An experimentally derived confidence score for binary protein-protein interactions

An abscisic acid-responsive protein interaction network for sucrose non-fermenting related kinase1 in abiotic stress response

Yeast Snf1 (Sucrose non-fermenting1), mammalian AMPK (5′ AMP-activated protein kinase) and plant SnRK1 (Snf1-Related Kinase1) are conserved heterotrimeric kinase complexes that re-establish energy homeostasis following stress. The hormone abscisic acid (ABA) plays a crucial role in plant stress response. Activation of SnRK1 or ABA signaling results in overlapping transcriptional changes, suggesting these stress pathways share common targets. To investigate how SnRK1 and ABA interact during stress response in Arabidopsis thaliana, we screened the SnRK1 complex by yeast two-hybrid against a library of proteins encoded by 258 ABA-regulated genes. Here, we identify 125 SnRK1- interacting proteins (SnIPs). Network analysis indicates that a subset of SnIPs form signaling modules in response to abiotic stress. Functional studies show the involvement of SnRK1 and select SnIPs in abiotic stress responses. This targeted study uncovers the largest set of SnRK1 interactors, which can be used to further characterize SnRK1 role in plant survival under stress.

Carianopol et al. construct a detailed protein interaction network for the SnRK1 kinase complex to investigate the interaction of SnRK1 and ABA during stress response. They identify 125 proteins that interact with SnRK1, which can be used further to characterise the role of SnRK1 in plant survival under stress.

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.

MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity

Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.

Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID)

Protein-protein interaction data is fundamental in molecular biology, and numerous online databases provide access to this data. However, the huge quantity, complexity, and variety of PPI data can be overwhelming, and rather than helping to address research problems, the data may add to their complexity and reduce interpretability. This protocol focuses on solutions for some of the main challenges of using PPI data, including accessing data, ensuring relevance by integrating useful annotations, and improving interpretability. While the issues are generic, we highlight how to perform such operations using Integrated Interactions Database (IID; http://ophid.utoronto.ca/iid ).

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

BACKGROUND

The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

OBJECTIVE

Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

METHODS

We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

RESULTS

We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

CONCLUSION

PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.

Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network

Many traits are complex, depending non-additively on variant combinations. Even in model systems, such as the yeast S. cerevisiae, carrying out the high-order variant-combination testing needed to dissect complex traits remains a daunting challenge. Here, we describe “X-gene” genetic analysis (XGA), a strategy for engineering and profiling highly combinatorial gene perturbations. We demonstrate XGA on yeast ABC transporters by engineering 5,353 strains, each deleted for a random subset of 16 transporters, and profiling each strain’s resistance to 16 compounds. XGA yielded 85,648 genotype-to-resistance observations, revealing high-order genetic interactions for 13 of the 16 transporters studied. Neural networks yielded intuitive functional models and guided exploration of fluconazole resistance, which was influenced non-additively by five genes. Together, our results showed that highly combinatorial genetic perturbation can functionally dissect complex traits, supporting pursuit of analogous strategies in human cells and other model systems.

Celaj et al. introduce “X-gene” genetic analysis (XGA), a strategy for modeling complex systems by engineering and profiling highly combinatorial genetic perturbations. They apply XGA to 16 yeast ABC transporters, revealing many high-order genetic interactions. Neural network models yielded intuitive functional models and illuminated an ABC transporter influence network, supporting application of XGA to other organisms and processes.

A multi-scale coevolutionary approach to predict interactions between protein domains

Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Maximizing binary interactome mapping with a minimal number of assays

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.

Automated feature engineering improves prediction of protein-protein interactions

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

Applications of Protein Fragment Complementation Assays for Analyzing Biomolecular Interactions and Biochemical Networks in Living Cells

Protein-protein interactions (PPIs) are indispensable for the dynamic assembly of multiprotein complexes that are central players of nearly all of the intracellular biological processes, such as signaling pathways, metabolic pathways, formation of intracellular organelles, establishment of cytoplasmic skeletons, etc. Numerous approaches have been invented to study PPIs both in vivo and in vitro, including the protein-fragment complementation assay (PCA), which is a widely applied technology to study PPIs and biomolecular interactions. PCA is a technology based on the expression of the bait and prey proteins in fusion with two complementary reporter protein fragments, respectively, that will reassemble when in close proximity. The reporter protein can be the enzymes or fluorescent proteins. Recovery of the enzymatic activity or fluorescent signal can be the indicator of PPI between the bait and prey proteins. Significant effort has been invested in developing many derivatives of PCA, along with various applications, in order to address specific questions. Therefore, a prompt review of these applications is important. In this review, we will categorize these applications according to the scenarios that the PCAs were applied and expect to provide a reference guideline for the future selection of PCA methods in solving a specific problem.

Atypical function of a centrosomal module in WNT signalling drives contextual cancer cell motility

Centrosomes control cell motility, polarity and migration that is thought to be mediated by their microtubule-organizing capacity. Here we demonstrate that WNT signalling drives a distinct form of non-directional cell motility that requires a key centrosome module, but not microtubules or centrosomes. Upon exosome mobilization of PCP-proteins, we show that DVL2 orchestrates recruitment of a CEP192-PLK4/AURKB complex to the cell cortex where PLK4/AURKB act redundantly to drive protrusive activity and cell motility. This is mediated by coordination of formin-dependent actin remodelling through displacement of cortically localized DAAM1 for DAAM2. Furthermore, abnormal expression of PLK4, AURKB and DAAM1 is associated with poor outcomes in breast and bladder cancers. Thus, a centrosomal module plays an atypical function in WNT signalling and actin nucleation that is critical for cancer cell motility and is associated with more aggressive cancers. These studies have broad implications in how contextual signalling controls distinct modes of cell migration.

Centrosomes function in cell migration by organizing microtubules. Here, Luo et al. surprisingly show that centrosome proteins also control migration after recruitment by Wnt-PCP proteins to the cell cortex, leading to actin remodelling and protrusive activity relevant to aggressive cancer motility.

The Plasmodium falciparum MESA erythrocyte cytoskeleton-binding (MEC) motif binds to erythrocyte ankyrin

The MESA erythrocyte cytoskeleton binding (MEC) motif is a 13-amino acid sequence found in 14 exported Plasmodium falciparum proteins. First identified in the P. falciparum Mature-parasite-infected Erythrocyte Surface Antigen (MESA), the MEC motif is sufficient to target proteins to the infected red blood cell cytoskeleton. To identify host cell targets, purified MESA MEC motif was incubated with a soluble extract from uninfected erythrocytes, precipitated and subjected to mass spectrometry. The most abundant co-purifying protein was erythrocyte ankyrin (ANK1). A direct interaction between the MEC motif and ANK1 was independently verified using co-purification experiments, the split-luciferase assay, and the yeast two-hybrid assay. A systematic mutational analysis of the core MEC motif demonstrated a critical role for the conserved aspartic acid residue at the C-terminus of the MEC motif for binding to both erythrocyte inside-out vesicles and to ANK1. Using a panel of ANK1 constructs, the MEC motif binding site was localized to the ZU5^C domain, which has no known function. The MEC motif had no impact on erythrocyte deformability when introduced into uninfected erythrocyte ghosts, suggesting the MEC motif's primary function is to target exported proteins to the cytoskeleton. Finally, we show that PF3D7_0402100 (PFD0095c) binds to ANK1 and band 4.1, likely through its MEC and PHIST motifs, respectively. In conclusion, we have provided multiple lines of evidence that the MEC motif binds to erythrocyte ANK1.

Straightforward Protein-Protein Interaction Interface Mapping via Random Mutagenesis and Mammalian Protein Protein Interaction Trap (MAPPIT)

The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.

Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer

Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction network is essential for understanding the functional organization of the proteome. Numerous small-scale studies and a couple of large-scale ones have elucidated a fraction of the estimated 300,000 binary protein-protein interactions in Arabidopsis. In this study, we provide evidence that a docking algorithm has the ability to identify real interactions using both experimentally determined and predicted protein structures. We ranked 0.91 million interactions generated by all possible pairwise combinations of 1,346 predicted structure models from an Arabidopsis predicted "structure-ome" and found a significant enrichment of real interactions for the top-ranking predicted interactions, as shown by cosubcellular enrichment analysis and yeast two-hybrid validation. Our success rate for computationally predicted, structure-based interactions was 63% of the success rate for published interactions naively tested using the yeast two-hybrid system and 2.7 times better than for randomly picked pairs of proteins. This study provides another perspective in interactome exploration and biological network reconstruction using protein structural information. We have made these interactions freely accessible through an improved Arabidopsis Interactions Viewer and have created community tools for accessing these and ∼2.8 million other protein-protein and protein-DNA interactions for hypothesis generation by researchers worldwide. The Arabidopsis Interactions Viewer is freely available at http://bar.utoronto.ca/interactions2/.

Options and Considerations When Using a Yeast One-Hybrid System

Comprehensive mapping of protein-DNA interactions is essential to uncover the mechanisms involved in gene regulation. However, the data generated has been sparse given the number of regulatory elements and transcription factors (TFs) encoded in the genomes of metazoan organisms. Yeast one-hybrid (Y1H) assays provide a powerful "DNA-centered" method, complementary to "TF-centered" methods such as chromatin immunoprecipitation, to identify the TFs that can bind a DNA sequence of interest. Here, we present different technical variations that should be considered when using a Y1H system, including the type of DNA sequence to test, source of TF clones, as well as types of vectors and screening format. Finally, we discuss limitations of the assay and future challenges.

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis

Background

Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark.

Results

Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks.

Conclusions

IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2659-y) contains supplementary material, which is available to authorized users.

Protein interactome mapping in Caenorhabditis elegans

The systematic identification of all protein–protein interactions that take place in an organism (the ‘interactome’) is an important goal in modern biology. The nematode Caenorhabditis elegans was one of the first multicellular models for which a proteome-wide interactome mapping project was initiated. Most Caenorhabditis elegans interactome mapping efforts have utilized the yeast two-hybrid system, yielding an extensive binary interactome, while recent developments in mass spectrometry-based approaches hold great potential for further improving our understanding of protein interactome networks in a multicellular context. For example, methods like co-fractionation, proximity labeling, and tissue-specific protein purification not only identify protein–protein interactions, but have the potential to provide crucial insight into when and where interactions take place. Here we review current standards and recent improvements in protein interaction mapping in C. elegans.

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Protein interactome mapping is a key method to understanding biology.

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C. elegans was one of the first multicellular organisms whose interactome was mapped.

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Renewed efforts and improved technologies are needed to complete the interactome.

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Novel technologies are poised to provide spatial information to interactome networks.

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.

Making the Case for Functional Proteomics

"Making the Case for Functional Proteomics" first differentiates the Functional Proteome from the products of genetic protein expression. Qualitatively, the prevalence of posttranslational modifications (PTMs) virtually insures that individual, functional proteins do not equate to their genetic expression counterparts. Quantitatively, considering the frequency of PTMs and a conservative estimate of the number of functional entities arising from protein interactions, the size of the Functional Proteome exceeds that of the human genome by at least two orders of magnitude. The human genome does not, cannot, map the Functional Proteome. Further, the collective genome of the human microbiome dwarfs the human genome. With these facts established, "Making the Case…" proceeds to examine Functional Proteomics (of which both "gene expression" and "epigenetics" are but parts of a larger whole) within the context of Systems Biology, concluding that functionally related networks comprise the dominant motif for biological activity. Creating just such a network focus is essential in not only expanding basic knowledge but also in applying that knowledge in the pragmatic efforts of drug and biomarker development. Outlines for development of drugs and biomarkers, as well as the realization of precision medicine, within a functional proteomics-based, network motif are provided. The chapter proceeds to asses both the knowledge base and the tools to fully embrace Functional Proteomics. Given the decades-long infatuation with the reductionism of genomics, it is not surprising that both the proteomics knowledge base and tools are assessed as poor to fair. However, even a minor shift in research funding and a renewed challenge to methods developers will rapidly improve the current situation. Adoption of the included "Roadmap" will realistically make the twenty-first century the century of a long-awaited revolution in biology.

Two protein/protein interaction assays in one go

Discovering and characterizing protein–protein interactions (PPI s) that contribute to cellular homeostasis, development, and disease is a key priority in proteomics. Numerous assays for protein–protein interactions have been developed, but each one comes with its own strengths, weaknesses, and false‐positive/false‐negative rates. Therefore, it seems rather intuitive that combining multiple assays is beneficial for robust and reliable discovery of interactions. Along those lines, in their recent study, Wanker and colleagues (Trepte et al , 2018 ) combined two complementary and quantitative interaction assays in one pot. One assay is luminescence‐based and depends on protein proximity in living cells, while the other relies on formation of more stable complexes detected by co‐precipitation with a luminescence‐based readout, which facilitates confident identification and quantitation of interactions in high throughput.

LuTHy: a double-readout bioluminescence-based two-hybrid technology for quantitative mapping of protein-protein interactions in mammalian cells

Information on protein-protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double-readout bioluminescence-based two-hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence-based co-precipitation (LuC). The double-readout procedure detects interactions with higher sensitivity than traditional single-readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease-causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult-onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease-causing missense mutations L115R and ∆L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease-associated mutations impair protein activity in biological systems.

High-Quality Yeast-2-Hybrid Interaction Network Mapping

In this article, we describe a Y2H interaction mapping protocol for systematically screening medium-to-large collections of open reading frames (ORFs) against each other. The protocol is well suited for analysis of focused networks, for modules of interest, assembling genome-scale interactome maps, and investigating host-microbe interactions. The four-step high-throughput protocol has a demonstrated low false-discovery rate that is essential for producing meaningful network maps. Following the assembly of yeast expression clones from an existing ORFeome collection, we describe in detail the four-step procedure that begins with the primary screen using minipools, followed by secondary verification of primary positives, identification of candidate interaction pairs by sequencing, and a final verification step using fresh inoculants. In addition to this core protocol, aspects of network mapping and quality control will be discussed briefly. © 2018 by John Wiley & Sons, Inc.

An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders

Identifying disease-associated missense mutations remains a challenge, especially in large-scale sequencing studies. Here we establish an experimentally and computationally integrated approach to investigate the functional impact of missense mutations in the context of the human interactome network and test our approach by analyzing ~2,000 de novo missense mutations found in autism subjects and their unaffected siblings. Interaction-disrupting de novo missense mutations are more common in autism probands, principally affect hub proteins, and disrupt a significantly higher fraction of hub interactions than in unaffected siblings. Moreover, they tend to disrupt interactions involving genes previously implicated in autism, providing complementary evidence that strengthens previously identified associations and enhances the discovery of new ones. Importantly, by analyzing de novo missense mutation data from six disorders, we demonstrate that our interactome perturbation approach offers a generalizable framework for identifying and prioritizing missense mutations that contribute to the risk of human disease.

High-Confidence Interactome for RNF41 Built on Multiple Orthogonal Assays

Ring finger protein 41 (RNF41) is an E3 ubiquitin ligase involved in the ubiquitination and degradation of many proteins including ErbB3 receptors, BIRC6, and parkin. Next to this, RNF41 regulates the intracellular trafficking of certain JAK2-associated cytokine receptors by ubiquitinating and suppressing USP8, which, in turn, destabilizes the ESCRT-0 complex. To further elucidate the function of RNF41 we used different orthogonal approaches to reveal the RNF41 protein complex: affinity purification-mass spectrometry, BioID, and Virotrap. We combined these results with known data sets for RNF41 obtained with microarray MAPPIT and Y2H screens. This way, we establish a comprehensive high-resolution interactome network comprising 175 candidate protein partners. To remove potential methodological artifacts from this network, we distilled the data into a high-confidence interactome map by retaining a total of 19 protein hits identified in two or more of the orthogonal methods. AP2S1, a novel RNF41 interaction partner, was selected from this high-confidence interactome for further functional validation. We reveal a role for AP2S1 in leptin and LIF receptor signaling and show that RNF41 stabilizes and relocates AP2S1.

An extracellular network of Arabidopsis leucine-rich repeat receptor kinases

Multicellular organisms receive extracellular signals at the surface of a cell by using receptors. The extracellular domains (ECDs) of cell surface receptors serve as interaction platforms, and as regulatory modules of receptor activation1,2. Understanding how interactions between ECDs produce signal-competent receptor complexes is challenging because of their low biochemical tractability3,4. In plants, discovery of ECD interactions is complicated by the massive expansion of receptor families, which creates tremendous potential for changeover in receptor interactions5. The largest of these families in Arabidopsis thaliana consists of 225 evolutionarily-related leucine-rich repeat receptor kinases (LRR-RKs)5, that function in microbe sensing, cell expansion, stomata development and stem cell maintenance6–9. While the principles governing LRR-RK signalling activation are emerging1,10, the systems-level organization of this family of proteins is totally unexplored. To address this, we interrogated 40,000 potential ECD interactions via a sensitized high-throughput interaction assay3, and produced an LRR-based Cell Surface Interaction network (CSI^LRR) comprising 567 interactions. To demonstrate the power of CSI^LRR for detecting biologically relevant interactions, we predicted and validated the function of uncharacterized LRR-RKs in plant growth and immunity. In addition, we show that CSI^LRR operates as a unified regulatory network in which the LRR-RKs most critical for its overall structure are required to prevent aberrant signalling of receptors that are several network-steps away. Thus, plants have evolved LRR-RK networks to process extracellular signals into carefully balanced responses.

Legionella effector AnkX interacts with host nuclear protein PLEKHN1

Background

The intracellular bacterial pathogen Legionella pneumophila proliferates in human alveolar macrophages, resulting in a severe pneumonia termed Legionnaires’ disease. Throughout the course of infection, L. pneumophila remains enclosed in a specialized membrane compartment that evades fusion with lysosomes. The pathogen delivers over 300 effector proteins into the host cell, altering host pathways in a manner that sets the stage for efficient pathogen replication. The L. pneumophila effector protein AnkX targets host Rab GTPases and functions in preventing fusion of the Legionella-containing vacuole with lysosomes. However, the current understanding of AnkX’s interaction with host proteins and the means through which it exerts its cellular function is limited.

Results

Here, we investigated the protein interaction network of AnkX by using the nucleic acid programmable protein array (NAPPA), a high-density platform comprising 10,000 unique human ORFs. This approach facilitated the discovery of PLEKHN1 as a novel interaction partner of AnkX. We confirmed this interaction through multiple independent in vitro pull-down, co-immunoprecipitation, and cell-based assays. Structured illumination microscopy revealed that endogenous PLEKHN1 is found in the nucleus and on vesicular compartments, whereas ectopically produced AnkX co-localized with lipid rafts at the plasma membrane. In mammalian cells, HaloTag-AnkX co-localized with endogenous PLEKHN1 on vesicular compartments. A central fragment of AnkX (amino acids 491–809), containing eight ankyrin repeats, extensively co-localized with endogenous PLEKHN1, indicating that this region may harbor a new function. Further, we found that PLEKHN1 associated with multiple proteins involved in the inflammatory response.

Conclusions

Altogether, our study provides evidence that in addition to Rab GTPases, the L. pneumophila effector AnkX targets nuclear host proteins and suggests that AnkX may have novel functions related to manipulating the inflammatory response.

Electronic supplementary material

The online version of this article (10.1186/s12866-017-1147-7) contains supplementary material, which is available to authorized users.

Analyses of Protein Interaction Networks Using Computational Tools

The knowledge of protein-protein interactions (PPIs) and PPI networks (PPINs) is the key to starting to understand the biological processes inside the cell. Many computational tools have been designed to help explore PPIs and PPINs, such as those for interaction detection, reliability assessment and interaction network construction. Here, the application of computational tools is reviewed from three perspectives: PPI database construction, PPI prediction, and interaction network construction and analysis. This overview will provide researchers guidance on choosing appropriate methods for exploring PPIs.

Protein Interactomics by Two-Hybrid Methods

Comprehensive identification of direct, physical interactions between biological macromolecules, such as protein-protein, protein-DNA, and protein-RNA interactions, is critical for our understanding of the function of gene products as well as the global organization and interworkings of various molecular machines within the cell. The accurate and comprehensive detection of direct interactions, however, remains a huge challenge due to the inherent structural complexity arising from various post-transcriptional and translational modifications coupled with huge heterogeneity in concentration, affinity, and subcellular location differences existing for any interacting molecules. This has created a need for developing multiple orthogonal and complementary assays for detecting various types of biological interactions. In this introduction, we discuss the methods developed for measuring different types of molecular interactions with an emphasis on direct protein-protein interactions, critical issues for generating high-quality interactome datasets, and the insights into biological networks and human diseases that current interaction mapping efforts provide. Further, we will discuss what future might lie ahead for the continued evolution of two-hybrid methods and the role of interactomics for expanding the advancement of biomedical science.

Quantitative Profiling of Chaperone/Client Interactions with LUMIER Assay

Chaperones associate with hundreds or thousands of diverse client proteins and regulate their function. Chaperone/client interactions are generally very transient and involve a highly orchestrated assembly and disassembly of regulatory co-factors. This poses specific challenges for identifying and characterizing these interactions in a scalable and sensitive manner. LUMIER assay, which takes advantage of the high sensitivity and linear range of luminescence-based detection, has proven to be an ideal assay to quantitatively profile chaperone/client interactions in a high-throughput manner. This article provides step-by-step instructions for quantitatively profiling these interactions with LUMIER.

KISS: A Mammalian Two-Hybrid Method for In Situ Analysis of Protein-Protein Interactions

KISS (KInase Substrate Sensor) is a recently developed two-hybrid technology that allows in situ analysis of protein-protein interactions in intact mammalian cells. In this method, which is derived from MAPPIT (mammalian protein-protein interaction trap), the bait protein is coupled to the kinase domain of TYK2, while the prey protein is fused to a fragment of the gp130 cytokine receptor chain. Bait and prey interaction leads to phosphorylation of the gp130 anchor by TYK2, followed by recruitment and activation of STAT3, resulting in transcription of a STAT3-dependent reporter system. This approach enables the identification of interactions between proteins, including transmembrane and cytosolic proteins, and their modulation in response to physiological or pharmacological challenges. Here, we describe a detailed step-by-step protocol for the detection of an interaction between two proteins of interest using KISS.

Protein-protein interactions: detection, reliability assessment and applications

Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.

A Bayesian approach for estimating protein-protein interactions by integrating structural and non-structural biological data

Accurate elucidation of genome wide protein-protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.

High-throughput characterization of protein-protein interactions by reprogramming yeast mating

High-throughput methods for screening protein-protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination of Saccharomyces cerevisiae can be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship between mating efficiency and protein binding strength for interactions with K_ds ranging from below 500 pM to above 300 μM. Using induced chromosomal translocation to pair barcodes representing binding proteins, thousands of distinct interactions can be screened in a single pot. We demonstrate the ability to characterize protein interaction networks in a modifiable environment by introducing a soluble peptide that selectively disrupts a subset of interactions in a representative network by up to 800-fold. SynAg enables the high-throughput, quantitative characterization of protein-protein interaction networks in a fully defined extracellular environment at a library-on-library scale.

More than Enzymes That Make or Break Cyclic Di-GMP-Local Signaling in the Interactome of GGDEF/EAL Domain Proteins of Escherichia coli

The bacterial second messenger bis-(3'-5')-cyclic diguanosine monophosphate (c-di-GMP) ubiquitously promotes bacterial biofilm formation. Intracellular pools of c-di-GMP seem to be dynamically negotiated by diguanylate cyclases (DGCs, with GGDEF domains) and specific phosphodiesterases (PDEs, with EAL or HD-GYP domains). Most bacterial species possess multiple DGCs and PDEs, often with surprisingly distinct and specific output functions. One explanation for such specificity is "local" c-di-GMP signaling, which is believed to involve direct interactions between specific DGC/PDE pairs and c-di-GMP-binding effector/target systems. Here we present a systematic analysis of direct protein interactions among all 29 GGDEF/EAL domain proteins of Escherichia coli Since the effects of interactions depend on coexpression and stoichiometries, cellular levels of all GGDEF/EAL domain proteins were also quantified and found to vary dynamically along the growth cycle. Instead of detecting specific pairs of interacting DGCs and PDEs, we discovered a tightly interconnected protein network of a specific subset or "supermodule" of DGCs and PDEs with a coregulated core of five hyperconnected hub proteins. These include the DGC/PDE proteins representing the c-di-GMP switch that turns on biofilm matrix production in E. coli Mutants lacking these core hub proteins show drastic biofilm-related phenotypes but no changes in cellular c-di-GMP levels. Overall, our results provide the basis for a novel model of local c-di-GMP signaling in which a single strongly expressed master PDE, PdeH, dynamically eradicates global effects of several DGCs by strongly draining the global c-di-GMP pool and thereby restricting these DGCs to serving as local c-di-GMP sources that activate specific colocalized effector/target systems.IMPORTANCE c-di-GMP signaling in bacteria is believed to occur via changes in cellular c-di-GMP levels controlled by antagonistic and potentially interacting pairs of diguanylate cyclases (DGCs) and c-di-GMP phosphodiesterases (PDEs). Our systematic analysis of protein-protein interaction patterns of all 29 GGDEF/EAL domain proteins of E. coli, together with our measurements of cellular c-di-GMP levels, challenges both aspects of this current concept. Knocking out distinct DGCs and PDEs has drastic effects on E. coli biofilm formation without changing the cellular c-di-GMP level. In addition, rather than generally coming in interacting DGC/PDE pairs, a subset of DGCs and PDEs operates as central interaction hubs in a larger "supermodule," with other DGCs and PDEs behaving as "lonely players" without contacts to other c-di-GMP-related enzymes. On the basis of these data, we propose a novel concept of "local" c-di-GMP signaling in bacteria with multiple enzymes that make or break the second messenger c-di-GMP.

Fusion of expression values and protein interaction information using multi-objective optimization for improving gene clustering

One of the crucial problems in the field of functional genomics is to identify a set of genes which are responsible for a particular cellular mechanism. The current work explores the usage of a multi-objective optimization based genetic clustering technique to classify genes into groups with respect to their functional similarities and biological relevance. Our contribution is two-fold: firstly a new quality measure to compute the goodness of gene-clusters namely protein-protein interaction confidence score is developed. This utilizes the confidence scores of the protein-protein interaction networks to measure the similarity between genes of a particular cluster with respect to their biochemical protein products. Secondly, a multi-objective based clustering approach is developed which intelligently uses integrated information of expression values of microarray dataset and protein-protein interaction confidence scores to select both statistically and biologically relevant genes. For that very purpose, some biological cluster validity indices, viz. biological homogeneity index and protein-protein interaction confidence score, along with two traditional internal cluster validity indices, viz. fuzzy partition coefficient and Pakhira-Bandyopadhyay-Maulik-index, are simultaneously optimized during the clustering process. Experimental results on three real-life gene expression datasets show that the addition of new objective capturing protein-protein interaction information aids in clustering the genes as compared to the existing techniques. The observations are further supported by biological and statistical significance tests.

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks

OVERVIEW

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

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.

Mapping the interactome of HPV E6 and E7 oncoproteins with the ubiquitin-proteasome system

Protein ubiquitination and its reverse reaction, deubiquitination, regulate protein stability, protein binding activity, and their subcellular localization. These reactions are catalyzed by the enzymes E1, E2, and E3 ubiquitin (Ub) ligases and deubiquitinases (DUBs). The Ub-proteasome system (UPS) is targeted by viruses for the sake of their replication and to escape host immune response. To identify novel partners of human papillomavirus 16 (HPV16) E6 and E7 proteins, we assembled and screened a library of 590 cDNAs related to the UPS by using the Gaussia princeps luciferase protein complementation assay. HPV16 E6 was found to bind to the homology to E6AP C terminus-type Ub ligase (E6AP), three really interesting new gene (RING)-type Ub ligases (MGRN1, LNX3, LNX4), and the DUB Ub-specific protease 15 (USP15). Except for E6AP, the binding of UPS factors did not require the LxxLL-binding pocket of HPV16 E6. LNX3 bound preferentially to all high-risk mucosal HPV E6 tested, whereas LNX4 bound specifically to HPV16 E6. HPV16 E7 was found to bind to several broad-complex tramtrack and bric-a-brac domain-containing proteins (such as TNFAIP1/KCTD13) that are potential substrate adaptors of Cullin 3-RING Ub ligases, to RING-type Ub ligases implicated in innate immunity (RNF135, TRIM32, TRAF2, TRAF5), to the substrate adaptor DCAF15 of Cullin 4-RING Ub ligase and to some DUBs (USP29, USP33). The binding to UPS factors did not require the LxCxE motif but rather the C-terminal region of HPV16 E7 protein. The identified UPS factors interacted with most of E7 proteins across different HPV types. This study establishes a strategy for the rapid identification of interactions between host or pathogen proteins and the human ubiquitination system.

Protein-Protein Interactions in Virus-Host Systems

To study virus–host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions—viral–viral or host–host interactions—are constantly targeted and inhibited by exogenous interfaces mediating viral–host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these “hub proteins” evolve. “Party hubs” have multiple interfaces; they establish simultaneous/stable (domain–domain) interactions, and tend to evolve slowly. On the other hand, “date hubs” have few interfaces; they establish transient/weak (domain–motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein–protein interactions (PPIs), which can be represented as networks (protein interaction networks, PINs), with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface residues, either to evade or to optimize their binding capabilities. Apart from gaining and losing interactions via rewiring mechanisms, virus–host PINs also evolve via gene duplication (paralogy); conservation (orthology); horizontal gene transfer (HGT) (xenology); and molecular mimicry (convergence). The last sections of this review focus on PPI experimental approaches and their limitations, and provide an overview of sources of biomolecular data for studying virus–host protein interactions.

Deconstruction of the beaten Path-Sidestep interaction network provides insights into neuromuscular system development

An ‘interactome’ screen of all Drosophila cell-surface and secreted proteins containing immunoglobulin superfamily (IgSF) domains discovered a network formed by paralogs of Beaten Path (Beat) and Sidestep (Side), a ligand-receptor pair that is central to motor axon guidance. Here we describe a new method for interactome screening, the Bio-Plex Interactome Assay (BPIA), which allows identification of many interactions in a single sample. Using the BPIA, we ‘deorphanized’ four more members of the Beat-Side network. We confirmed interactions using surface plasmon resonance. The expression patterns of beat and side genes suggest that Beats are neuronal receptors for Sides expressed on peripheral tissues. side-VI is expressed in muscle fibers targeted by the ISNb nerve, as well as at growth cone choice points and synaptic targets for the ISN and TN nerves. beat-V genes, encoding Side-VI receptors, are expressed in ISNb and ISN motor neurons.

Within every organ of the body, cells must be able to recognise and communicate with one another in order to work together to perform a particular role. Each cell has a specific protein on its surface that acts like a molecular identity card, and which can form weak bonds with a complementary protein on another cell. There are thousands of different cell surface proteins, and the interactions between them – known collectively as the interactome – dictate the how cells interact with one another.

Many cell surface proteins are similar across species. Humans and fruit flies, for example, both possess a family of cell surface proteins that contain a region called the Immunoglobulin Superfamily domain. This family can be further divided into subfamilies, two of which are known as “Beats” and “Sides” for short. As the nervous system develops, nerve cells carrying a particular Beat protein interact with nerve or muscle cells carrying a corresponding Side protein. Yet while experiments have matched up many Beats and Sides, the partners of others remain unknown.

Li et al. have now developed a new technique called the Bio-Plex Interactome Assay to rapidly screen for interactions between multiple cell surface proteins in a single sample. Applying the technique to cells from fruit flies revealed new binding partners within the Beats and the Sides. After verifying several of these interactions, Li et al. explored the role of various Beats and Sides in the developing nervous system of fruit fly embryos by mapping the cells that display them on their surfaces.

This increased knowledge of the Beat-Side binding network should provide further insights into how connections form between nerve cells. The new screening technique could also eventually be used to map the cell surface protein interactome in humans. A number of key drugs, including the breast cancer drug Herceptin, target cell surface proteins. Identifying interactions among cell surface proteins could thus provide additional leads for developing new therapies.

CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination.

The Enterococcus faecalis virulence factor ElrA interacts with the human Four-and-a-Half LIM Domains Protein 2

The commensal bacterium Enterococcus faecalis is a common cause of nosocomial infections worldwide. The increasing prevalence of multi-antibiotic resistant E. faecalis strains reinforces this public health concern. Despite numerous studies highlighting several pathology-related genetic traits, the molecular mechanisms of E. faecalis virulence remain poorly understood. In this work, we studied 23 bacterial proteins that could be considered as virulence factors or involved in the Enterococcus interaction with the host. We systematically tested their interactions with human proteins using the Human ORFeome library, a set of 12,212 human ORFs, in yeast. Among the thousands of tested interactions, one involving the E. faecalis virulence factor ElrA and the human protein FHL2 was evidenced by yeast two-hybrid and biochemically confirmed. Further molecular characterizations allowed defining an FHL2-interacting domain (FID) of ElrA. Deletion of the FID led to an attenuated in vivo phenotype of the mutated strain clearly indicating that this interaction is likely to contribute to the multifactorial virulence of this opportunistic pathogen. Altogether, our results show that FHL2 is the first host cellular protein directly targeted by an E. faecalis virulence factor and that this interaction is involved in Enterococcus pathogenicity.

The Ser/Thr Protein Kinase Protein-Protein Interaction Map of M. tuberculosis

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, the leading cause of death among all infectious diseases. There are 11 eukaryotic-like serine/threonine protein kinases (STPKs) in Mtb, which are thought to play pivotal roles in cell growth, signal transduction and pathogenesis. However, their underlying mechanisms of action remain largely uncharacterized. In this study, using a Mtb proteome microarray, we have globally identified the binding proteins in Mtb for all of the STPKs, and constructed the first STPK protein interaction (KPI) map that includes 492 binding proteins and 1,027 interactions. Bioinformatics analysis showed that the interacting proteins reflect diverse functions, including roles in two-component system, transcription, protein degradation, and cell wall integrity. Functional investigations confirmed that PknG regulates cell wall integrity through key components of peptidoglycan (PG) biosynthesis, e.g. MurC. The global STPK-KPIs network constructed here is expected to serve as a rich resource for understanding the key signaling pathways in Mtb, thus facilitating drug development and effective control of Mtb.

Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein-Protein Interactions

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.

Mapping, modeling, and characterization of protein-protein interactions on a proteomic scale

Proteins effect a number of biological functions, from cellular signaling, organization, mobility, and transport to catalyzing biochemical reactions and coordinating an immune response. These varied functions are often dependent upon macromolecular interactions, particularly with other proteins. Small-scale studies in the scientific literature report protein-protein interactions (PPIs), but slowly and with bias towards well-studied proteins. In an era where genomic sequence is readily available, deducing genotype-phenotype relationships requires an understanding of protein connectivity at proteome-scale. A proteome-scale map of the protein-protein interaction network provides a global view of cellular organization and function. Here, we discuss a summary of methods for building proteome-scale interactome maps and the current status and implications of mapping achievements. Not only do interactome maps serve as a reference, detailing global cellular function and organization patterns, but they can also reveal the mechanisms altered by disease alleles, highlight the patterns of interaction rewiring across evolution, and help pinpoint biologically and therapeutically relevant proteins. Despite the considerable strides made in proteome-wide mapping, several technical challenges persist. Therefore, future considerations that impact current mapping efforts are also discussed.

Analyzing trapped protein complexes by Virotrap and SFINX

The analysis of protein interaction networks is one of the key challenges in the study of biology. It connects genotypes to phenotypes, and disruption of such networks is associated with many pathologies. Virtually all the approaches to the study of protein complexes require cell lysis, a dramatic step that obliterates cellular integrity and profoundly affects protein interactions. This protocol starts with Virotrap, a novel approach that avoids the need for cell homogenization by fusing the protein of interest to the HIV-1 Gag protein, trapping protein complexes in virus-like particles. By using the straightforward filtering index (SFINX), which is a powerful and intuitive online tool (http://sfinx.ugent.be) that enables contaminant removal from candidate lists resulting from mass-spectrometry-based analysis, we provide a complete workflow for researchers interested in mammalian protein complexes. Given direct access to mass spectrometers, researchers can process up to 24 samples in 7 d.

Systematic identification of phosphorylation-mediated protein interaction switches

Proteomics techniques can identify thousands of phosphorylation sites in a single experiment, the majority of which are new and lack precise information about function or molecular mechanism. Here we present a fast method to predict potential phosphorylation switches by mapping phosphorylation sites to protein-protein interactions of known structure and analysing the properties of the protein interface. We predict 1024 sites that could potentially enable or disable particular interactions. We tested a selection of these switches and showed that phosphomimetic mutations indeed affect interactions. We estimate that there are likely thousands of phosphorylation mediated switches yet to be uncovered, even among existing phosphorylation datasets. The results suggest that phosphorylation sites on globular, as distinct from disordered, parts of the proteome frequently function as switches, which might be one of the ancient roles for kinase phosphorylation.