional fractionation of affinity purification-mass spectrometry samples

Affinity purification coupled to mass spectrometry (AP-MS) is gaining widespread use for the identification of protein-protein interactions. It is unclear, however, whether typical AP sample complexity is limiting for the identification of all protein components using standard one-dimensional LC-MS/MS. Multidimensional sample separation is useful for reducing sample complexity prior to MS analysis and increases peptide and protein coverage of complex samples. Here, we monitored the effects of upstream protein or peptide separation techniques on typical mammalian AP-MS samples, generated by FLAG affinity purification of four baits with different biological functions and/or subcellular distribution. As a first separation step, we employed SDS-PAGE, strong cation exchange LC, or reversed-phase LC at basic pH. We also analyzed the benefits of using an instrument with a faster scan rate, the new TripleTOF 5600 mass spectrometer. While all multidimensional approaches yielded a clear increase in spectral counts, the increase in unique peptides and additional protein identification was modest and came at the cost of increased instrument and handling time. The use of a high duty-cycle instrument achieved similar benefits without these drawbacks. An increase in spectral counts is beneficial when data analysis methods relying on spectral counts, including Significance Analysis of INTeractome (SAINT), are used.

Building protein-protein interaction networks with proteomics and informatics tools

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.

Loss of the chromatin regulator MRG15 limits neural stem/progenitor cell proliferation via increased expression of the p21 Cdk inhibitor

Chromatin regulation is crucial for many biological processes such as transcriptional regulation, DNA replication, and DNA damage repair. We have found that it is also important for neural stem/progenitor cell (NSC) function and neurogenesis. Here, we demonstrate that expression of the cyclin-dependent kinase inhibitor p21 is specifically up-regulated in Mrg15 deficient NSCs. Knockdown of p21 expression by p21 shRNA results in restoration of cell proliferation. This indicates that p21 is directly involved in the growth defects observed in Mrg15 deficient NSCs. Activated p53 accumulates in Mrg15 deficient NSCs and this most likely accounts for the up-regulation of p21 expression in the cells. We observed decreased p53 and p21 levels and a concomitant increase in the percentage of BrdU positive cells in Mrg15 null cultures following expression of p53 shRNA. DNA damage foci, as indicated by immunostaining for γH2AX and 53BP1, are detectable in a sub-population of Mrg15 deficient NSC cultures under normal growing conditions and the majority of p21-positive cells are also positive for 53BP1 foci. Furthermore, Mrg15 deficient NSCs exhibit severe defects in DNA damage response following ionizing radiation. Our observations highlight the importance of chromatin regulation and DNA damage response in NSC function and maintenance.

Defining the budding yeast chromatin-associated interactome

We previously reported a novel affinity purification (AP) method termed modified chromatin immunopurification (mChIP), which permits selective enrichment of DNA-bound proteins along with their associated protein network. In this study, we report a large-scale study of the protein network of 102 chromatin-related proteins from budding yeast that were analyzed by mChIP coupled to mass spectrometry. This effort resulted in the detection of 2966 high confidence protein associations with 724 distinct preys. mChIP resulted in significantly improved interaction coverage as compared with classical AP methodology for ∼75% of the baits tested. Furthermore, mChIP successfully identified novel binding partners for many lower abundance transcription factors that previously failed using conventional AP methodologies. mChIP was also used to perform targeted studies, particularly of Asf1 and its associated proteins, to allow for a understanding of the physical interplay between Asf1 and two other histone chaperones, Rtt106 and the HIR complex, to be gained.

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.

PDRG1, a novel tumor marker for multiple malignancies that is selectively regulated by genotoxic stress

We have previously cloned and characterized a novel p53 and DNA damage-regulated gene named PDRG1. PDRG1 was found to be differentially regulated by ultraviolet (UV) radiation and p53. In this study, we further investigated stress regulation of PDRG1 and found it to be selectively regulated by agents that induce genotoxic stress (DNA damage). Using cancer profiling arrays, we also investigated PDRG1 expression in matching normal and tumor samples representing various malignancies and found its expression to be upregulated in multiple malignancies including cancers of the colon, rectum, ovary, lung, stomach, breast and uterus when compared to their respective matched normal tissues. Western blot and immunohistochemical analyses were also performed on select specimen sets of colon cancers and matching normal tissues and the results also indicated PDRG1 overexpression in tumors relative to normal tissues. To gain insight into the function of PDRG1, we performed PDRG1 knockdown in human colon cancer cells and found its depletion to result in marked slowdown of tumor cell growth. These results suggest that PDGR1 may be linked to cell growth regulation. Yeast two-hybrid screen also led to the identification of PDCD7, CIZ1 and MAP1S as PDRG1-interacting proteins that are involved in apoptosis and cell cycle regulation which further implicate PDRG1 in controlling cell growth regulation. Taken together, our results indicate that PDRG1 expression is increased in multiple human malignancies suggesting it to be a high-value novel tumor marker that could play a role in cancer development and/or progression.

Label-free proteomic identification of endogenous, insulin-stimulated interaction partners of insulin receptor substrate-1

Protein-protein interactions are key to most cellular processes. Tandem mass spectrometry (MS/MS)-based proteomics combined with co-immunoprecipitation (CO-IP) has emerged as a powerful approach for studying protein complexes. However, a majority of systematic proteomics studies on protein-protein interactions involve the use of protein overexpression and/or epitope-tagged bait proteins, which might affect binding stoichiometry and lead to higher false positives. Here, we report an application of a straightforward, label-free CO-IP-MS/MS method, without the use of protein overexpression or protein tags, to the investigation of changes in the abundance of endogenous proteins associated with a bait protein, which is in this case insulin receptor substrate-1 (IRS-1), under basal and insulin stimulated conditions. IRS-1 plays a central role in the insulin signaling cascade. Defects in the protein-protein interactions involving IRS-1 may lead to the development of insulin resistance and type 2 diabetes. HPLC-ESI-MS/MS analyses identified eleven novel endogenous insulin-stimulated IRS-1 interaction partners in L6 myotubes reproducibly, including proteins play an important role in protein dephosphorylation [protein phosphatase 1 regulatory subunit 12A, (PPP1R12A)], muscle contraction and actin cytoskeleton rearrangement, endoplasmic reticulum stress, and protein folding, as well as protein synthesis. This novel application of label-free CO-IP-MS/MS quantification to assess endogenous interaction partners of a specific protein will prove useful for understanding how various cell stimuli regulate insulin signal transduction.

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks.

Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5

Affinity purification coupled to mass spectrometry (AP-MS) represents a powerful and proven approach for the analysis of protein-protein interactions. However, the detection of true interactions for proteins that are commonly considered background contaminants is currently a limitation of AP-MS. Here using spectral counts and the new statistical tool, Significance Analysis of INTeractome (SAINT), true interaction between the serine/threonine protein phosphatase 5 (PP5) and a chaperonin, heat shock protein 90 (Hsp90), is discerned. Furthermore, we report and validate a new interaction between PP5 and an Hsp90 adaptor protein, stress-induced phosphoprotein 1 (STIP1; HOP). Mutation of PP5, replacing key basic amino acids (K97A and R101A) in the tetratricopeptide repeat (TPR) region known to be necessary for the interactions with Hsp90, abolished both the known interaction of PP5 with cell division cycle 37 homolog and the novel interaction of PP5 with stress-induced phosphoprotein 1. Taken together, the results presented demonstrate the usefulness of label-free quantitative proteomics and statistical tools to discriminate between noise and true interactions, even for proteins normally considered as background contaminants.

Less label, more free: approaches in label-free quantitative mass spectrometry

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.

Construction of protein interaction networks based on the label-free quantitative proteomics

Multiprotein complexes are essential building blocks for many cellular processes in an organism. Taking the process of transcription as an example, the interplay of several chromatin-remodeling complexes is responsible for the tight regulation of gene expression. Knowing how those proteins associate into protein complexes not only helps to improve our understanding of these cellular processes, but can also lead to the discovery of the function of novel interacting proteins. Given the large number of proteins with little to no functional annotation throughout many organisms, including human, the identification and characterization of protein complexes has grown into a major focus of network biology. Toward this goal, we have developed several computational approaches based upon label-free quantitative proteomics approaches for the analysis of protein complexes and protein interaction networks. Here, we describe the computational approaches used to build probabilistic protein interaction networks, which are detailed in this chapter using the example of complexes involved in chromatin remodeling and transcription.

SAINT: probabilistic scoring of affinity purification-mass spectrometry data

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification-mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

Modeling contaminants in AP-MS/MS experiments

Identification of protein-protein interactions (PPI) by affinity purification (AP) coupled with tandem mass spectrometry (AP-MS/MS) produces large data sets with high rates of false positives. This is in part because of contamination at the AP level (due to gel contamination, nonspecific binding to the TAP columns in the context of tandem affinity purification, insufficient purification, etc.). In this paper, we introduce a Bayesian approach to identify false-positive PPIs involving contaminants in AP-MS/MS experiments. Specifically, we propose a confidence assessment algorithm (called Decontaminator) that builds a model of contaminants using a small number of representative control experiments. It then uses this model to determine whether the Mascot score of a putative prey is significantly larger than what was observed in control experiments and assigns it a p-value and a false discovery rate. We show that our method identifies contaminants better than previously used approaches and results in a set of PPIs with a larger overlap with databases of known PPIs. Our approach will thus allow improved accuracy in PPI identification while reducing the number of control experiments required.

Physical and functional HAT/HDAC interplay regulates protein acetylation balance

The balance between protein acetylation and deacetylation controls several physiological and pathological cellular processes, and the enzymes involved in the maintenance of this equilibrium—acetyltransferases (HATs) and deacetylases (HDACs)—have been widely studied. Presently, the evidences obtained in this field suggest that the dynamic acetylation equilibrium is mostly maintained through the physical and functional interplay between HAT and HDAC activities. This model overcomes the classical vision in which the epigenetic marks of acetylation have only an activating function whereas deacetylation marks have a repressing activity. Given the existence of several players involved in the preservation of this equilibrium, the identification of these complex networks of interacting proteins will likely foster our understanding of how cells regulate intracellular processes and respond to the extracellular environment and will offer the rationale for new therapeutic approaches based on epigenetic drugs in human diseases.

Comparison of strong cation exchange and SDS-PAGE fractionation for analysis of multiprotein complexes

Affinity purification of protein complexes followed by identification using liquid chromatography/mass spectrometry (LC-MS/MS) is a robust method to study the fundamental process of protein interaction. Although affinity isolation reduces the complexity of the sample, fractionation prior to LC-MS/MS analysis is still necessary to maximize protein coverage. In this study, we compared the protein coverage obtained via LC-MS/MS analysis of protein complexes prefractionated using two commonly employed methods, SDS-PAGE and strong cation exchange chromatography (SCX). The two complexes analyzed focused on the nuclear proteins Bmi-1 and GATA3 that were expressed within the cells at low and high levels, respectively. Prefractionation of the complexes at the peptide level using SCX consistently resulted in the identification of approximately 3-fold more proteins compared to separation at the protein level using SDS-PAGE. The increase in the number of identified proteins was especially pronounced for the Bmi-1 complex, where the target protein was expressed at a low level. The data show that prefractionation of affinity isolated protein complexes using SCX prior to LC-MS/MS analysis significantly increases the number of identified proteins and individual protein coverage, particularly for target proteins expressed at low levels.