Affinity Purification-mass Spectrometry Data Research Articles

Open Modification Searching of SARS-CoV-2–Human Protein Interaction Data Reveals Novel Viral Modification Sites

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus–host protein–protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification–mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus–host protein–protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus–host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

A meta-analysis of affinity purification-mass spectrometry experimental systems used to identify eukaryotic and chlamydial proteins at the Chlamydia trachomatis inclusion membrane

The obligate intracellular bacterial pathogen, Chlamydia trachomatis, develops within a membrane-bound vacuole termed the inclusion. Affinity purification-mass spectrometry (AP-MS) experiments to study the interactions that occur at the chlamydial inclusion membrane have been performed and, more recently, combined with advances in C. trachomatis genetics. However, each of the four AP-MS published reports used either different experimental approaches or statistical tools to identify proteins that localize at the inclusion. We critically analyzed each experimental approach and performed a meta-analysis of the reported statistically significant proteins for each study, finding that only a few eukaryotic proteins were commonly identified between all four experimental approaches. The two similarly conducted in vivo labeling studies were compared using the same statistical analysis tool, Significance Analysis of INTeractome (SAINT), which revealed a disparity in the number of significant proteins identified by the original analysis. We further examined methods to identify potential background contaminant proteins that remain after statistical analysis. Overall, this meta-analysis highlights the importance of carefully controlling and analyzing the AP-MS data so that pertinent information can be obtained from these various AP-MS experimental approaches. This study provides important guidelines and considerations for using this methodology to study intracellular pathogens residing within a membrane-bound compartment. SignificanceChlamydia trachomatis, an obligate intracellular pathogen, grows within a membrane-bound vacuole termed the inclusion. The inclusion is studded with bacterial membrane proteins that likely orchestrate numerous interactions with the host cell. Although maintenance of the intracellular niche is vital, an understanding of the host-pathogen interactions that occur at the inclusion membrane is limited by the difficulty in purifying membrane protein fractions from infected host cells. The experimental procedures necessary to solubilize hydrophobic proteins fail to maintain transient protein-protein interactions. Advances in C. trachomatis genetics has allowed us and others to use various experimental approaches in combination with affinity purification mass spectrometry (AP-MS) to study the interactions that occur at the chlamydial vacuolar, or inclusion, membrane. For the first time, two groups have published AP-MS studies using the same tool, the ascorbate peroxidase proximity labeling system (APEX2), which overcomes past experimental limitations because membrane protein interactions are labeled in vivo in the context of infection. The utility of this system is highlighted by its ability to study chlamydial type III secreted inclusion membrane protein (Inc) interactions. Incs act as the mediators of host-pathogen interactions at the inclusion during C. trachomatis infection. When carefully controlled and analyzed, the data obtained can yield copious amounts of useful information. Here, we critically analyzed four previously published studies, including statistical analysis of AP-MS datasets related to Chlamydia-host interactions, to contextualize the data and to identify the best practices in interpreting these types of complex outputs.

SAINTq: Scoring protein-protein interactions in affinity purification - mass spectrometry experiments with fragment or peptide intensity data.

SAINT (Significance Analysis of INTeractome) is a probabilistic method for scoring bait-prey interactions against negative controls in affinity purification - mass spectrometry (AP-MS) experiments. Our published SAINT algorithms use spectral counts or protein intensities as the input for calculating the probability of true interaction, which enables objective selection of high-confidence interactions with false discovery control. With the advent of new protein quantification methods such as Data Independent Acquisition (DIA), we redeveloped the scoring method to utilize the reproducibility information embedded in the peptide or fragment intensity data as a key scoring criterion, bypassing protein intensity summarization required in the previous SAINT workflow. The new software package, SAINTq, addresses key issues in the interaction scoring based on intensity data, including treatment of missing values and selection of peptides and fragments for scoring each prey protein. We applied SAINTq to two independent DIA AP-MS data sets profiling the interactome of MEPCE and EIF4A2 and that of 14-3-3β, and benchmarked the performance in terms of recovering previously reported literature interactions in the iRefIndex database. In both data sets, the SAINTq analysis using the fragment-level intensity data led to the most sensitive detection of literature interactions at the same level of specificity. This analysis outperforms the analysis using protein intensity data summed from fragment intensity data that is equivalent to the model in SAINTexpress.

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data.

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

Identifying binary protein-protein interactions from affinity purification mass spectrometry data.

BackgroundThe identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells. With the development of affinity purification-mass spectrometry (AP-MS) techniques, several computational scoring methods have been proposed to detect protein interactions from AP-MS data. However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions. Consequently, less is known about the precise physical wiring diagram within cells.ResultsIn this paper, we develop a Binary Interaction Network Model (BINM) to computationally identify direct physical interactions from co-complex interactions which can be inferred from purification data using previous scoring methods. This model provides a mathematical framework for capturing topological relationships between direct physical interactions and observed co-complex interactions. It reassigns a confidence score to each observed interaction to indicate its propensity to be a direct physical interaction. Then observed interactions with high confidence scores are predicted as direct physical interactions. We run our model on two yeast co-complex interaction networks which are constructed by two different scoring methods on a same combined AP-MS data. The direct physical interactions identified by various methods are comprehensively benchmarked against different reference sets that provide both direct and indirect evidence for physical contacts. Experiment results show that our model has a competitive performance over the state-of-the-art methods.ConclusionsAccording to the results obtained in this study, BINM is a powerful scoring method that can solely use network topology to predict direct physical interactions from AP-MS data. This study provides us an alternative approach to explore the information inherent in AP-MS data. The software can be downloaded from https://github.com/Zhangxf-ccnu/BINM.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1944-z) contains supplementary material, which is available to authorized users.

Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST.

High-throughput Affinity Purification Mass Spectrometry (AP-MS) experiments can identify a large number of protein interactions, but only a fraction of these interactions are biologically relevant. Here, we describe a comprehensive computational strategy to process raw AP-MS data, perform quality controls, and prioritize biologically relevant bait-prey pairs in a set of replicated AP-MS experiments with Mass spectrometry interaction STatistics (MiST). The MiST score is a linear combination of prey quantity (abundance), abundance invariability across repeated experiments (reproducibility), and prey uniqueness relative to other baits (specificity). We describe how to run the full MiST analysis pipeline in an R environment and discuss a number of configurable options that allow the lay user to convert any large-scale AP-MS data into an interpretable, biologically relevant protein-protein interaction network.

Pre- and Post-Processing Workflow for Affinity Purification Mass Spectrometry Data

The reliable detection of protein-protein interactions by affinity purification mass spectrometry (AP-MS) is crucial for the understanding of biological processes. Quantitative information can be used to separate truly interacting proteins from false-positives by contrasting counts of proteins binding to specific baits with counts of negative controls. Several approaches have been proposed for computing scores for potential interaction proteins, for example, the commonly used SAINT software. However, it remains a subjective decision where to set the cutoff score for candidate selection; furthermore, no precise control for the expected number of false-positives is provided. In related fields, successful data analysis strongly relies on statistical pre- and post-processing steps, which, so far, have played only a minor role in AP-MS data analysis. We introduce a complete workflow, embedding either the scoring method SAINT or alternatively a two-stage Poisson model into a pre- and post-processing framework. To this end, we investigate different normalization methods and apply a statistical filter adjusted to AP-MS data. Furthermore, we propose permutation and adjustment procedures, which allow the replacement of scores by statistical p values. The performance of the workflow is assessed on simulations as well as on a study focusing on interactions with the T3SS in Salmonella Typhimurium. Preprocessing methods significantly increase the number of detected truly interacting proteins, while a constant false-discovery rate is maintained. The software solution is freely available.

Detection of protein complexes from affinity purification/mass spectrometry data

BackgroundRecent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored.MethodsThis paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation.ResultsExperimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes.ConclusionsOur study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.

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.

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.

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.

Predicting direct protein interactions from affinity purification mass spectrometry data

BackgroundAffinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest.ResultsWe first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm.Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset.ConclusionsAs the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.

Local modeling of global interactome networks

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. 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. 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 dscholtens@northwestern.edu http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling