Recent advances in charting protein-protein interaction: mass spectrometry-based approaches

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

The Complementarity of Nuclear Magnetic Resonance and Native Mass Spectrometry in Probing Protein-Protein Interactions

Nuclear magnetic resonance (NMR) and native mass spectrometry (MS) are mature physicochemical techniques with long histories and important applications. NMR spectroscopy provides detailed information about the structure, dynamics, interactions, and chemical environment of biomolecules. MS is an effective approach for determining the mass of biomolecules with high accuracy, sensitivity, and speed. The two techniques offer unique advantages and provide solid tools for structural biology. In the present review, we discuss their individual merits in the context of their applications to structural studies in biology with specific focus on protein interactions and evaluate their limitations. We provide specific examples in which these techniques can complement each other, providing new information on the same scientific case. We discuss how the field may develop and what challenges are expected in the future. Overall, the combination of NMR and MS plays an increasingly important role in integrative structural biology, assisting scientists in deciphering the three-dimensional structure of composite macromolecular assemblies.

ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, personalized medicine, systems biology and biomedical applications. By combining MS with different proteomics approaches such as immunopurification MS, immunopeptidomics, and total protein proteomics, researchers can gain insights into protein-protein interactions, immune responses, cellular processes, and disease mechanisms. The application of MS-based proteomics in these areas continues to advance our understanding of protein function, cellular signaling, and complex biological systems. Data analysis for mass spectrometry is a critical process that includes identifying and quantifying proteins and peptides and exploring biological functions for these proteins in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analysis. ProtPipe provides downstream analysis including identifying differential abundance proteins and peptides, pathway enrichment analysis, protein-protein interaction analysis, and MHC1-peptide binding affinity. ProtPipe generates annotated tables and diagnostic visualizations from statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. ProtPipe is well-documented open-source software and is available at https://github.com/NIH-CARD/ProtPipe , accompanied by a web interface.

Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions

INTRODUCTION

Radiological/nuclear accidents, hostile military activity, or terrorist strikes have the potential to expose a large number of civilians and military personnel to high doses of radiation resulting in the development of acute radiation syndrome and delayed effects of exposure. Thus, there is an urgent need for sensitive and specific assays to assess the levels of radiation exposure to individuals. Such radiation exposures are expected to alter primary cellular proteomic processes, resulting in multifaceted biological responses.

AREAS COVERED

This article covers the application of proteomics, a promising and fast developing technology based on quantitative and qualitative measurements of protein molecules for possible rapid measurement of radiation exposure levels. Recent advancements in high-resolution chromatography, mass spectrometry, high-throughput, and bioinformatics have resulted in comprehensive (relative quantitation) and precise (absolute quantitation) approaches for the discovery and accuracy of key protein biomarkers of radiation exposure. Such proteome biomarkers might prove useful for assessing radiation exposure levels as well as for extrapolating the pharmaceutical dose of countermeasures for humans based on efficacy data generated using animal models.

EXPERT OPINION

The field of proteomics promises to be a valuable asset in evaluating levels of radiation exposure and characterizing radiation injury biomarkers.

Protein-protein interactions in plant antioxidant defense

The regulation of reactive oxygen species (ROS) levels in plants is ensured by mechanisms preventing their over accumulation, and by diverse antioxidants, including enzymes and nonenzymatic compounds. These are affected by redox conditions, posttranslational modifications, transcriptional and posttranscriptional modifications, Ca²⁺, nitric oxide (NO) and mitogen-activated protein kinase signaling pathways. Recent knowledge about protein-protein interactions (PPIs) of antioxidant enzymes advanced during last decade. The best-known examples are interactions mediated by redox buffering proteins such as thioredoxins and glutaredoxins. This review summarizes interactions of major antioxidant enzymes with regulatory and signaling proteins and their diverse functions. Such interactions are important for stability, degradation and activation of interacting partners. Moreover, PPIs of antioxidant enzymes may connect diverse metabolic processes with ROS scavenging. Proteins like receptor for activated C kinase 1 may ensure coordination of antioxidant enzymes to ensure efficient ROS regulation. Nevertheless, PPIs in antioxidant defense are understudied, and intensive research is required to define their role in complex regulation of ROS scavenging.

Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning

Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functional similarity among proteins, network distance to proteins that are part of safety panels used in preclinical drug development, set of network descriptors in the form of degree and betweenness centrality measurements, and conservation. This diverse set of descriptors were used to generate predictors based on different machine learning classifiers ranging from specific models for individual ADR to higher levels of abstraction as per MEDDRA hierarchy such as system organ class. The results obtained from the different machine-learning classifiers, namely, support vector machine, random forest, and neural network were further analyzed as a meta-predictor exploiting three different voting systems, namely, jury vote, consensus vote, and red flag, obtaining different models for each of the ADRs in analysis. The level of accuracy of the predictors justifies the identification of problematic protein targets both at the level of individual ADR as well as a set of related ADRs grouped in common system organ classes. As an example, the prediction of ventricular tachycardia achieved an accuracy and precision of 0.83 and 0.90, respectively, and a Matthew correlation coefficient of 0.70. We believe that this approach is a good complement to the existing methodologies devised to foresee potential liabilities in preclinical drug discovery. The method is available through the DocTOR utility at GitHub (https://github.com/cristian931/DocTOR).

Identification of Tomato Proteins That Interact With Replication Initiator Protein (Rep) of the Geminivirus TYLCV

Geminiviruses are plant-infecting DNA viruses that reshape the intracellular environment of their host in order to create favorable conditions for viral replication and propagation. Viral manipulation is largely mediated via interactions between viral and host proteins. Identification of this protein network helps us to understand how these viruses manipulate their host and therefore provides us potentially with novel leads for resistance against this class of pathogens, as genetic variation in the corresponding plant genes could subvert viral manipulation. Different studies have already yielded a list of host proteins that interact with one of the geminiviral proteins. Here, we use affinity purification followed by mass spectrometry (AP-MS) to further expand this list of interacting proteins, focusing on an important host (tomato) and the Replication initiator protein (Rep, AL1, C1) from Tomato yellow leaf curl virus (TYLCV). Rep is the only geminiviral protein proven to be essential for geminiviral replication and it forms an integral part of viral replisomes, a protein complex that consists of plant and viral proteins that allows for viral DNA replication. Using AP-MS, fifty-four 'high confidence' tomato proteins were identified that specifically co-purified with Rep. For two of them, an unknown EWS-like RNA-binding protein (called Geminivirus Rep interacting EWS-like protein 1 or GRIEP1) and an isoform of the THO complex subunit 4A (ALY1), we were able to confirm this interaction with Rep in planta using a second method, bimolecular fluorescence complementation (BiFC). The THO subunit 4 is part of the THO/TREX (TRanscription-EXport) complex, which controls RNA splicing and nuclear export of mRNA to the cytoplasm and is also connected to plant disease resistance. This work represents the first step towards characterization of novel host factors with a putative role in the life cycle of TYLCV and possibly other geminiviruses.

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry

The quantitative multiplexing capacity of isobaric tandem mass tags (TMT) has increased the throughput of affinity purification mass spectrometry (AP-MS) to characterize protein interaction networks of immunoprecipitated bait proteins. However, variable bait levels between replicates can convolute interactor identification. We compared the Student's t-test and Pearson's R correlation as methods to generate t-statistics and assessed the significance of interactors following TMT-AP-MS. Using a simple linear model of protein recovery in immunoprecipitates to simulate reporter ion ratio distributions, we found that correlation-derived t-statistics protect against bait variance while robustly controlling type I errors (false positives). We experimentally determined the performance of these two approaches for determining t-statistics under two experimental conditions: irreversible prey association to the Hsp40 mutant DNAJB8^H31Q followed by stringent washing, and reversible association to 14-3-3ζ with gentle washing. Correlation-derived t-statistics performed at least as well as Student's t-statistics for each sample and with substantial improvement in performance for experiments with high bait-level variance. Deliberately varying bait levels over a large range fails to improve selectivity but does increase the robustness between runs. The use of correlation-derived t-statistics should improve identification of interactors using TMT-AP-MS. Data are available via ProteomeXchange with identifier PXD016613.

Next Generation Techniques for Determination of Protein-Protein Interactions: Beyond the Crystal Structure

PURPOSE OF REVIEW

We discuss recent advancements in structural biology methods for investigating sites of protein-protein interactions. We will inform readers outside the field of structural biology about techniques beyond crystallography, and how these different technologies can be utilized for drug development.

RECENT FINDINGS

Advancements in cryo-electron microscopy (cryoEM) and micro-electron diffraction (microED) may change how we view atomic resolution structural biology, such that well-ordered macrocrystals of protein complexes are not required for interface identification. However, some drug discovery applications, such as lead peptide compound generation, may not require atomic resolution; mass spectrometry techniques can provide an expedited path to generation of lead compounds. New crosslinking compounds, more user-friendly data analysis, and novel protocols such as protein painting can advance drug discovery programs, even in the absence of atomic resolution structural data. Finally, artificial intelligence and machine learning methods, while never truly replacing experimental methods, may provide rational ways to stratify potential druggable regions identified with mass spectrometry into higher and lower priority candidates.

SUMMARY

Electron diffraction of nanocrystals combines the benefits of both x-ray diffraction and cryoEM, and may prove to be the next generation of atomic resolution protein-protein interface identification. However, in situations such as peptide drug discovery, mass spectrometry techniques supported by advancements in computational methods will likely prove sufficient to support drug discovery efforts. In addition, these methods can be significantly faster than any crystallographic or cryoEM methods for identification of interacting regions.

A Case Study on the Keap1 Interaction with Peptide Sequence Epitopes Selected by the Peptidomic mRNA Display

Many protein-protein and peptide-protein interactions (PPIs) play key roles in the regulation of biological functions, and therefore, the modulation of PPIs has become an attractive target of new drug development. Although a number of PPIs have already been identified, over 100 000 unknown PPIs are predicted to exist. To uncover such unknown PPIs, it is important to devise a conceptually distinct method from that of currently available methods. Herein, an mRNA display by using a total RNA library derived from various human tissues, which serves as a unique method to physically isolate peptide epitopes that potentially bind to a target protein of interest, is reported. In this study, selection was performed against Kelch-like ECH-associated protein (Keap1) as a model target protein, leading to a peptide epitope originating from astrotactin-1 (ASTN1). It turned out that this ASTN1 peptide was able to interact with Keap1 more strongly than that with a known peptide derived from Nrf2; a well-known, naturally occurring Keap1 binder. This case study demonstrates the applicability of peptidomic mRNA display for the rapid exploration of consensus binding peptide motifs and the potential for the discovery of unknown PPIs with other proteins of interest.

The RTK Interactome: Overview and Perspective on RTK Heterointeractions

Receptor tyrosine kinases (RTKs) play important roles in cell growth, motility, differentiation, and survival. These single-pass membrane proteins are grouped into subfamilies based on the similarity of their extracellular domains. They are generally thought to be activated by ligand binding, which promotes homodimerization and then autophosphorylation in trans. However, RTK interactions are more complicated, as RTKs can interact in the absence of ligand and heterodimerize within and across subfamilies. Here, we review the known cross-subfamily RTK heterointeractions and their possible biological implications, as well as the methodologies which have been used to study them. Moreover, we demonstrate how thermodynamic models can be used to study RTKs and to explain many of the complicated biological effects which have been described in the literature. Finally, we discuss the concept of the RTK interactome: a putative, extensive network of interactions between the RTKs. This RTK interactome can produce unique signaling outputs; can amplify, inhibit, and modify signaling; and can allow for signaling backups. The existence of the RTK interactome could provide an explanation for the irreproducibility of experimental data from different studies and for the failure of some RTK inhibitors to produce the desired therapeutic effects. We argue that a deeper knowledge of RTK interactome thermodynamics can lead to a better understanding of fundamental RTK signaling processes in health and disease. We further argue that there is a need for quantitative, thermodynamic studies that probe the strengths of the interactions between RTKs and their ligands and between different RTKs.

Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

Discovering cellular protein-protein interactions: Technological strategies and opportunities

The analysis of protein interaction networks is one of the key challenges in the study of biology. It connects genotypes to phenotypes, and disruption often leads to diseases. Hence, many technologies have been developed to study protein-protein interactions (PPIs) in a cellular context. The expansion of the PPI technology toolbox however complicates the selection of optimal approaches for diverse biological questions. This review gives an overview of the binary and co-complex technologies, with the former evaluating the interaction of two co-expressed genetically tagged proteins, and the latter only needing the expression of a single tagged protein or no tagged proteins at all. Mass spectrometry is crucial for some binary and all co-complex technologies. After the detailed description of the different technologies, the review compares their unique specifications, advantages, disadvantages, and applicability, while highlighting opportunities for further advancements.

Affinity capture of polyribosomes followed by RNAseq (ACAPseq), a discovery platform for protein-protein interactions

Defining protein-protein interactions (PPIs) is central to the biological sciences. Here, we present a novel platform - Affinity Capture of Polyribosomes followed by RNA sequencing (ACAPseq) - for identifying PPIs. ACAPseq harnesses the power of massively parallel RNA sequencing (RNAseq) to quantify the enrichment of polyribosomes based on the affinity of their associated nascent polypeptides for an immobilized protein 'bait'. This method was developed and tested using neonatal mouse brain polyribosomes and a variety of extracellular domains as baits. Of 92 baits tested, 25 identified one or more binding partners that appear to be biologically relevant; additional candidate partners remain to be validated. ACAPseq can detect binding to targets that are present at less than 1 part in 100,000 in the starting polyribosome preparation. One of the observed PPIs was analyzed in detail, revealing the mode of homophilic binding for Protocadherin-9 (PCDH9), a non-clustered Protocadherin family member.

Determining the Effect of Catechins on SOD1 Conformation and Aggregation by Ion Mobility Mass Spectrometry Combined with Optical Spectroscopy

The aggregation of Cu,Zn-superoxide dismutase (SOD1) plays an important role in the etiology of amyotrophic lateral sclerosis (ALS). For the disruption of ALS progression, discovering new drugs or compounds that can prevent SOD1 aggregation is important. In this study, ESI-MS was used to investigate the interaction of catechins and SOD1. The noncovalent complex of catechins that interact with SOD1 was found and retained in the gas phase under native ESI-MS condition. The conformation changes of SOD1 after binding with catechins were also explored via traveling wave ion mobility (IM) spectrometry. Epigallocatechin gallate (EGCG) can stabilize SOD1 conformation against unfolding in three catechins. To further evaluate the efficacy of EGCG, we monitored the fluorescence changes of dimer E₂,E₂,-SOD1(apo-SOD1, E:empty) with and without ligands under denaturation conditions, and found that EGCG can inhibit apo-SOD1 aggregation. In addition, the circular dichroism spectra of the samples showed that EGCG can decrease the β-sheet content of SOD1, which can produce aggregates. These results indicated that orthogonal separation dimension in the gas-phase IM coupled with ESI-MS (ESI-IM-MS) can potentially provide insight into the interaction between SOD1 and small molecules. The advantage is that it dramatically decreases the analysis time. Meantime, optical spectroscopy techniques can be used to confirm ESI-IM-MS results. Graphical Abstract ᅟ.

Neuregulin 1 discovered as a cleavage target for the HCV NS3/4A protease by a microfluidic membrane protein array

The hepatitis C virus (HCV) non-structural protein 3 (NS3) is essential for HCV maturation. The NS3/4A protease is a target for several HCV treatments and is a well-known target for HCV drug discovery. The protein is membrane associated and thus probably interacts with other membrane proteins. However, the vast majority of known NS3 host partners are soluble proteins rather than membrane proteins, most likely due to lack of appropriate platforms for their discovery. Utilization of an integrated microfluidics platform enables analysis of membrane proteins in their native form. We screened over 2800 membrane proteins for interaction with NS3 and 90 previously unknown interactions were identified. Of these, several proteins were selected for validation by co-immunoprecipitation and for NS3 proteolytic activity. Bearing in mind the considerable number of interactions formed, together with the popularity of NS3/4A protease as a drug target, it was striking to note its lack of proteolytic activity. Only a single protein, Neuregulin1, was observed to be cleaved, adding to the 3 known NS3/4A cleavage targets. Neuregulin1 participates in neural proliferation. Recent studies have shown its involvement in HCV infection and hepatocellular carcinoma. We showed that NS3/4A triggers an increase in neuregulin1 mRNA levels in HCV infected cells. Despite this increase, its protein concentration is decreased due to proteolytic cleavage. Additionally, its EGF-like domain levels were increased, possibly explaining the ErbB2 and EGFR upregulation in HCV infected cells. The newly discovered protein interactions may provide insights into HCV infection mechanisms and potentially provide new therapeutic targets against HCV.

MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform

Summary

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments.

Availability and Implementation

MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT.

Supplementary information

Supplementary data are available at Bioinformatics online.

Quantitative Tandem Affinity Purification, an Effective Tool to Investigate Protein Complex Composition in Plant Hormone Signaling: Strigolactones in the Spotlight

Phytohormones tightly regulate plant growth by integrating changing environmental and developmental cues. Although the key players have been identified in many plant hormonal pathways, the molecular mechanisms and mode of action of perception and signaling remain incompletely resolved. Characterization of protein partners of known signaling components provides insight into the formed protein complexes, but, unless quantification is involved, does not deliver much, if any, information about the dynamics of the induced or disrupted protein complexes. Therefore, in proteomics research, the discovery of what actually triggers, regulates or interrupts the composition of protein complexes is gaining importance. Here, tandem affinity purification coupled to mass spectrometry (TAP-MS) is combined with label-free quantification (LFQ) to a highly valuable tool to detect physiologically relevant, dynamic protein-protein interactions in Arabidopsis thaliana cell cultures. To demonstrate its potential, we focus on the signaling pathway of one of the most recently discovered phytohormones, strigolactones.

Alcohol-Induced Hepatic Steatosis: A Comparative Study to Identify Possible Indicator(s) of Alcoholic Fatty Liver Disease

Background

Fatty liver is an early sign of both nonalcoholic and alcoholic fatty liver diseases. Ethanol feeding using a Lieber-DeCarli liquid diet (LD) model which contains 35% fat to rats or mice is a well-established model for alcoholic fatty liver. However, LD diet alone can also induce fatty liver and its differential metabolic profile may be able to differentiate steatosis induced by LD versus LD plus ethanol.

Purpose

We investigated the lipidomic differences in the livers of Sprague-Dawley (SD) rats fed a pellet diet (PD), LD and liquid ethanol diet (LED) for six weeks.

Study Design

Male Sprague Dawley rats were fed with nonalcoholic diets PD, LD or LED (ethanol in LD) for six weeks. Lipids were extracted and analyzed by nuclear magnetic resonance (NMR)- based metabolomics. The NMR data obtained was analyzed by multivariate Principal Component Analysis (PCA) and Spotfire DecisionSite 9.0 software to compare PD versus LD and LD versus LED groups.

Results

PCA of the NMR spectral data of livers of both comparisons showed a clear separation of PD from LD group and LD from LED group indicating differences in lipid profiles which corresponded with changes in total lipid weights. LD showed increases for cholesterol, esterified cholesterol, cholesterol acetate and triglycerides with decreases for fatty acyl chain, diallylic and allylic protons, while the LED showed increases in esterified cholesterol, cholesterol acetate, fatty acid methyl esters, allylic protons and some triglyceride protons with decreases in free cholesterol and phosphatidylcholine (PC).

Conclusion

Our data suggest that altered lipid signature or PC levels could be an indicator to differentiate between nonalcoholic versus alcoholic fatty liver.

The computational prediction of protein assemblies

The function of proteins in the cell is almost always mediated by their interaction with different partners, including other proteins, nucleic acids or small organic molecules. The ability of identifying all of them is an essential step in our quest for understanding life at the molecular level. The inference of the protein complex composition and of its molecular details can also provide relevant clues for the development and the design of drugs. In this short review, I will discuss the computational aspects of the analysis and prediction of protein-protein assemblies and discuss some of the most recent developments as seen in the last Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment.

Peptide retention time prediction

Most methods for interpreting data from shotgun proteomics experiments are to large degree dependent on being able to predict properties of peptide-ions. Often such predicted properties are limited to molecular mass and fragment spectra, but here we put focus on a perhaps underutilized property, a peptide's chromatographic retention time. We review a couple of different principles of retention time prediction,and their applications within computational proteomics. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:615-623, 2017.

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.

Definition of a RACK1 Interaction Network in Drosophila melanogaster Using SWATH-MS

Receptor for Activated protein C kinase 1 (RACK1) is a scaffold protein that has been found in association with several signaling complexes, and with the 40S subunit of the ribosome. Using the model organism Drosophila melanogaster, we recently showed that RACK1 is required at the ribosome for internal ribosome entry site (IRES)-mediated translation of viruses. Here, we report a proteomic characterization of the interactome of RACK1 in Drosophila S2 cells. We carried out Label-Free quantitation using both Data-Dependent and Data-Independent Acquisition (DDA and DIA, respectively) and observed a significant advantage for the Sequential Window Acquisition of all THeoretical fragment-ion spectra (SWATH) method, both in terms of identification of interactants and quantification of low abundance proteins. These data represent the first SWATH spectral library available for Drosophila and will be a useful resource for the community. A total of 52 interacting proteins were identified, including several molecules involved in translation such as structural components of the ribosome, factors regulating translation initiation or elongation, and RNA binding proteins. Among these 52 proteins, 15 were identified as partners by the SWATH strategy only. Interestingly, these 15 proteins are significantly enriched for the functions translation and nucleic acid binding. This enrichment reflects the engagement of RACK1 at the ribosome and highlights the added value of SWATH analysis. A functional screen did not reveal any protein sharing the interesting properties of RACK1, which is required for IRES-dependent translation and not essential for cell viability. Intriguingly however, 10 of the RACK1 partners identified restrict replication of Cricket paralysis virus (CrPV), an IRES-containing virus.

Medium-scale Preparation of Drosophila Embryo Extracts for Proteomic Experiments

Analysis of protein-protein interactions (PPIs) has become an indispensable approach to study biological processes and mechanisms, such as cell signaling, organism development, and disease. It is often desirable to obtain PPI information using in vivo material, to gain the most natural and unbiased view of the interaction networks. The fruit fly Drosophila melanogaster is an excellent platform to study PPIs in vivo, and lends itself to straightforward approaches to isolating material for biochemical experiments. In particular, fruit fly embryos represent a convenient type of tissue to study PPIs, due to the ease of collecting animals at this developmental stage and the fact that the majority of proteins are expressed in embryogenesis, thus providing a relevant environment to reveal most PPIs. Here we present a protocol for collection of Drosophila embryos at medium scale (0.5-1 g), which is an ideal amount for a wide range of proteomic applications, including analysis of PPIs by affinity purification-mass spectrometry (AP-MS). We describe our designs for 1 L and 5 L cages for embryo collections that can be easily and inexpensively set up in any laboratory. We also provide a general protocol for embryo collection and protein extraction to generate lysates that can be directly used in downstream applications such as AP-MS. Our goal is to provide an accessible means for all researchers to carry out the analyses of PPIs in vivo.

Single-Step Affinity Purification of ERK Signaling Complexes Using the Streptavidin-Binding Peptide (SBP) Tag

Elucidation of biological functions of signaling proteins is facilitated by studying their protein-protein interaction networks. Affinity purification combined with mass spectrometry (AP-MS) has become a favorite method to study protein complexes. Here we describe a procedure for single-step purification of ERK (Rolled) and associated proteins from Drosophila cultured cells. The use of the streptavidin-binding peptide (SBP) tag allows for a highly efficient isolation of native ERK signaling complexes, which are suitable for subsequent analysis by mass spectrometry. Our analysis of the ERK interactome has identified both known and novel signaling components. This method can be easily adapted for SBP-based purification of protein complexes in any expression system.

Chickpea transcription factor CaTLP1 interacts with protein kinases, modulates ROS accumulation and promotes ABA-mediated stomatal closure

Tubby and Tubby-like proteins (TLPs), in mammals, play critical roles in neural development, while its function in plants is largely unknown. We previously demonstrated that the chickpea TLP, CaTLP1, participates in osmotic stress response and might be associated with ABA-dependent network. However, how CaTLP1 is connected to ABA signaling remains unclear. The CaTLP1 was found to be engaged in ABA-mediated gene expression and stomatal closure. Complementation of the yeast yap1 mutant with CaTLP1 revealed its role in ROS scavenging. Furthermore, complementation of Arabidopsis attlp2 mutant displayed enhanced stress tolerance, indicating the functional conservation of TLPs across the species. The presence of ABA-responsive element along with other motifs in the proximal promoter regions of TLPs firmly established their involvement in stress signalling pathways. The CaTLP1 promoter driven GUS expression was restricted to the vegetative organs, especially stem and rosette leaves. Global protein expression profiling of wild-type, attlp2 and complemented Arabidopsis plants revealed 95 differentially expressed proteins, presumably involved in maintaining physiological and biological processes under dehydration. Immunoprecipitation assay revealed that protein kinases are most likely to interact with CaTLP1. This study provides the first demonstration that the TLPs act as module for ABA-mediated stomatal closure possibly via interaction with protein kinase.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Quantitative GTPase Affinity Purification Identifies Rho Family Protein Interaction Partners

Although Rho GTPases are essential molecular switches involved in many cellular processes, an unbiased experimental comparison of their interaction partners was not yet performed. Here, we develop quantitative GTPase affinity purification (qGAP) to systematically identify interaction partners of six Rho GTPases (Cdc42, Rac1, RhoA, RhoB, RhoC, and RhoD), depending on their nucleotide loading state. The method works with cell line or tissue-derived protein lysates in combination with SILAC-based or label-free quantification, respectively. We demonstrate that qGAP identifies known and novel binding partners that can be validated in an independent assay. Our interaction network for six Rho GTPases contains many novel binding partners, reveals highly promiscuous interaction of several effectors, and mirrors evolutionary relationships among Rho GTPases.

Technical advances in proteomics: new developments in data-independent acquisition

The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

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.

Probing protein interactions in living mammalian cells on a microtubule bench

Microtubules are μm-long cylinders of about 25 nm in diameter which are present in the cytoplasm of eukaryotic cells. Here, we have developed a new method which uses these cylindrical structures as platforms to detect protein interactions in cells. The principle is simple: a protein of interest used as bait is brought to microtubules by fusing it to Tau, a microtubule-associated protein. The presence of a protein prey on microtubules then reveals an interaction between bait and prey. This method requires only a conventional optical microscope and straightforward fluorescence image analysis for detection and quantification of protein interactions. To test the reliability of this detection scheme, we used it to probe the interactions among three mRNA-binding proteins in both fixed and living cells and compared the results to those obtained by pull-down assays. We also tested whether the molecular interactions of Cx43, a membrane protein, can be investigated with this system. Altogether, the results indicate that microtubules can be used as platforms to detect protein interactions in mammalian cells, which should provide a basis for investigating pathogenic protein interactions involved in human diseases.

Identification of Evening Complex Associated Proteins in Arabidopsis by Affinity Purification and Mass Spectrometry

Many species possess an endogenous circadian clock to synchronize internal physiology with an oscillating external environment. In plants, the circadian clock coordinates growth, metabolism and development over daily and seasonal time scales. Many proteins in the circadian network form oscillating complexes that temporally regulate myriad processes, including signal transduction, transcription, protein degradation and post-translational modification. In Arabidopsis thaliana, a tripartite complex composed of EARLY FLOWERING 4 (ELF4), EARLY FLOWERING 3 (ELF3), and LUX ARRHYTHMO (LUX), named the evening complex, modulates daily rhythms in gene expression and growth through transcriptional regulation. However, little is known about the physical interactions that connect the circadian system to other pathways. We used affinity purification and mass spectrometry (AP-MS) methods to identify proteins that associate with the evening complex in A. thaliana. New connections within the circadian network as well as to light signaling pathways were identified, including linkages between the evening complex, TIMING OF CAB EXPRESSION1 (TOC1), TIME FOR COFFEE (TIC), all phytochromes and TANDEM ZINC KNUCKLE/PLUS3 (TZP). Coupling genetic mutation with affinity purifications tested the roles of phytochrome B (phyB), EARLY FLOWERING 4, and EARLY FLOWERING 3 as nodes connecting the evening complex to clock and light signaling pathways. These experiments establish a hierarchical association between pathways and indicate direct and indirect interactions. Specifically, the results suggested that EARLY FLOWERING 3 and phytochrome B act as hubs connecting the clock and red light signaling pathways. Finally, we characterized a clade of associated nuclear kinases that regulate circadian rhythms, growth, and flowering in A. thaliana. Coupling mass spectrometry and genetics is a powerful method to rapidly and directly identify novel components and connections within and between complex signaling pathways.

Characterization of the Cardiac Overexpression of HSPB2 Reveals Mitochondrial and Myogenic Roles Supported by a Cardiac HspB2 Interactome

Small Heat Shock Proteins (sHSPs) are molecular chaperones that transiently interact with other proteins, thereby assisting with quality control of proper protein folding and/or degradation. They are also recruited to protect cells from a variety of stresses in response to extreme heat, heavy metals, and oxidative-reductive stress. Although ten human sHSPs have been identified, their likely diverse biological functions remain an enigma in health and disease, and much less is known about non-redundant roles in selective cells and tissues. Herein, we set out to comprehensively characterize the cardiac-restricted Heat Shock Protein B-2 (HspB2), which exhibited ischemic cardioprotection in transgenic overexpressing mice including reduced infarct size and maintenance of ATP levels. Global yeast two-hybrid analysis using HspB2 (bait) and a human cardiac library (prey) coupled with co-immunoprecipitation studies for mitochondrial target validation revealed the first HspB2 “cardiac interactome” to contain many myofibril and mitochondrial-binding partners consistent with the overexpression phenotype. This interactome has been submitted to the Biological General Repository for Interaction Datasets (BioGRID). A related sHSP chaperone HspB5 had only partially overlapping binding partners, supporting specificity of the interactome as well as non-redundant roles reported for these sHSPs. Evidence that the cardiac yeast two-hybrid HspB2 interactome targets resident mitochondrial client proteins is consistent with the role of HspB2 in maintaining ATP levels and suggests new chaperone-dependent functions for metabolic homeostasis. One of the HspB2 targets, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), has reported roles in HspB2 associated phenotypes including cardiac ATP production, mitochondrial function, and apoptosis, and was validated as a potential client protein of HspB2 through chaperone assays. From the clientele and phenotypes identified herein, it is tempting to speculate that small molecule activators of HspB2 might be deployed to mitigate mitochondrial related diseases such as cardiomyopathy and neurodegenerative disease.

A Scalable Genome-Editing-Based Approach for Mapping Multiprotein Complexes in Human Cells

Conventional affinity purification followed by mass spectrometry (AP-MS) analysis is a broadly applicable method used to decipher molecular interaction networks and infer protein function. However, it is sensitive to perturbations induced by ectopically overexpressed target proteins and does not reflect multilevel physiological regulation in response to diverse stimuli. Here, we developed an interface between genome editing and proteomics to isolate native protein complexes produced from their natural genomic contexts. We used CRISPR/Cas9 and TAL effector nucleases (TALENs) to tag endogenous genes and purified several DNA repair and chromatin-modifying holoenzymes to near homogeneity. We uncovered subunits and interactions among well-characterized complexes and report the isolation of MCM8/9, highlighting the efficiency and robustness of the approach. These methods improve and simplify both small- and large-scale explorations of protein interactions as well as the study of biochemical activities and structure-function relationships.

High-throughput methods for identification of protein-protein interactions involving short linear motifs

Interactions between modular domains and short linear motifs (3–10 amino acids peptide stretches) are crucial for cell signaling. The motifs typically reside in the disordered regions of the proteome and the interactions are often transient, allowing for rapid changes in response to changing stimuli. The properties that make domain-motif interactions suitable for cell signaling also make them difficult to capture experimentally and they are therefore largely underrepresented in the known protein-protein interaction networks. Most of the knowledge on domain-motif interactions is derived from low-throughput studies, although there exist dedicated high-throughput methods for the identification of domain-motif interactions. The methods include arrays of peptides or proteins, display of peptides on phage or yeast, and yeast-two-hybrid experiments. We here provide a survey of scalable methods for domain-motif interaction profiling. These methods have frequently been applied to a limited number of ubiquitous domain families. It is now time to apply them to a broader set of peptide binding proteins, to provide a comprehensive picture of the linear motifs in the human proteome and to link them to their potential binding partners. Despite the plethora of methods, it is still a challenge for most approaches to identify interactions that rely on post-translational modification or context dependent or conditional interactions, suggesting directions for further method development.

A Double-Barrel Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) System to Quantify 96 Interactomes per Day

The field of proteomics has evolved hand-in-hand with technological advances in LC-MS/MS systems, now enabling the analysis of very deep proteomes in a reasonable time. However, most applications do not deal with full cell or tissue proteomes but rather with restricted subproteomes relevant for the research context at hand or resulting from extensive fractionation. At the same time, investigation of many conditions or perturbations puts a strain on measurement capacity. Here, we develop a high-throughput workflow capable of dealing with large numbers of low or medium complexity samples and specifically aim at the analysis of 96-well plates in a single day (15 min per sample). We combine parallel sample processing with a modified liquid chromatography platform driving two analytical columns in tandem, which are coupled to a quadrupole Orbitrap mass spectrometer (Q Exactive HF). The modified LC platform eliminates idle time between measurements, and the high sequencing speed of the Q Exactive HF reduces required measurement time. We apply the pipeline to the yeast chromatin remodeling landscape and demonstrate quantification of 96 pull-downs of chromatin complexes in about 1 day. This is achieved with only 500 μg input material, enabling yeast cultivation in a 96-well format. Our system retrieved known complex-members and the high throughput allowed probing with many bait proteins. Even alternative complex compositions were detectable in these very short gradients. Thus, sample throughput, sensitivity and LC/MS-MS duty cycle are improved severalfold compared with established workflows. The pipeline can be extended to different types of interaction studies and to other medium complexity proteomes.

From the static interactome to dynamic protein complexes: Three challenges

Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein–protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.

The emerging role of native mass spectrometry in characterizing the structure and dynamics of macromolecular complexes

Mass spectrometry (MS) is a powerful tool for determining the mass of biomolecules with high accuracy and sensitivity. MS performed under so-called "native conditions" (native MS) can be used to determine the mass of biomolecules that associate noncovalently. Here we review the application of native MS to the study of protein-ligand interactions and its emerging role in elucidating the structure of macromolecular assemblies, including soluble and membrane protein complexes. Moreover, we discuss strategies aimed at determining the stoichiometry and topology of subunits by inducing partial dissociation of the holo-complex. We also survey recent developments in "native top-down MS", an approach based on Fourier Transform MS, whereby covalent bonds are broken without disrupting non-covalent interactions. Given recent progress, native MS is anticipated to play an increasingly important role for researchers interested in the structure of macromolecular complexes.

Proteins interacting with cloning scars: a source of false positive protein-protein interactions

A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.

Studying macromolecular complex stoichiometries by peptide-based mass spectrometry

A majority of cellular functions are carried out by macromolecular complexes. A host of biochemical and spectroscopic methods exists to characterize especially protein/protein complexes, however there has been a lack of a universal method to determine protein stoichiometries. Peptide‐based MS, especially as a complementary method to the MS analysis of intact protein complexes, has now been developed to a point where it can be employed to assay protein stoichiometries in a routine manner. While the experimental demands are still significant, peptide‐based MS has been successfully applied to analyze stoichiometries for a variety of protein complexes from very different biological backgrounds. In this review, we discuss the requirements especially for targeted MS acquisition strategies to be used in this context, with a special focus on the interconnected experimental aspects of sample preparation, protein digestion, and peptide stability. In addition, different strategies for the introduction of quantitative peptide standards and their suitability for different scenarios are compared.

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.

Affinity purification-mass spectrometry and network analysis to understand protein-protein interactions

By determining protein-protein interactions in normal, diseased and infected cells, we can improve our understanding of cellular systems and their reaction to various perturbations. In this protocol, we discuss how to use data obtained in affinity purification-mass spectrometry (AP-MS) experiments to generate meaningful interaction networks and effective figures. We begin with an overview of common epitope tagging, expression and AP practices, followed by liquid chromatography-MS (LC-MS) data collection. We then provide a detailed procedure covering a pipeline approach to (i) pre-processing the data by filtering against contaminant lists such as the Contaminant Repository for Affinity Purification (CRAPome) and normalization using the spectral index (SIN) or normalized spectral abundance factor (NSAF); (ii) scoring via methods such as MiST, SAInt and CompPASS; and (iii) testing the resulting scores. Data formats familiar to MS practitioners are then transformed to those most useful for network-based analyses. The protocol also explores methods available in Cytoscape to visualize and analyze these types of interaction data. The scoring pipeline can take anywhere from 1 d to 1 week, depending on one's familiarity with the tools and data peculiarities. Similarly, the network analysis and visualization protocol in Cytoscape takes 2-4 h to complete with the provided sample data, but we recommend taking days or even weeks to explore one's data and find the right questions.

Protein-protein interaction predictions using text mining methods

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.

A mass spectrometry view of stable and transient protein interactions

Through an impressive range of dynamic interactions, proteins succeed to carry out the majority of functions in a cell. These temporally and spatially regulated interactions provide the means through which one single protein can perform diverse functions and modulate different cellular pathways. Understanding the identity and nature of these interactions is therefore critical for defining protein functions and their contribution to health and disease processes. Here, we provide an overview of workflows that incorporate immunoaffinity purifications and quantitative mass spectrometry (frequently abbreviated as IP-MS or AP-MS) for characterizing protein-protein interactions. We discuss experimental aspects that should be considered when optimizing the isolation of a protein complex. As the presence of nonspecific associations is a concern in these experiments, we discuss the common sources of nonspecific interactions and present label-free and metabolic labeling mass spectrometry-based methods that can help determine the specificity of interactions. The effective regulation of cellular pathways and the rapid reaction to various environmental stresses rely on the formation of stable, transient, and fast-exchanging protein-protein interactions. While determining the exact nature of an interaction remains challenging, we review cross-linking and metabolic labeling approaches that can help address this important aspect of characterizing protein interactions and macromolecular assemblies.