Interactive proteomics research technologies: recent applications and advances

State of the art in protein-protein interactions within the fungi kingdom

Proteins rarely exert their function by themselves. Protein-protein interactions (PPIs) regulate virtually every biological process that takes place in a cell. Such interactions are targets for new therapeutic agents against all sorts of diseases, through the screening and design of a variety of inhibitors. Here we discuss several aspects of PPIs that contribute to prediction of protein function and drug discovery. As the high-throughput techniques continue to release biological data, targets for fungal therapeutics that rely on PPIs are being proposed worldwide. Computational approaches have reduced the time taken to develop new therapeutic approaches. The near future brings the possibility of developing new PPI and interaction network inhibitors and a revolution in the way we treat fungal diseases.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

Split-Tobacco Etch Virus (Split-TEV) Method in G Protein-Coupled Receptor Interacting Proteins

Split-TEV assay enables the identification of protein-protein interaction in mammalian cells. This method is based on the split of tobacco etch virus (TEV) protease in two fragments, where each fragment is fused to the candidate proteins predicted to interact. If there is indeed an interaction between both proteins, TEV protease reconstitutes its proteolytic activity and this activity is used to induce the expression of some reporter genes. However, some studies have detected unspecific interaction between membrane proteins due to its higher tendency to aggregate. Here we describe a variation of the Split-TEV method developed with the aim to increase the specificity in the study of G protein-coupled receptor (GPCR) interacting proteins. This approach for monitoring interactions between GPCRs is an easy and robust assay and offers good perspectives in drug discovery.

Split Intein-Mediated Protein Ligation for detecting protein-protein interactions and their inhibition

Here, to overcome many limitations accompanying current available methods to detect protein-protein interactions (PPIs), we develop a live cell method called Split Intein-Mediated Protein Ligation (SIMPL). In this approach, bait and prey proteins are respectively fused to an intein N-terminal fragment (IN) and C-terminal fragment (IC) derived from a re-engineered split intein GP41-1. The bait/prey binding reconstitutes the intein, which splices the bait and prey peptides into a single intact protein that can be detected by regular protein detection methods such as Western blot analysis and ELISA, serving as readouts of PPIs. The method is robust and can be applied not only in mammalian cell lines but in animal models such as C. elegans. SIMPL demonstrates high sensitivity and specificity, and enables exploration of PPIs in different cellular compartments and tracking of kinetic interactions. Additionally, we establish a SIMPL ELISA platform that enables high-throughput screening of PPIs and their inhibitors.

Protein-protein interactions are fundamental to the regulation of protein activity and cellular phyisology. Here the authors present Split Intein-Mediated Protein Ligation, which uses bait and prey proteins fused to intein fragments to generate single intact proteins upon interaction.

Identification and visualization of protein binding regions with the ArDock server

ArDock (ardock.ibcp.fr) is a structural bioinformatics web server for the prediction and the visualization of potential interaction regions at protein surfaces. ArDock ranks the surface residues of a protein according to their tendency to form interfaces in a set of predefined docking experiments between the query protein and a set of arbitrary protein probes. The ArDock methodology is derived from large scale cross-docking studies where it was observed that randomly chosen proteins tend to dock in a non-random way at protein surfaces. The method predicts interaction site of the protein, or alternate interfaces in the case of proteins with multiple interaction modes. The server takes a protein structure as input and computes a score for each surface residue. Its output focuses on the interactive visualization of results and on interoperability with other services.

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

Technologies for Proteome-Wide Discovery of Extracellular Host-Pathogen Interactions

Pathogens have evolved unique mechanisms to breach the cell surface barrier and manipulate the host immune response to establish a productive infection. Proteins exposed to the extracellular environment, both cell surface-expressed receptors and secreted proteins, are essential targets for initial invasion and play key roles in pathogen recognition and subsequent immunoregulatory processes. The identification of the host and pathogen extracellular molecules and their interaction networks is fundamental to understanding tissue tropism and pathogenesis and to inform the development of therapeutic strategies. Nevertheless, the characterization of the proteins that function in the host-pathogen interface has been challenging, largely due to the technical challenges associated with detection of extracellular protein interactions. This review discusses available technologies for the high throughput study of extracellular protein interactions between pathogens and their hosts, with a focus on mammalian viruses and bacteria. Emerging work illustrates a rich landscape for extracellular host-pathogen interaction and points towards the evolution of multifunctional pathogen-encoded proteins. Further development and application of technologies for genome-wide identification of extracellular protein interactions will be important in deciphering functional host-pathogen interaction networks, laying the foundation for development of novel therapeutics.

Development of a membrane-anchored ligand and receptor yeast two-hybrid system for ligand-receptor interaction identification

Identifying interactions between ligands and transmembrane receptors is crucial for understanding the endocrine system. However, the present approaches for this purpose are still not capable of high-throughput screening. In this report, a membrane-anchored ligand and receptor yeast two-hybrid (MALAR-Y2H) system was established. In the method, an extracellular ligand is linked with an intracellular split-ubiquitin reporter system via a chimeric transmembrane structure. Meanwhile, the prey proteins of transmembrane receptors are fused to the other half of the split-ubiquitin reporter system. The extracellular interaction of ligands and receptors can lead to the functional recovery of the ubiquitin reporter system in yeast, and eventually lead to the expression of report genes. Consequently, the system can be used to detect the interactions between extracellular ligands and their transmembrane receptors. To test the efficiency and universality of the method, interactions between several pairs of ligands and receptors of mouse were analyzed. The detecting results were shown to be thoroughly consistent with the present knowledge, indicating MALAR-Y2H can be utilized for such purpose with high precision, high efficiency and strong universality. The characteristics of the simple procedure and high-throughput potential make MALAR-Y2H a powerful platform to study protein-protein interaction networks between secreted proteins and transmembrane proteins.

Membrane Yeast Two-Hybrid (MYTH) Mapping of Full-Length Membrane Protein Interactions

Mapping of protein interaction networks is a major strategy for obtaining a global understanding of protein function in cells and represents one of the primary goals of proteomics research. Membrane proteins, which play key roles in human disease and as drug targets, are of considerable interest; however, because of their hydrophobic nature, mapping their interactions presents significant technical challenges and requires the use of special methodological approaches. One powerful approach is the membrane yeast two-hybrid (MYTH) assay, a split-ubiquitin-based system specifically suited to the study of full-length membrane protein interactions in vivo using the yeast Saccharomyces cerevisiae as a host. The system can be used in both low- and high-throughput formats to study proteins from a wide range of different organisms. There are two primary variants of MYTH: integrated (iMYTH), which involves endogenous expression and tagging of baits and is suitable for studying native yeast membrane proteins, and traditional (tMYTH), which involves ectopic plasmid-based expression of tagged baits and is suitable for studying membrane proteins from other organisms. Here we provide an introduction to the MYTH assay, including both the iMYTH and tMYTH variants. MYTH can be set up in almost any laboratory environment, with results typically obtainable within 4 to 6 wk.

Bimolecular Fluorescence Complementation (BiFC) Analysis: Advances and Recent Applications for Genome-Wide Interaction Studies

Complex protein networks are involved in nearly all cellular processes. To uncover these vast networks of protein interactions, various high-throughput screening technologies have been developed. Over the last decade, bimolecular fluorescence complementation (BiFC) assay has been widely used to detect protein-protein interactions (PPIs) in living cells. This technique is based on the reconstitution of a fluorescent protein in vivo. Easy quantification of the BiFC signals allows effective cell-based high-throughput screenings for protein binding partners and drugs that modulate PPIs. Recently, with the development of large screening libraries, BiFC has been effectively applied for genome-wide PPI studies and has uncovered novel protein interactions, providing new insight into protein functions. In this review, we describe the development of reagents and methods used for BiFC-based screens in yeast, plants, and mammalian cells. We also discuss the advantages and drawbacks of these methods and highlight the application of BiFC in large-scale studies.

Enzyme inhibitor discovery by activity-based protein profiling

Eukaryotic and prokaryotic organisms possess huge numbers of uncharacterized enzymes. Selective inhibitors offer powerful probes for assigning functions to enzymes in native biological systems. Here, we discuss how the chemical proteomic platform activity-based protein profiling (ABPP) can be implemented to discover selective and in vivo-active inhibitors for enzymes. We further describe how these inhibitors have been used to delineate the biochemical and cellular functions of enzymes, leading to the discovery of metabolic and signaling pathways that make important contributions to human physiology and disease. These studies demonstrate the value of selective chemical probes as drivers of biological inquiry.

Phage display library screening for identification of interacting protein partners

Phage display is a versatile high-throughput screening method employed to understand and improve the chemical biology, be it production of human monoclonal antibodies or identification of interacting protein partners. A majority of cell proteins operate in a concerted fashion either by stable or transient interactions. Such interactions can be mediated by recognition of small amino acid sequence motifs on the protein surface. Phage display can play a crucial role in identification of such motifs. This report describes the use of phage display for the identification of high affinity sequence motifs that could be responsible for interactions with a target (bait) protein.

Detection of Aβ-interacting proteins via a novel Aβ-adsorbents that use immobilized regular comb polymer

A detailed study of individual Aβ-interacting proteins has always been a difficult task because Aβ has a wide range of molecular weights and can easily form aggregates. In this study, we established a novel method for isolating Aβ-interacting proteins by utilizing regular comb polymer immobilized on Sepharose CL-4B. To achieve site-directed ligation of Aβ, a cysteine residue was added at the N-terminus of Aβ. Asp and Asp12, which have 2 and 13 carboxyl groups, respectively, were selected as the carriers for the regular comb polymer. Firstly, the N-termini of Asp and Asp12 were immobilized on Sepharose CL-4B. Next, modified Aβ molecules were coupled to the carboxyl groups of Asp and Asp12 using bromoethylamine as a spacer. To obtain homogeneous comb polymer, the efficiency of the reaction was controlled during the synthesis process. Thioflavin T staining indicated that homogeneous Aβ was achieved. The prepared Aβ-adsorbents were used to isolate Aβ-interacting proteins from mice brain extracts. The results showed that the adsorption capacity of the Aβ-adsorbents for proteins in mice brain extracts increased with the ages of the animals. SDS-PAGE analysis of the Aβ-interacting proteins showed that many kinds of brain proteins were selectively adsorbed by the Aβ adsorbents, and the levels of some of these proteins varied with the ages of the animals. The results indicated that Aβ-interacting proteins could be successfully obtained through the use of immobilized comb polymer. Similar method could also be used to isolate other amyloid-interacting proteins.

An optimized optogenetic clustering tool for probing protein interaction and function

The Arabidopsis photoreceptor cryptochrome 2 (CRY2) was previously used as an optogenetic module, allowing spatiotemporal control of cellular processes with light. Here we report the development of a new CRY2-derived optogenetic module, 'CRY2olig', which induces rapid, robust, and reversible protein oligomerization in response to light. Using this module, we developed a novel protein interaction assay, Light-Induced Co-clustering, that can be used to interrogate protein interaction dynamics in live cells. In addition to use probing protein interactions, CRY2olig can also be used to induce and reversibly control diverse cellular processes with spatial and temporal resolution. Here we demonstrate disrupting clathrin-mediated endocytosis and promoting Arp2/3-mediated actin polymerization with light. These new CRY2-based approaches expand the growing arsenal of optogenetic strategies to probe cellular function.

Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein-protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Interplay of PKA and Rac: fine-tuning of Rac localization and signaling

Cellular membrane receptors sense environmental changes and relay the reshaped signal through spatially and temporally organized protein-protein interactions (PPI). Many of such PPI are transient and occur in a certain cell-dependent context. Molecular switches such as kinases and GTPases are engaged in versatile PPI. Recently, we have identified dynamic interaction and reciprocal regulation of cAMP-dependent protein kinase A (PKA) and Rho-GTPase Rac signaling. We demonstrated that GTP-activated Rac acts as a dual kinase-tuning scaffold for p21-activated kinase (PAK) and PKA activities. We showed that receptor-triggered PKA trans-phosphorylation of GTP-Rac-organized PAK contributes to elevations of nuclear Erk1/2 signaling and proliferation. We discuss these recent observations and we provide additional insights how the cAMP-PKA axis might also participate in the regulation of Rac localization.

Evolution of the cognitive proteome: from static to dynamic network models

Integrative analysis of the neuronal synapse proteome has uncovered an evolutionarily conserved signalling complex that underpins the cognitive capabilities of the brain. Highly dynamic, cell type specific and intricately regulated, the synaptic proteome presents many challenges to systems biology approaches, yet this is likely to be the best route to unlock a new generation of neuroscience research and CNS drug development that society so urgently demands. Most systems biology approaches today have focussed on exploiting protein-protein interaction data to their fullest extent within static interaction models. These have revealed structure-function relationships within the protein network, uncovered new candidate genes for genetic studies and drug research and development and finally provided a means to study the evolution of the system. The rapid maturation of medium and high-throughput biochemical technologies means that dissecting the synapse proteome's dynamic complexity is fast becoming a reality. Here we look at these new challenges and explore rule-based modelling as a basis for a new generation of synaptic models.

Investigation of stable and transient protein-protein interactions: Past, present, and future

This article presents an overview of the literature and a review of recent advances in the analysis of stable and transient protein-protein interactions (PPIs) with a focus on their function within cells, organs, and organisms. The significance of PTMs within the PPIs is also discussed. We focus on methods to study PPIs and methods of detecting PPIs, with particular emphasis on electrophoresis-based and MS-based investigation of PPIs, including specific examples. The validation of PPIs is emphasized and the limitations of the current methods for studying stable and transient PPIs are discussed. Perspectives regarding PPIs, with focus on bioinformatics and transient PPIs are also provided.

Unraveling the different proteomic platforms

This review is addressed to scientists working outside the field of proteomics and wishes to shed a light on the possibility offered by the latest proteomics strategies. Bottom-up and top-down platforms are critically examined outlining advantages and limitations of their application to qualitative and quantitative investigations. Discovery, directed and targeted proteomics as different options for the management of the MS instrument are defined emphasizing their integration in the experimental plan to accomplish meaningful results. The issue of data validation is analyzed and discussed. The most common qualitative proteomic platforms are described, with a particular emphasis on enrichment methods to elucidate PTMs codes (i.e. ubiquitin and histone codes). Label-free and labeled methods for relative and absolute quantification are critically compared. The possible contribution of proteomics platforms to the transition from structural proteomics to functional proteomics (study of the functional connections between different proteins) and to the challenging system biology (integrated study of all the functional cellular functions) is also briefly discussed.

Recent advances in Schistosoma genomics

Schistosome research has entered the genomic era with the publications reporting the Schistosoma mansoni and Schistosoma japonicum genomes. Schistosome genomics is motivated by the need for new control tools. However, much can also be learned about the biology of Schistosoma, which is a tractable experimental model. In this article, we review the recent achievements in the field of schistosome research and discuss future perspectives on genomics and how it can be integrated in a usable format, on the genetic mapping and how it has improved the genome assembly and provided new research approaches, on how epigenetics provides interesting insights into the biology of the species and on new functional genomics tools that will contribute to the understanding of the function of genes, many of which are parasite- or taxon specific.

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.

A modification of the split-tobacco etch virus method for monitoring interactions between membrane proteins in mammalian cells

Despite progress in the development of methods to monitor protein interactions, studies of interactions between membrane proteins in mammalian cells remain challenging. Protein complementation assays (PCAs) are commonly used to study interactions between proteins due to their simplicity. They are based on interaction-mediated reconstitution of a reporter protein, which can be easily monitored. Recently, a protein complementation method named split-TEV (tobacco etch virus) has been developed and is based on the functional reconstitution of TEV protease and subsequent proteolytic-mediated activation of reporters. In this work, we have developed a modification of the split-TEV method to study the interactions between membrane proteins with increased specificity. This assay was validated by addressing the interactions between different membrane proteins, including G protein-coupled receptors (GPCRs) and ion channels. By comparing it with another PCA, we found that this new method showed a higher sensitivity.

MAPPIT: a protein interaction toolbox built on insights in cytokine receptor signaling

MAPPIT (mammalian protein-protein interaction trap) is a two-hybrid interaction mapping technique based on functional complementation of a type I cytokine receptor signaling pathway. Over the last decade, the technology has been extended into a platform of complementary assays for the detection of interactions among proteins and between chemical compounds and proteins, and for the identification of small molecules that interfere with protein-protein interactions. Additionally, several screening approaches have been developed to broaden the utility of the platform. In this review we provide an overview of the different components of the MAPPIT toolbox and highlight a number of applications in interactomics, drug screening and compound target profiling.

Categorizing biases in high-confidence high-throughput protein-protein interaction data sets

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.