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

The DEAD-box RNA helicase PfDOZI imposes opposing actions on RNA metabolism in Plasmodium falciparum

In malaria parasites, the regulation of mRNA translation, storage and degradation during development and life-stage transitions remains largely unknown. Here, we functionally characterized the DEAD-box RNA helicase PfDOZI in P. falciparum. Disruption of pfdozi enhanced asexual proliferation but reduced sexual commitment and impaired gametocyte development. By quantitative transcriptomics, we show that PfDOZI is involved in the regulation of invasion-related genes and sexual stage-specific genes during different developmental stages. PfDOZI predominantly participates in processing body-like mRNPs in schizonts but germ cell granule-like mRNPs in gametocytes to impose opposing actions of degradation and protection on different mRNA targets. We further show the formation of stress granule-like mRNPs during nutritional deprivation, highlighting an essential role of PfDOZI-associated mRNPs in stress response. We demonstrate that PfDOZI participates in distinct mRNPs to maintain mRNA homeostasis in response to life-stage transition and environmental changes by differentially executing post-transcriptional regulation on the target mRNAs.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Proximity Interactome Analysis of Super Conserved Receptors Expressed in the Brain Identifies EPB41L2, SLC3A2, and LRBA as Main Partners

The Super-Conserved Receptors Expressed in the Brain (SREBs) form a subfamily of orphan G protein-coupled receptors, highly conserved in evolution and characterized by a predominant expression in the brain. The signaling pathways activated by these receptors (if any) are presently unclear. Given the strong conservation of their intracellular loops, we used a BioID2 proximity-labeling assay to identify protein partners of SREBs that would interact with these conserved domains. Using streptavidin pull-down followed by mass spectrometry analysis, we identified the amino acid transporter SLC3A2, the AKAP protein LRBA, and the 4.1 protein EPB41L2 as potential interactors of these GPCRs. Using co-immunoprecipitation experiments, we confirmed the physical association of these proteins with the receptors. We then studied the functional relevance of the interaction between EPB41L2 and SREB1. Immunofluorescence microscopy revealed that SREB1 and EPB41L2 co-localize at the plasma membrane and that SREB1 is enriched in the β-catenin-positive cell membranes. siRNA knockdown experiments revealed that EPB41L2 promotes the localization of SREB1 at the plasma membrane and increases the solubilization of SREB1 when using detergents, suggesting a modification of its membrane microenvironment. Altogether, these data suggest that EPB41L2 could regulate the subcellular compartmentalization of SREBs and, as proposed for other GPCRs, could affect their stability or activation.

Protein network analysis and functional enrichment via computational biotechnology unravel molecular and pathogenic mechanisms of kidney stone disease

Mass spectrometry-based proteomics has been extensively applied to current biomedical research. From such large-scale identification of proteins, several computational tools have been developed for determining protein-protein interactions (PPI) network and functional significance of the identified proteins and their complex. Analyses of PPI network and functional enrichment have been widely applied to various fields of biomedical research. Herein, we summarize commonly used tools for PPI network analysis and functional enrichment in kidney stone research and discuss their applications to kidney stone disease (KSD). Such computational approach has been used mainly to investigate PPI networks and functional significance of the proteins derived from urine of patients with kidney stone (stone formers), stone matrix, Randall's plaque, renal papilla, renal tubular cells, mitochondria and immune cells. The data obtained from computational biotechnology leads to experimental validation and investigations that offer new knowledge on kidney stone formation processes. Moreover, the computational approach may also lead to defining new therapeutic targets and preventive strategies for better outcome in KSD management.

Identification and Quantification of Affinity-Purified Proteins with MaxQuant, Followed by the Discrimination of Nonspecific Interactions with the CRAPome Interface

Affinity purification coupled to mass spectrometry (AP-MS) is a powerful method to analyze protein-protein interactions (PPIs). The AP-MS approach provides an unbiased analysis of the entire protein complex and is useful to identify indirect interactors. However, reliable protein identification from the complex AP-MS experiments requires appropriate control of false identifications and rigorous statistical analysis. Another challenge that can arise from AP-MS analysis is to distinguish bona fide interacting proteins from the non-specifically bound endogenous proteins or the "background contaminants" that co-purified by the bait experiments. In this chapter, we will first describe the protocol for performing in-solution trypsinization for the samples from the AP experiment followed by LC-MS/MS analysis. We will then detail the MaxQuant workflow for protein identification and quantification for the PPI data derived from the AP-MS experiment. Finally, we describe the CRAPome interface to process the data by filtering against contaminant lists, score the interactions and visualize the protein interaction networks.

Profiling protein interactions by purification with capillary monolithic affinity column in combination with label-free quantitative proteomics

An approach for profiling protein-protein interactions by using affinity purification with capillary monolithic immobilized metal affinity chromatography column (cm-IMAC) in combination with label free quantitative proteomics was described in the present work. The cm-IMAC columns were prepared in a single step by copolymerization of the function monomer, namely (S)-2,2'-((1-carboxy-5-(pent‑4-enamido)pentyl)azanediyl)diacetic acid which provide a nitrilotriacetate (NTA) moiety to form chelated complexation with Ni (II) ions, inside the fused silica capillaries. The His₆-tagged bait protein can be easily immobilized on the cm-IMAC columns through the formation of chelating complexation with the NTA-Ni (II) functional groups of the matrix. The cm-IMAC columns were used to explore protein-protein interactions (PPIs) on a proteomic scale when combined with label-free proteomics. A known interaction pair of proteins, namely NDP52 (amino acid sequence 10-126) and NAP1 (33-75) as well as Bcl-2 family proteins were used for proof of concept. New interactors of Bcl-X_L were identified and validated by co-immunoprecipitation.

Structural dynamics shape the fitness window of alanine:glyoxylate aminotransferase

The conformational landscape of a protein is constantly expanded by genetic variations that have a minimal impact on the function(s) while causing subtle effects on protein structure. The wider the conformational space sampled by these variants, the higher the probabilities to adapt to changes in environmental conditions. However, the probability that a single mutation may result in a pathogenic phenotype also increases. Here we present a paradigmatic example of how protein evolution balances structural stability and dynamics to maximize protein adaptability and preserve protein fitness. We took advantage of known genetic variations of human alanine:glyoxylate aminotransferase (AGT1), which is present as a common major allelic form (AGT‐Ma) and a minor polymorphic form (AGT‐Mi) expressed in 20% of Caucasian population. By integrating crystallographic studies and molecular dynamics simulations, we show that AGT‐Ma is endowed with structurally unstable (frustrated) regions, which become disordered in AGT‐Mi. An in‐depth biochemical characterization of variants from an anticonsensus library, encompassing the frustrated regions, correlates this plasticity to a fitness window defined by AGT‐Ma and AGT‐Mi. Finally, co‐immunoprecipitation analysis suggests that structural frustration in AGT1 could favor additional functions related to protein–protein interactions. These results expand our understanding of protein structural evolution by establishing that naturally occurring genetic variations tip the balance between stability and frustration to maximize the ensemble of conformations falling within a well‐defined fitness window, thus expanding the adaptability potential of the protein.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

PHF3 regulates neuronal gene expression through the Pol II CTD reader domain SPOC

The C-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) is a regulatory hub for transcription and RNA processing. Here, we identify PHD-finger protein 3 (PHF3) as a regulator of transcription and mRNA stability that docks onto Pol II CTD through its SPOC domain. We characterize SPOC as a CTD reader domain that preferentially binds two phosphorylated Serine-2 marks in adjacent CTD repeats. PHF3 drives liquid-liquid phase separation of phosphorylated Pol II, colocalizes with Pol II clusters and tracks with Pol II across the length of genes. PHF3 knock-out or SPOC deletion in human cells results in increased Pol II stalling, reduced elongation rate and an increase in mRNA stability, with marked derepression of neuronal genes. Key neuronal genes are aberrantly expressed in Phf3 knock-out mouse embryonic stem cells, resulting in impaired neuronal differentiation. Our data suggest that PHF3 acts as a prominent effector of neuronal gene regulation by bridging transcription with mRNA decay.

Bottom-up and top-down proteomic approaches for the identification, characterization, and quantification of the low molecular weight proteome with focus on short open reading frame-encoded peptides

The recent discovery of alternative open reading frames creates a need for suitable analytical approaches to verify their translation and to characterize the corresponding gene products at the molecular level. As the analysis of small proteins within a background proteome by means of classical bottom-up proteomics is challenging, method development for the analysis of small open reading frame encoded peptides (SEPs) have become a focal point for research. Here, we highlight bottom-up and top-down proteomics approaches established for the analysis of SEPs in both pro- and eukaryotes. Major steps of analysis, including sample preparation and (small) proteome isolation, separation and mass spectrometry, data interpretation and quality control, quantification, the analysis of post-translational modifications, and exploration of functional aspects of the SEPs by means of proteomics technologies are described. These methods do not exclusively cover the analytics of SEPs but simultaneously include the low molecular weight proteome, and moreover, can also be used for the proteome-wide analysis of proteolytic processing events.

Defining the Caprin-1 Interactome in Unstressed and Stressed Conditions

Cytoplasmic stress granules (SGs) are dynamic foci containing translationally arrested mRNA and RNA-binding proteins (RBPs) that form in response to a variety of cellular stressors. It has been debated that SGs may evolve into cytoplasmic inclusions observed in many neurodegenerative diseases. Recent studies have examined the SG proteome by interrogating the interactome of G3BP1. However, it is widely accepted that multiple baits are required to capture the full SG proteome. To gain further insight into the SG proteome, we employed immunoprecipitation coupled with mass spectrometry of endogenous Caprin-1, an RBP implicated in mRNP granules. Overall, we identified 1543 proteins that interact with Caprin-1. Interactors under stressed conditions were primarily annotated to the ribosome, spliceosome, and RNA transport pathways. We validated four Caprin-1 interactors that localized to arsenite-induced SGs: ANKHD1, TALIN-1, GEMIN5, and SNRNP200. We also validated these stress-induced interactions in SH-SY5Y cells and further determined that SNRNP200 also associated with osmotic- and thermal-induced SGs. Finally, we identified SNRNP200 in cytoplasmic aggregates in amyotrophic lateral sclerosis (ALS) spinal cord and motor cortex. Collectively, our findings provide the first description of the Caprin-1 protein interactome, identify novel cytoplasmic SG components, and reveal a SG protein in cytoplasmic aggregates in ALS patient neurons. Proteomic data collected in this study are available via ProteomeXchange with identifier PXD023271.

Quantitative Interactomics of Lck-TurboID in Living Human T Cells Unveils T Cell Receptor Stimulation-Induced Proximal Lck Interactors

While Lck has been widely recognized to play a pivotal role in the initiation of the T cell receptor (TCR) signaling pathway, an understanding of the precise regulation of Lck in T cells upon TCR activation remains elusive. Investigation of protein-protein interaction (PPI) using proximity labeling techniques such as TurboID has the potential to provide valuable molecular insights into Lck regulatory networks. By expressing Lck-TurboID in Jurkat T cells, we have uncovered a dynamic, short-range Lck protein interaction network upon 30 min of TCR stimulation. In this novel application of TurboID, we detected 27 early signaling-induced Lck-proximal interactors in living T cells, including known and novel Lck interactors, validating the discovery power of this tool. Our results revealed previously unappreciated Lck PPI which may be associated with cytoskeletal rearrangement, ubiquitination of TCR signaling proteins, activation of the mitogen-activated protein kinase cascade, coalescence of the LAT signalosome, and formation of the immunological synapse. In this study, we demonstrated for the first time in immune cells and for the kinase Lck that TurboID can be utilized to unveil PPI dynamics in living cells at a time scale consistent with early TCR signaling. Data are available via ProteomeXchange with identifier PXD020759.

Comparison of CRISPR Genomic Tagging for Affinity Purification and Endogenous Immunoprecipitation Coupled with Quantitative Mass Spectrometry To Identify the Dynamic AMPKα2 Interactome

Recent advances in genome editing technologies have enabled the insertion of epitope tags at endogenous loci with relative efficiency. We describe an approach for investigation of protein interaction dynamics of the AMP-activated kinase complex AMPK using a catalytic subunit AMPKα2 (PRKAA2 gene) as the bait, based on CRISPR/Cas9-mediated genome editing coupled to stable isotope labeling in cell culture, multidimensional protein identification technology, and computational and statistical analyses. Furthermore, we directly compare this genetic epitope tagging approach to endogenous immunoprecipitations of the same gene under homologous conditions to assess differences in observed interactors. Additionally, we directly compared each enrichment strategy in the genetically modified cell-line with two separate endogenous antibodies. For each approach, we analyzed the interaction profiles of this protein complex under basal and activated states, and after implementing the same analytical, computational, and statistical analyses, we found that high-confidence protein interactors vary greatly with each method and between commercially available endogenous antibodies.

Insulin Induces Microtubule Stabilization and Regulates the Microtubule Plus-end Tracking Protein Network in Adipocytes

Insulin-stimulated glucose uptake is known to involve microtubules, although the function of microtubules and the microtubule-regulating proteins involved in insulin action are poorly understood. CLASP2, a plus-end tracking microtubule-associated protein (+TIP) that controls microtubule dynamics, was recently implicated as the first +TIP associated with insulin-regulated glucose uptake. Here, using protein-specific targeted quantitative phosphoproteomics within 3T3-L1 adipocytes, we discovered that insulin regulates phosphorylation of the CLASP2 network members G2L1, MARK2, CLIP2, AGAP3, and CKAP5 as well as EB1, revealing the existence of a previously unknown microtubule-associated protein system that responds to insulin. To further investigate, G2L1 interactome studies within 3T3-L1 adipocytes revealed that G2L1 coimmunoprecipitates CLASP2 and CLIP2 as well as the master integrators of +TIP assembly, the end binding (EB) proteins. Live-cell total internal reflection fluorescence microscopy in adipocytes revealed G2L1 and CLASP2 colocalize on microtubule plus-ends. We found that although insulin increases the number of CLASP2-containing plus-ends, insulin treatment simultaneously decreases CLASP2-containing plus-end velocity. In addition, we discovered that insulin stimulates redistribution of CLASP2 and G2L1 from exclusive plus-end tracking to "trailing" behind the growing tip of the microtubule. Insulin treatment increases α-tubulin Lysine 40 acetylation, a mechanism that was observed to be regulated by a counterbalance between GSK3 and mTOR, and led to microtubule stabilization. Our studies introduce insulin-stimulated microtubule stabilization and plus-end trailing of +TIPs as new modes of insulin action and reveal the likelihood that a network of microtubule-associated proteins synergize to coordinate insulin-regulated microtubule dynamics.

FKBP12 contributes to α-synuclein toxicity by regulating the calcineurin-dependent phosphoproteome

Calcineurin is an essential Ca2+-dependent phosphatase. Increased calcineurin activity is associated with α-synuclein (α-syn) toxicity, a protein implicated in Parkinson's Disease (PD) and other neurodegenerative diseases. Calcineurin can be inhibited with Tacrolimus through the recruitment and inhibition of the 12-kDa cis-trans proline isomerase FK506-binding protein (FKBP12). Whether calcineurin/FKBP12 represents a native physiologically relevant assembly that occurs in the absence of pharmacological perturbation has remained elusive. We leveraged α-syn as a model to interrogate whether FKBP12 plays a role in regulating calcineurin activity in the absence of Tacrolimus. We show that FKBP12 profoundly affects the calcineurin-dependent phosphoproteome, promoting the dephosphorylation of a subset of proteins that contributes to α-syn toxicity. Using a rat model of PD, partial elimination of the functional interaction between FKBP12 and calcineurin, with low doses of the Food and Drug Administration (FDA)-approved compound Tacrolimus, blocks calcineurin's activity toward those proteins and protects against the toxic hallmarks of α-syn pathology. Thus, FKBP12 can endogenously regulate calcineurin activity with therapeutic implications for the treatment of PD.

Insect RNAi: Integrating a New Tool in the Crop Protection Toolkit

Protecting crops against insect pests is a major focus area in crop protection. Over the past two decades, biotechnological interventions, especially Bt proteins, have been successfully implemented across the world and have had major impacts on reducing chemical pesticide applications. As insects continue to adapt to insecticides, both chemical and protein-based, new methods, molecules, and modes of action are necessary to provide sustainable solutions. RNA interference (RNAi) has emerged as a significant tool to knock down or alter gene expression profiles in a species-specific manner. In the past decade, there has been intense research on RNAi applications in crop protection. This chapter looks at the current state of knowledge in the field and outlines the methodology, delivery methods, and precautions required in designing targets. Assessing the targeting of specific gene expression is also an important part of a successful RNAi strategy. The current literature on the use of RNAi in major orders of insect pests is reviewed, along with a perspective on the regulatory aspects of the approach. Risk assessment of RNAi would focus on molecular characterization, food/feed risk assessment, and environmental risk assessment. As more RNAi-based products come through regulatory systems, either via direct application or plant expression based, the impact of this approach on crop protection will become clearer.

msVolcano: A flexible web application for visualizing quantitative proteomics data

We introduce msVolcano, a web application for the visualization of label‐free mass spectrometric data. It is optimized for the output of the MaxQuant data analysis pipeline of interactomics experiments and generates volcano plots with lists of interacting proteins. The user can optimize the cutoff values to find meaningful significant interactors for the tagged protein of interest. Optionally, stoichiometries of interacting proteins can be calculated. Several customization options are provided to the user for flexibility, and publication‐quality outputs can also be downloaded (tabular and graphical). Availability: msVolcano is implemented in R Statistical language using Shiny. It can be accessed freely at http://projects.biotec.tu-dresden.de/msVolcano/

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

Characterization of the CLASP2 Protein Interaction Network Identifies SOGA1 as a Microtubule-Associated Protein

CLASP2 is a microtubule-associated protein that undergoes insulin-stimulated phosphorylation and co-localization with reorganized actin and GLUT4 at the plasma membrane. To gain insight to the role of CLASP2 in this system, we developed and successfully executed a streamlined interactome approach and built a CLASP2 protein network in 3T3-L1 adipocytes. Using two different commercially available antibodies for CLASP2 and an antibody for epitope-tagged, overexpressed CLASP2, we performed multiple affinity purification coupled with mass spectrometry (AP-MS) experiments in combination with label-free quantitative proteomics and analyzed the data with the bioinformatics tool Significance Analysis of Interactome (SAINT). We discovered that CLASP2 coimmunoprecipitates (co-IPs) the novel protein SOGA1, the microtubule-associated protein kinase MARK2, and the microtubule/actin-regulating protein G2L1. The GTPase-activating proteins AGAP1 and AGAP3 were also enriched in the CLASP2 interactome, although subsequent AGAP3 and CLIP2 interactome analysis suggests a preference of AGAP3 for CLIP2. Follow-up MARK2 interactome analysis confirmed reciprocal co-IP of CLASP2 and revealed MARK2 can co-IP SOGA1, glycogen synthase, and glycogenin. Investigating the SOGA1 interactome confirmed SOGA1 can reciprocal co-IP both CLASP2 and MARK2 as well as glycogen synthase and glycogenin. SOGA1 was confirmed to colocalize with CLASP2 and with tubulin, which identifies SOGA1 as a new microtubule-associated protein. These results introduce the metabolic function of these proposed novel protein networks and their relationship with microtubules as new fields of cytoskeleton-associated protein biology.

SIRT2 regulates nuclear envelope reassembly through ANKLE2 deacetylation

Sirtuin 2 (SIRT2) is an NAD-dependent deacetylase known to regulate microtubule dynamics and cell cycle progression. SIRT2 has also been implicated in the pathology of cancer, neurodegenerative diseases and progeria. Here, we show that SIRT2 depletion or overexpression causes nuclear envelope reassembly defects. We link this phenotype to the recently identified regulator of nuclear envelope reassembly ANKLE2. ANKLE2 acetylation at K302 and phosphorylation at S662 are dynamically regulated throughout the cell cycle by SIRT2 and are essential for normal nuclear envelope reassembly. The function of SIRT2 therefore extends beyond the regulation of microtubules to include the regulation of nuclear envelope dynamics.

APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data

With continuously increasing scale and depth of coverage in affinity proteomics (AP-MS) data, the analysis and visualization is becoming more challenging. A number of tools have been developed to identify high-confidence interactions; however, a cohesive and intuitive pipeline for analysis and visualization is still needed. Here we present Automated Processing of SAINT Templated Layouts (APOSTL), a freely available Galaxy-integrated software suite and analysis pipeline for reproducible, interactive analysis of AP-MS data. APOSTL contains a number of tools woven together using Galaxy workflows, which are intuitive for the user to move from raw data to publication-quality figures within a single interface. APOSTL is an evolving software project with the potential to customize individual analyses with additional Galaxy tools and widgets using the R web application framework, Shiny. The source code, data, and documentation are freely available from GitHub ( https://github.com/bornea/APOSTL ) and other sources.

Stable association of RNAi machinery is conserved between the cytoplasm and nucleus of human cells

Argonaute 2 (AGO2), the catalytic engine of RNAi, is typically associated with inhibition of translation in the cytoplasm. AGO2 has also been implicated in nuclear processes including transcription and splicing. There has been little insight into AGO2's nuclear interactions or how they might differ relative to cytoplasm. Here we investigate the interactions of cytoplasmic and nuclear AGO2 using semi-quantitative mass spectrometry. Mass spectrometry often reveals long lists of candidate proteins, complicating efforts to rigorously discriminate true interacting partners from artifacts. We prioritized candidates using orthogonal analytical strategies that compare replicate mass spectra of proteins associated with Flag-tagged and endogenous AGO2. Interactions with TRNC6A, TRNC6B, TNRC6C, and AGO3 are conserved between nuclei and cytoplasm. TAR binding protein interacted stably with cytoplasmic AGO2 but not nuclear AGO2, consistent with strand loading in the cytoplasm. Our data suggest that interactions between functionally important components of RNAi machinery are conserved between the nucleus and cytoplasm but that accessory proteins differ. Orthogonal analysis of mass spectra is a powerful approach to streamlining identification of protein partners.

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.

Data Independent Acquisition analysis in ProHits 4.0

Affinity purification coupled with mass spectrometry (AP-MS) is a powerful technique for the identification and quantification of physical interactions. AP-MS requires careful experimental design, appropriate control selection and quantitative workflows to successfully identify bona fide interactors amongst a large background of contaminants. We previously introduced ProHits, a Laboratory Information Management System for interaction proteomics, which tracks all samples in a mass spectrometry facility, initiates database searches and provides visualization tools for spectral counting-based AP-MS approaches. More recently, we implemented Significance Analysis of INTeractome (SAINT) within ProHits to provide scoring of interactions based on spectral counts. Here, we provide an update to ProHits to support Data Independent Acquisition (DIA) with identification software (DIA-Umpire and MSPLIT-DIA), quantification tools (through DIA-Umpire, or externally via targeted extraction), and assessment of quantitative enrichment (through mapDIA) and scoring of interactions (through SAINT-intensity). With additional improvements, notably support of the iProphet pipeline, facilitated deposition into ProteomeXchange repositories and enhanced export and viewing functions, ProHits 4.0 offers a comprehensive suite of tools to facilitate affinity proteomics studies.

SIGNIFICANCE

It remains challenging to score, annotate and analyze proteomics data in a transparent manner. ProHits was previously introduced as a LIMS to enable storing, tracking and analysis of standard AP-MS data. In this revised version, we expand ProHits to include integration with a number of identification and quantification tools based on Data-Independent Acquisition (DIA). ProHits 4.0 also facilitates data deposition into public repositories, and the transfer of data to new visualization tools.

Immunoprecipitation and mass spectrometry defines an extensive RBM45 protein-protein interaction network

The pathological accumulation of RNA-binding proteins (RBPs) within inclusion bodies is a hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). RBP aggregation results in both toxic gain and loss of normal function. Determining the protein binding partners and normal functions of disease-associated RBPs is necessary to fully understand molecular mechanisms of RBPs in disease. Herein, we characterized the protein-protein interactions (PPIs) of RBM45, a RBP that localizes to inclusions in ALS/FTLD. Using immunoprecipitation coupled to mass spectrometry (IP-MS), we identified 132 proteins that specifically interact with RBM45 within HEK293 cells. Select PPIs were validated by immunoblot and immunocytochemistry, demonstrating that RBM45 associates with a number of other RBPs primarily via RNA-dependent interactions in the nucleus. Analysis of the biological processes and pathways associated with RBM45-interacting proteins indicates enrichment for nuclear RNA processing/splicing via association with hnRNP proteins and cytoplasmic RNA translation via eiF2 and eiF4 pathways. Moreover, several other ALS-linked RBPs, including TDP-43, FUS, Matrin-3, and hnRNP-A1, interact with RBM45, consistent with prior observations of these proteins within intracellular inclusions in ALS/FTLD. Taken together, our results define a PPI network for RBM45, suggest novel functions for this protein, and provide new insights into the contributions of RBM45 to neurodegeneration in ALS/FTLD. This article is part of a Special Issue entitled SI:RNA Metabolism in Disease.

Proteomic network analysis of human uterine smooth muscle in pregnancy, labor, and preterm labor

The molecular mechanisms involved in human uterine quiescence during gestation and the induction of labor at term or preterm are not completely known. Preterm delivery is associated with major morbidity and mortality and current efforts to prevent delivery until term are largely ineffective. Identification and semi-quantification of proteomic changes in uterine smooth muscle during pregnancy will allow for targeted research into how quiescence is maintained and what changes are associated with induction of labor. Examining preterm labor in this context will provide potential therapeutic targets for the management of preterm labor. We have recently performed two dimensional liquid chromatography coupled with tandem mass spectrometry on myometrial proteins isolated from pregnant patients in labor, pregnant patients not in labor, and pregnant patients in labor preterm. Using a conservative false discovery rate of 1% we have identified 2132 protein groups using this method and semi-quantitative spectral counting shows 201 proteins that have disparate levels of expression in preterm laboring samples. To our knowledge this is the first large scale proteomic study examining human uterine smooth muscle and this initial work has provided a target list for future experiments that can address how changing protein levels are involved in the induction of labor at term and preterm.

TRAF1 Coordinates Polyubiquitin Signaling to Enhance Epstein-Barr Virus LMP1-Mediated Growth and Survival Pathway Activation

The Epstein-Barr virus (EBV) encoded oncoprotein Latent Membrane Protein 1 (LMP1) signals through two C-terminal tail domains to drive cell growth, survival and transformation. The LMP1 membrane-proximal TES1/CTAR1 domain recruits TRAFs to activate MAP kinase, non-canonical and canonical NF-kB pathways, and is critical for EBV-mediated B-cell transformation. TRAF1 is amongst the most highly TES1-induced target genes and is abundantly expressed in EBV-associated lymphoproliferative disorders. We found that TRAF1 expression enhanced LMP1 TES1 domain-mediated activation of the p38, JNK, ERK and canonical NF-kB pathways, but not non-canonical NF-kB pathway activity. To gain insights into how TRAF1 amplifies LMP1 TES1 MAP kinase and canonical NF-kB pathways, we performed proteomic analysis of TRAF1 complexes immuno-purified from cells uninduced or induced for LMP1 TES1 signaling. Unexpectedly, we found that LMP1 TES1 domain signaling induced an association between TRAF1 and the linear ubiquitin chain assembly complex (LUBAC), and stimulated linear (M1)-linked polyubiquitin chain attachment to TRAF1 complexes. LMP1 or TRAF1 complexes isolated from EBV-transformed lymphoblastoid B cell lines (LCLs) were highly modified by M1-linked polyubiqutin chains. The M1-ubiquitin binding proteins IKK-gamma/NEMO, A20 and ABIN1 each associate with TRAF1 in cells that express LMP1. TRAF2, but not the cIAP1 or cIAP2 ubiquitin ligases, plays a key role in LUBAC recruitment and M1-chain attachment to TRAF1 complexes, implicating the TRAF1:TRAF2 heterotrimer in LMP1 TES1-dependent LUBAC activation. Depletion of either TRAF1, or the LUBAC ubiquitin E3 ligase subunit HOIP, markedly impaired LCL growth. Likewise, LMP1 or TRAF1 complexes purified from LCLs were decorated by lysine 63 (K63)-linked polyubiqutin chains. LMP1 TES1 signaling induced K63-polyubiquitin chain attachment to TRAF1 complexes, and TRAF2 was identified as K63-Ub chain target. Co-localization of M1- and K63-linked polyubiquitin chains on LMP1 complexes may facilitate downstream canonical NF-kB pathway activation. Our results highlight LUBAC as a novel potential therapeutic target in EBV-associated lymphoproliferative disorders.

The linear ubiquitin assembly complex (LUBAC) plays crucial roles in immune receptor-mediated NF-kB and MAP kinase pathway activation. Comparatively little is known about the extent to which microbial pathogens use LUBAC to activate downstream pathways. We demonstrate that TRAF1 enhances EBV oncoprotein LMP1 TES1/CTAR1 domain mediated MAP kinase and canonical NF-kB activation. LMP1 TES1 signaling induces association between TRAF1 and LUBAC, and triggers M1-polyubiquitin chain attachment to TRAF1 complexes. TRAF1 and LMP1 complexes are decorated by M1-polyubiquitin chains in LCL extracts. TRAF2 plays a key role in LMP1-induced LUBAC recruitment and M1-chain attachment to TRAF1 complexes. TRAF1 and LMP1 complexes are modified by lysine 63-linked polyubiquitin chains in LCL extracts, and TRAF2 is a target of LMP1-induced K63-ubiquitin chain attachment. Thus, the TRAF1:TRAF2 heterotrimer may coordinate ubiquitin signaling downstream of TES1. Depletion of TRAF1 or the LUBAC subunit HOIP impairs LCL growth and survival. Thus, although TRAF1 is the only TRAF without a RING finger ubiquitin ligase domain, TRAF1 nonetheless has important roles in ubiqutin-mediated signal transduction downstream of LMP1. Our work suggests that LUBAC is important for EBV-driven B-cell proliferation, and suggests that LUBAC may be a novel therapeutic target in EBV-associated lymphoproliferative disorders.

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics

As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.

The interactome of the atypical phosphatase Rtr1 in Saccharomyces cerevisiae

The phosphatase Rtr1 has been implicated in dephosphorylation of the RNA Polymerase II (RNAPII) C-terminal domain (CTD) during transcription elongation and in regulation of nuclear import of RNAPII. Although it has been shown that Rtr1 interacts with RNAPII in yeast and humans, the specific mechanisms that underlie Rtr1 recruitment to RNAPII have not been elucidated. To address this, we have performed an in-depth proteomic analysis of Rtr1 interacting proteins in yeast. Our studies revealed that hyperphosphorylated RNAPII is the primary interacting partner for Rtr1. To extend these findings, we performed quantitative proteomic analyses of Rtr1 interactions in yeast strains deleted for CTK1, the gene encoding the catalytic subunit of the CTD kinase I (CTDK-I) complex. Interestingly, we found that the interaction between Rtr1 and RNAPII is decreased in ctk1Δ strains. We hypothesize that serine-2 CTD phosphorylation is required for Rtr1 recruitment to RNAPII during transcription elongation.

A systems-wide screen identifies substrates of the SCFβTrCP ubiquitin ligase

Cellular proteins are degraded by the ubiquitin-proteasome system (UPS) in a precise and timely fashion. Such precision is conferred by the high substrate specificity of ubiquitin ligases. Identification of substrates of ubiquitin ligases is crucial not only to unravel the molecular mechanisms by which the UPS controls protein degradation but also for drug discovery purposes because many established UPS substrates are implicated in disease. We developed a combined bioinformatics and affinity purification-mass spectrometry (AP-MS) workflow for the system-wide identification of substrates of SCF(βTrCP), a member of the SCF family of ubiquitin ligases. These ubiquitin ligases are characterized by a multisubunit architecture typically consisting of the invariable subunits Rbx1, Cul1, and Skp1 and one of 69 F-box proteins. The F-box protein of this member of the family is βTrCP. SCF(βTrCP) binds, through the WD40 repeats of βTrCP, to the DpSGXX(X)pS diphosphorylated motif in its substrates. We recovered 27 previously reported SCF(βTrCP) substrates, of which 22 were verified by two independent statistical protocols, thereby confirming the reliability of this approach. In addition to known substrates, we identified 221 proteins that contained the DpSGXX(X)pS motif and also interacted specifically with the WD40 repeats of βTrCP. Thus, with SCF(βTrCP), as the example, we showed that integration of structural information, AP-MS, and degron motif mining constitutes an effective method to screen for substrates of ubiquitin ligases.

Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS)

Protein–protein interactions are fundamental to the understanding of biological processes. Affinity purification coupled to mass spectrometry (AP-MS) is one of the most promising methods for their investigation. Previously, complexes were purified as much as possible, frequently followed by identification of individual gel bands. However, todays mass spectrometers are highly sensitive, and powerful quantitative proteomics strategies are available to distinguish true interactors from background binders. Here we describe a high performance affinity enrichment-mass spectrometry method for investigating protein–protein interactions, in which no attempt at purifying complexes to homogeneity is made. Instead, we developed analysis methods that take advantage of specific enrichment of interactors in the context of a large amount of unspecific background binders. We perform single-step affinity enrichment of endogenously expressed GFP-tagged proteins and their interactors in budding yeast, followed by single-run, intensity-based label-free quantitative LC-MS/MS analysis. Each pull-down contains around 2000 background binders, which are reinterpreted from troubling contaminants to crucial elements in a novel data analysis strategy. First the background serves for accurate normalization. Second, interacting proteins are not identified by comparison to a single untagged control strain, but instead to the other tagged strains. Third, potential interactors are further validated by their intensity profiles across all samples. We demonstrate the power of our AE-MS method using several well-known and challenging yeast complexes of various abundances. AE-MS is not only highly efficient and robust, but also cost effective, broadly applicable, and can be performed in any laboratory with access to high-resolution mass spectrometers.

Spotlite: web application and augmented algorithms for predicting co-complexed proteins from affinity purification--mass spectrometry data

Protein-protein interactions defined by affinity purification and mass spectrometry (APMS) suffer from high false discovery rates. Consequently, lists of potential interactions must be pruned of contaminants before network construction and interpretation, historically an expensive, time-intensive, and error-prone task. In recent years, numerous computational methods were developed to identify genuine interactions from the hundreds of candidates. Here, comparative analysis of three popular algorithms, HGSCore, CompPASS, and SAINT, revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. We improved each algorithm by an average area under a receiver operating characteristics curve increase of 16% by integrating a variety of indirect data known to correlate with established protein-protein interactions, including mRNA coexpression, gene ontologies, domain-domain binding affinities, and homologous protein interactions. Each APMS scoring approach was incorporated into a separate logistic regression model along with the indirect features; the resulting three classifiers demonstrate improved performance on five diverse APMS data sets. To facilitate APMS data scoring within the scientific community, we created Spotlite, a user-friendly and fast web application. Within Spotlite, data can be scored with the augmented classifiers, annotated, and visualized ( http://cancer.unc.edu/majorlab/software.php ). The utility of the Spotlite platform to reveal physical, functional, and disease-relevant characteristics within APMS data is established through a focused analysis of the KEAP1 E3 ubiquitin ligase.

From pathways to networks: connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry

Signal transductions are the basis of biological activities in all living organisms. Studying the signaling pathways, especially under physiological conditions, has become one of the most important facets of modern biological research. During the last decade, MS has been used extensively in biological research and is proven to be effective in addressing important biological questions. Here, we review the current progress in the understanding of signaling networks using MS approaches. We will focus on studies of protein-protein interactions that use affinity purification followed by MS approach. We discuss obstacles to affinity purification, data processing, functional validation, and identification of transient interactions and provide potential solutions for pathway-specific proteomics analysis, which we hope one day will lead to a comprehensive understanding of signaling networks in humans.

Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

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.

Detecting protein-protein interactions/complex components using mass spectrometry coupled techniques

Proteins play important roles in biochemical processes. Most biological functions are realized through protein-protein interactions (PPI). Co-immunoprecipitation is the most straightforward method to detect PPI. With the development of modern mass spectrometry (MS), throughput, sensitivity, and confidence for the detection of PPI can be readily achieved by scaling up traditional antibody-based strategies. Herein, we describe a typical workflow for general PPI detection using mass spectrometry coupled techniques, covering from Co-immunoprecipitation (Co-IP), to gel display, in-gel digestion, liquid chromatography mass spectrometry (LC-MS) analysis, as well as result interpretation and statistic filtering. This protocol provides an overview of the technique as well as practical tips.

SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software

Significance Analysis of INTeractome (SAINT) is a statistical method for probabilistically scoring protein-protein interaction data from affinity purification-mass spectrometry (AP-MS) experiments. The utility of the software has been demonstrated in many protein-protein interaction mapping studies, yet the extensive testing also revealed some practical drawbacks. In this paper, we present a new implementation, SAINTexpress, with simpler statistical model and quicker scoring algorithm, leading to significant improvements in computational speed and sensitivity of scoring. SAINTexpress also incorporates external interaction data to compute supplemental topology-based scores to improve the likelihood of identifying co-purifying protein complexes in a probabilistically objective manner. Overall, these changes are expected to improve the performance and user experience of SAINT across various types of high quality datasets.

BIOLOGICAL SIGNIFICANCE

We present SAINTexpress, an upgraded implementation of Significance Analysis of INTeractome (SAINT) for filtering high confidence interaction data from affinity purification-mass spectrometry (AP-MS) experiments. SAINTexpress features faster computation and incorporation of external data sources into the scoring, improving the performance and user experience of SAINT across various types of datasets. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis

Background

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts.

Results

We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance.

Conclusions

Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT

Significance Analysis of INTeractome (SAINT) is a software package for scoring protein-protein interactions based on label-free quantitative proteomics data (e.g., spectral count or intensity) in affinity purification-mass spectrometry (AP-MS) experiments. SAINT allows bench scientists to select bona fide interactions and remove nonspecific interactions in an unbiased manner. However, there is no 'one-size-fits-all' statistical model for every dataset, since the experimental design varies across studies. Key variables include the number of baits, the number of biological replicates per bait, and control purifications. Here we give a detailed account of input data format, control data, selection of high-confidence interactions, and visualization of filtered data. We explain additional options for customizing the statistical model for optimal filtering in specific datasets. We also discuss a graphical user interface of SAINT in connection to the LIMS system ProHits, which can be installed as a virtual machine on Mac OS X or Windows computers.

Label-free quantitative proteomics trends for protein-protein interactions

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

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.

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.

Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

Mass-spectrometry-based proteomics is continuing to make major contributions to the discovery of fundamental biological processes and, more recently, has also developed into an assay platform capable of measuring hundreds to thousands of proteins in any biological system. The field has progressed at an amazing rate over the past five years in terms of technology as well as the breadth and depth of applications in all areas of the life sciences. Some of the technical approaches that were at an experimental stage back then are considered the gold standard today, and the community is learning to come to grips with the volume and complexity of the data generated. The revolution in DNA/RNA sequencing technology extends the reach of proteomic research to practically any species, and the notion that mass spectrometry has the potential to eventually retire the western blot is no longer in the realm of science fiction. In this review, we focus on the major technical and conceptual developments since 2007 and illustrate these by important recent applications.

Mass spectrometry approaches to study mammalian kinase and phosphatase associated proteins

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

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

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