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.     

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.     

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.     

Endogenous epitope tagging of heat shock protein 70 isoform Hsc70 using CRISPR/Cas9

Heat shock protein 70 (Hsp70) is an evolutionarily well-conserved molecular chaperone involved in several cellular processes such as folding of proteins, modulating protein-protein interactions, and transport of proteins across the membrane. Binding partners of Hsp70 (known as “clients”) are identified on an individual basis as researchers discover their particular protein of interest binds to Hsp70. A full complement of Hsp70 interactors under multiple stress conditions remains to be determined. A promising approach to characterizing the Hsp70 “interactome” is the use of protein epitope tagging and then affinity purification followed by mass spectrometry (AP-MS/MS). AP-MS analysis is a widely used method to decipher protein-protein interaction networks and identifying protein functions. Conventionally, the proteins are overexpressed ectopically which interferes with protein complex stoichiometry, skewing AP-MS/MS data. In an attempt to solve this issue, we used CRISPR/Cas9-mediated gene editing to integrate a tandem-affinity (TAP) epitope tag into the genomic locus of HSC70. This system offers several benefits over existing expression systems including native expression, no requirement for selection, and homogeneity between cells. This cell line, freely available to chaperone researchers, will aid in small and large-scale protein interaction studies as well as the study of biochemical activities and structure-function relationships of the Hsc70 protein.     

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.     

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.     

Purification and characterization of HIV–human protein complexes

To fully understand how pathogens infect their host and hijack key biological processes, systematic mapping of intra-pathogenic and pathogen–host protein–protein interactions (PPIs) is crucial. Due to the relatively small size of viral genomes (usually around 10–100 proteins), generation of comprehensive host–virus PPI maps using different experimental platforms, including affinity tag purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) approaches, can be achieved. Global maps such as these provide unbiased insight into the molecular mechanisms of viral entry, replication and assembly. However, to date, only two-hybrid methodology has been used in a systematic fashion to characterize viral–host protein–protein interactions, although a deluge of data exists in databases that manually curate from the literature individual host–pathogen PPIs. We will summarize this work and also describe an AP-MS platform that can be used to characterize viral-human protein complexes and discuss its application for the HIV genome.     

Local modeling of global interactome networks

MOTIVATION: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity-purification/mass-spectrometry (AP-MS) technologies measure different protein-protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput 'interactome' data collection. Static graphs currently used to model Y2H and AP-MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function. RESULTS: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available datasets and demonstrate its uses, interpretation and limitations. Specifically, we use this technology to address four major issues pertaining to protein-protein networks. (1) We motivate the need to move from static global interactome graphs to local protein complex models. (2) We formally show that accurate local interactome models require both Y2H and AP-MS data, even in idealized situations. (3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. (4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity and coordination with gene-expression data. AVAILABILITY: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org CONTACT: dscholtens@northwestern.edu SUPPLEMENTARY INFORMATION: http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling     

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.     

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.     

Analysis of protein complexes using mass spectrometry

The versatile combination of affinity purification and mass spectrometry (AP-MS) has recently been applied to the detailed characterization of many protein complexes and large protein-interaction networks. The combination of AP-MS with other techniques, such as biochemical fractionation, intact mass measurement and chemical crosslinking, can help to decipher the supramolecular organization of protein complexes. AP-MS can also be combined with quantitative proteomics approaches to better understand the dynamics of protein-complex assembly.     

Deconvolution of targeted protein-protein interaction maps

Current proteomic techniques allow researchers to analyze chosen biological pathways or an ensemble of related protein complexes at a global level via the measure of physical protein-protein interactions by affinity purification mass spectrometry (AP-MS). Such experiments yield information-rich but complex interaction maps whose unbiased interpretation is challenging. Guided by current knowledge on the modular structure of protein complexes, we propose a novel statistical approach, named BI-MAP, complemented by software tools and a visual grammar to present the inferred modules. We show that the BI-MAP tools can be applied from small and very detailed maps to large, sparse, and much noisier data sets. The BI-MAP tool implementation and test data are made freely available.     

ROCS: A reproducibility index and confidence score for interaction proteomics

ABSTRACT: BACKGROUND: Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifyingprotein complexes and interactions. Several important challenges exist in interpreting theresults of AP-MS experiments. First, the reproducibility of AP-MS experimental replicatescan be low, due both to technical variability and the dynamic nature of protein interactions inthe cell. Second, the identification of true protein-protein interactions in AP-MS experimentsis subject to inaccuracy due to high false negative and false positive rates. Severalexperimental approaches can be used to mitigate these drawbacks, including the use ofreplicated and control experiments and relative quantification to sensitively distinguish trueinteracting proteins from false ones. RESULTS: To address the issues of reproducibility and accuracy of protein-protein interactions, weintroduce a two-step method, called ROCS, which makes use of Indicator Proteins to selectreproducible AP-MS experiments, and of Confidence Scores to select specific protein-proteininteractions. The Indicator Proteins account for measures of protein identification as well asprotein reproducibility, effectively allowing removal of outlier experiments that contributenoise and affect downstream inferences. The filtered set of experiments is then used in theProtein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing aConfidence Score, which accounts for the probability of occurrence of prey proteins in thebait experiments relative to the control experiment, where the significance cutoff parameter isestimated by simultaneously controlling false positives and false negatives against metrics offalse discovery rate and biological coherence respectively. In summary, the ROCS methodrelies on automatic objective criterions for parameter estimation and error-controlledprocedures. 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 onits own to make accurate identification of specific, biologically relevant protein-proteininteractions or in combination with other AP-MS scoring methods to significantly improveinferences. CONCLUSIONS: Our method addresses important issues encountered in AP-MS datasets, making ROCS a verypromising tool for this purpose, either on its own or especially in conjunction with othermethods. We anticipate that our methodology may be used more generally in proteomicsstudies and databases, where experimental reproducibility issues arise. The method isimplemented in the R language, and is available as an R package called "ROCS", freelyavailable from the CRAN repository http://cran.r-project.org/.     

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.     

CIG-P: Circular Interaction Graph for Proteomics

Background

A typical affinity purification coupled to mass spectrometry (AP-MS) experiment includes the purification of a target protein (bait) using an antibody and subsequent mass spectrometry analysis of all proteins co-purifying with the bait (aka prey proteins). Like any other systems biology approach, AP-MS experiments generate a lot of data and visualization has been challenging, especially when integrating AP-MS experiments with orthogonal datasets.

Results

We present Circular Interaction Graph for Proteomics (CIG-P), which generates circular diagrams for visually appealing final representation of AP-MS data. Through a Java based GUI, the user inputs experimental and reference data as file in csv format. The resulting circular representation can be manipulated live within the GUI before exporting the diagram as vector graphic in pdf format. The strength of CIG-P is the ability to integrate orthogonal datasets with each other, e.g. affinity purification data of kinase PRPF4B in relation to the functional components of the spliceosome. Further, various AP-MS experiments can be compared to each other.

Conclusions

CIG-P aids to present AP-MS data to a wider audience and we envision that the tool finds other applications too, e.g. kinase – substrate relationships as a function of perturbation. CIG-P is available under: http://sourceforge.net/projects/cig-p/

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-344) contains supplementary material, which is available to authorized users.     

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.     

Distinctive Network Topology of Phase-Separated Proteins in Human Interactome

Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein–protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.     

Toward optimal clearance: A universal affinity-based mass spectrometry approach for comprehensive ELISA reagent coverage evaluation and HCP hitchhiker analysis

In the control strategy for process related impurities in biopharmaceuticals, the enzyme linked immunosorbent assay (ELISA) is the method of choice for the quantification of host cell proteins (HCPs). Besides two dimensional-western blots (2D-WB), the coverage of ELISA antibodies is increasingly evaluated by affinity purification-based liquid chromatography–tandem mass spectrometry (AP-MS) methods. However, all these methods face the problem of unspecific binding issues between antibodies and the matrix, involving the application of arbitrarily defined thresholds during data evaluation. To solve this, a new approach (optimized AP-MS) was developed in this study, for which a cleavable linker was conjugated to the ELISA antibodies enabling the subsequent isolation of specifically interacting HCPs. By comparing both approaches in terms of method variability and the number of false positive or negative hits, we could demonstrate that the optimized AP-MS method is very reproducible and superior in the identification of antibody detection gaps, while previously described strategies suffered from over- or underestimating the coverage. As only antibody associated HCPs were identified, we demonstrated that the method is beneficial for hitchhiker analysis. Overall, the method described herein has proven as a powerful tool for reliable coverage determination of ELISA antibodies, without the need to arbitrarily exclude HCPs during the coverage evaluation.     

A versatile lentiviral expression system to identify mammalian protein-protein interactions

Protein-protein interactions (PPIs) are central to our understanding of protein function, biological processes and signaling pathways. Affinity purification coupled with mass spectrometry (AP-MS) is a powerful approach for detecting PPIs and protein complexes and relies on the purification of bait proteins using bait-specific binding reagents. These binding reagents may recognize bait proteins directly or affinity tags that are fused to bait proteins. A limitation of the latter approach is that expression of affinity tagged baits is largely constrained to engineered or unnatural cell lines, which results in the AP-MS identification of PPIs that may not accurately reflect those seen in nature. Therefore, generating cell lines stably expressing affinity tagged bait proteins in a broad range of cell types and cell lines is important for identifying PPIs that are dependent on different contexts. To facilitate the identification of PPIs across many mammalian cell types, we developed the mammalian affinity purification and lentiviral expression (MAPLE) system. MAPLE uses recombinant lentiviral technology to stably and efficiently express affinity tagged complementary DNA (cDNA) in mammalian cells, including cells that are difficult to transfect and non-dividing cells. The MAPLE vectors contain a versatile affinity (VA) tag for multi-step protein purification schemes and subcellular localization studies. In this methods article, we present a step-by-step overview of the MAPLE system workflow.