Large-scale protein interactome mapping: strategies and opportunities

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Proteomimetic surface fragments distinguish targets by function

The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein-protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces.

The online Tabloid Proteome: an annotated database of protein associations

A complete knowledge of the proteome can only be attained by determining the associations between proteins, along with the nature of these associations (e.g. physical contact in protein–protein interactions, participation in complex formation or different roles in the same pathway). Despite extensive efforts in elucidating direct protein interactions, our knowledge on the complete spectrum of protein associations remains limited. We therefore developed a new approach that detects protein associations from identifications obtained after re-processing of large-scale, public mass spectrometry-based proteomics data. Our approach infers protein association based on the co-occurrence of proteins across many different proteomics experiments, and provides information that is almost completely complementary to traditional direct protein interaction studies. We here present a web interface to query and explore the associations derived from this method, called the online Tabloid Proteome. The online Tabloid Proteome also integrates biological knowledge from several existing resources to annotate our derived protein associations. The online Tabloid Proteome is freely available through a user-friendly web interface, which provides intuitive navigation and data exploration options for the user at http://iomics.ugent.be/tabloidproteome.

Current Understanding of the TCTP Interactome

Evolutionarily conserved and pleiotropic, the translationally controlled tumor protein (TCTP) is a housekeeping protein present in eukaryotic organisms. It plays an important role in regulating many fundamental processes, such as cell proliferation, cell death, immune responses, and apoptosis. As a result of the pioneer work by Adam Telerman and Robert Amson, the critical role of TCTP in tumor reversion was revealed. Moreover, TCTP has emerged as a regulator of cell fate determination and a promising therapeutic target for cancers. The multifaceted action of TCTP depends on its ability to interact with different proteins. Through this interaction network, TCTP regulates diverse physiological and pathological processes in a context-dependent manner. Complete mapping of the entire sets of TCTP protein interactions (interactome) is essential to understand its various cellular functions and to lay the foundation for the rational design of TCTP-based therapeutic approaches. So far, the global profiling of the interacting partners of TCTP has rarely been performed, but many interactions have been identified in small-scale studies in a specific biological system. This chapter, based on information from protein interaction databases and the literature, illustrates current knowledge of the TCTP interactome.

Characterization of the Translationally Controlled Tumor Protein (TCTP) Interactome Reveals Novel Binding Partners in Human Cancer Cells

Translationally controlled tumor protein (TCTP) is a highly conserved housekeeping protein present in eukaryotic organisms. It is involved in regulating many fundamental processes and plays a critical role in tumor reversion and tumorigenesis. Increasing evidence suggests that TCTP plays a role in the regulation of cell fate determination and is a promising therapeutic target for cancer. To decipher the exact mechanisms by which TCTP functions and how all these functions are integrated, we analyzed the interactome of TCTP in HeLa cells by coimmunoprecipitation (IP) and mass spectrometry (MS). A total of 98 proteins were identified. We confirmed the in vitro and in vivo association of TCTP with six of the identified binding proteins using reciprocal IP and bimolecular fluorescence complementation (BiFC) analysis, respectively. Moreover, TCTP interacted with Y-box-binding protein 1 (YBX1), and their interaction was localized to the N-terminal region of TCTP and the 1-129 amino acid (aa) residues of YBX1. The YBX1 protein plays an important role in cell proliferation, RNA splicing, DNA repair, drug resistance, and stress response to extracellular signals. These data suggest that the interaction of TCTP with YBX1 might cooperate or coordinate their functions in the control of diverse regulatory pathways in cancer cells. Taken together, our results not only reveal a large number of TCTP-associated proteins that possess pleiotropic functions, but also provide novel insights into the molecular mechanisms of TCTP in tumorigenesis.

Rhodium complexes as therapeutic agents

This perspective highlights recent examples of rhodium complexes that show diverse biological activities against various targets, including enzymes and protein–protein interactions.

Bioinformatics and Moonlighting Proteins

Multitasking or moonlighting is the capability of some proteins to execute two or more biochemical functions. Usually, moonlighting proteins are experimentally revealed by serendipity. For this reason, it would be helpful that Bioinformatics could predict this multifunctionality, especially because of the large amounts of sequences from genome projects. In the present work, we analyze and describe several approaches that use sequences, structures, interactomics, and current bioinformatics algorithms and programs to try to overcome this problem. Among these approaches are (a) remote homology searches using Psi-Blast, (b) detection of functional motifs and domains, (c) analysis of data from protein–protein interaction databases (PPIs), (d) match the query protein sequence to 3D databases (i.e., algorithms as PISITE), and (e) mutation correlation analysis between amino acids by algorithms as MISTIC. Programs designed to identify functional motif/domains detect mainly the canonical function but usually fail in the detection of the moonlighting one, Pfam and ProDom being the best methods. Remote homology search by Psi-Blast combined with data from interactomics databases (PPIs) has the best performance. Structural information and mutation correlation analysis can help us to map the functional sites. Mutation correlation analysis can only be used in very specific situations – it requires the existence of multialigned family protein sequences – but can suggest how the evolutionary process of second function acquisition took place. The multitasking protein database MultitaskProtDB (http://wallace.uab.es/multitask/), previously published by our group, has been used as a benchmark for the all of the analyses.

Quantifying domain-ligand affinities and specificities by high-throughput holdup assay

Many protein interactions are mediated by small linear motifs interacting specifically with defined families of globular domains. Quantifying the specificity of a motif requires measuring and comparing its binding affinities to all its putative target domains. To this aim, we developed the high-throughput holdup assay, a chromatographic approach that can measure up to a thousand domain-motif equilibrium binding affinities per day. Extracts of overexpressed domains are incubated with peptide-coated resins and subjected to filtration. Binding affinities are deduced from microfluidic capillary electrophoresis of flow-throughs. After benchmarking the approach on 210 PDZ-peptide pairs with known affinities, we determined the affinities of two viral PDZ-binding motifs derived from Human Papillomavirus E6 oncoproteins for 209 PDZ domains covering 79% of the human PDZome. We obtained exquisite sequence-dependent binding profiles, describing quantitatively the PDZome recognition specificity of each motif. This approach, applicable to many categories of domain-ligand interactions, has a wide potential for quantifying the specificities of interactomes.

Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis

The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field.

Recent advances in proteomics: towards the human proteome

After the successful completion of the Human Genome project in 2003, the next major challenge was to understand when and where the encoded proteins were expressed, and to generate a map of the complex, interconnected pathways, networks and molecular systems (the human proteome) that, taken together, control the workings of all cells, tissues, organs and organisms. Proteomics will be fundamental for such studies. This review summarizes the key discoveries that laid down the foundations for proteomics as we now know it, and describes key recent technological advances that will undoubtedly contribute to achieving the initial goal of the Human Proteome Organization of identifying and characterizing at least one protein product and representative post-translational modifications, single amino acid polymorphisms and splice variant isoforms from the 20,300 human protein-coding genes within the next 10 years. Successful unraveling of the human proteome will undoubtedly improve our understanding of human biology at the cellular level and lay the foundations for improved diagnostic, prognostic, therapeutic and preventive medical outcomes as we enter the era of personalized medicine.

Group 9 organometallic compounds for therapeutic and bioanalytical applications

CONSPECTUS: Compared with organic small molecules, metal complexes offer several distinct advantages as therapeutic agents or biomolecular probes. Carbon atoms are typically limited to linear, trigonal planar, or tetrahedral geometries, with a maximum of two enantiomers being formed if four different substituents are attached to a single carbon. In contrast, an octahedral metal center with six different substituents can display up to 30 different stereoisomers. While platinum- and ruthenium-based anticancer agents have attracted significant attention in the realm of inorganic medicinal chemistry over the past few decades, group 9 complexes (i.e., iridium and rhodium) have garnered increased attention in therapeutic and bioanalytical applications due to their adjustable reactivity (from kinetically liable to substitutionally inert), high water solubility, stability to air and moisture, and relative ease of synthesis. In this Account, we describe our efforts in the development of group 9 organometallic compounds of general form [M(C(∧)N)2(N(∧)N)] (where M = Ir, Rh) as therapeutic agents against distinct biomolecular targets and as luminescent probes for the construction of oligonucleotide-based assays for a diverse range of analytes. Earlier studies by researchers had focused on organometallic iridium(III) and rhodium(III) half-sandwich complexes that show promising anticancer activity, although their precise mechanisms of action still remain unknown. More recently, kinetically-inert group 9 complexes have arisen as fascinating alternatives to organic small molecules for the specific targeting of enzyme activity. Research in our laboratory has shown that cyclometalated octahedral rhodium(III) complexes were active against Janus kinase 2 (JAK2) or NEDD8-activating enzyme (NAE) activity, or against NO production leading to antivasculogenic activity in cellulo. At the same time, recent interest in the development of small molecules as modulators of protein-protein interactions has stimulated our research group to investigate whether kinetically-inert metal complexes could also be used to target protein-protein interfaces relevant to the pathogenesis of certain diseases. We have recently discovered that cyclometalated octahedral iridium(III) and rhodium(III) complexes bearing C(∧)N ligands based on 2-phenylpyridine could function as modulators of protein-protein interactions, such as TNF-α, STAT3, and mTOR. One rhodium(III) complex antagonized STAT3 activity in vitro and in vivo and displayed potent antitumor activity in a mouse xenograft model of melanoma. Notably, these studies were among the first to demonstrate the direct inhibition of protein-protein interfaces by kinetically-inert group 9 metal complexes. Additionally, we have discovered that group 9 solvato complexes carrying 2-phenylpyridine coligands could function as inhibitors and probes of β-amyloid fibrillogenesis. Meanwhile, the rich photophysical properties of iridium complexes have made them popular tools for the design of luminescent labels and probes. Luminescent iridium(III) complexes benefit from a high quantum yield, responsive emissive properties, long-lived phosphorescence lifetimes, and large Stokes shift values. Over the past few years, our group has developed a number of kinetically-inert, organometallic iridium(III) complexes bearing various C(∧)N and N(∧)N ligands that are selective for G-quadruplex DNA, which is a DNA secondary structure formed from planar stacks of guanine tetrads stabilized by Hoogsteen hydrogen bonding. These complexes were then employed to develop G-quadruplex-based, label-free luminescence switch-on assays for nucleic acids, enzyme activity, small molecules, and metal ions.

Co-expression and co-localization of hub proteins and their partners are encoded in protein sequence

Spatiotemporal coordination is a critical factor in biological processes. Some hubs in protein-protein interaction networks tend to be co-expressed and co-localized with their partners more strongly than others, a difference which is arguably related to functional differences between the hubs. Based on numerous analyses of yeast hubs, it has been suggested that differences in co-expression and co-localization are reflected in the structural and molecular characteristics of the hubs. We hypothesized that if indeed differences in co-expression and co-localization are encoded in the molecular characteristics of the protein, it may be possible to predict the tendency for co-expression and co-localization of human hubs based on features learned from systematically characterized yeast hubs. Thus, we trained a prediction algorithm on hubs from yeast that were classified as either strongly or weakly co-expressed and co-localized with their partners, and applied the trained model to 800 human hub proteins. We found that the algorithm significantly distinguishes between human hubs that are co-expressed and co-localized with their partners and hubs that are not. The prediction is based on sequence derived features such as "stickiness", i.e. the existence of multiple putative binding sites that enable multiple simultaneous interactions, "plasticity", i.e. the existence of predicted structural disorder which conjecturally allows for multiple consecutive interactions with the same binding site and predicted subcellular localization. These results suggest that spatiotemporal dynamics is encoded, at least in part, in the amino acid sequence of the protein and that this encoding is similar in yeast and in human.

2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

Plant protein interactomes

Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.

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.

Integrative proteomic profiling of protein activity and interactions using protein arrays

Proteomic studies based on abundance, activity, or interactions have been used to investigate protein functions in normal and pathological processes, but their combinatory approach has not been attempted. We present an integrative proteomic profiling method to measure protein activity and interaction using fluorescence-based protein arrays. We used an on-chip assay to simultaneously monitor the transamidating activity and binding affinity of transglutaminase 2 (TG2) for 16 TG2-related proteins. The results of this assay were compared with confidential scores provided by the STRING database to analyze the functional interactions of TG2 with these proteins. We further created a quantitative activity-interaction map of TG2 with these 16 proteins, categorizing them into seven groups based upon TG2 activity and interaction. This integrative proteomic profiling method can be applied to quantitative validation of previously known protein interactions, and in understanding the functions and regulation of target proteins in biological processes of interest.

Constructing structural networks of signaling pathways on the proteome scale

Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.

Exploring mixed microbial community functioning: recent advances in metaproteomics

System approaches to elucidate ecosystem functioning constitute an emerging area of research within microbial ecology. Such approaches aim at investigating all levels of biological information (DNA, RNA, proteins and metabolites) to capture the functional interactions occurring in a given ecosystem and track down characteristics that could not be accessed by the study of isolated components. In this context, the study of the proteins collectively expressed by all the microorganisms present within an ecosystem (metaproteomics) is not only crucial but can also provide insights into microbial functionality. Overall, the success of metaproteomics is closely linked to metagenomics, and with the exponential increase in the availability of metagenome sequences, this field of research is starting to experience generation of an overwhelming amount of data, which requires systematic analysis. Metaproteomics has been employed in very diverse environments, and this review discusses the recent advances achieved in the context of human biology, soil, marine and freshwater environments as well as natural and bioengineered systems.

How do dynamic cellular signals travel long distances?

Communication is essential. It is vital between cells in multi-cellular organisms, and within cells. A signaling molecule binds to a receptor protein, and initiates a cascade of dynamic events. Signaling is a multistep pathway, which allows signal amplification: if some of the molecules in a pathway transmit the signal to multiple molecules, the result can be a large number of activated molecules across the cell and multiple reactions. That is how a small number of extracellular signaling molecules can produce a major cellular response. The pathway can relay signals from the extracellular space to the nucleus. How do signals travel efficiently over long-distances across the cell? Here we argue that evolution has utilized three properties: a modular functional organization of the cellular network; sequences in some key regions of proteins, such as linkers or loops, which were pre-encoded by evolution to facilitate signaling among domains; and compact interactions between proteins which is achieved via conformational disorder.

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.

Do protein-protein interaction databases identify moonlighting proteins?

One of the most striking results of the human (and mammalian) genomes is the low number of protein-coding genes. To-date, the main molecular mechanism to increase the number of different protein isoforms and functions is alternative splicing. However, a less-known way to increase the number of protein functions is the existence of multifunctional, multitask, or "moonlighting", proteins. By and large, moonlighting proteins are experimentally disclosed by serendipity. Proteomics is becoming one of the very active areas of biomedical research, which permits researchers to identify previously unseen connections among proteins and pathways. In principle, protein-protein interaction (PPI) databases should contain information on moonlighting proteins and could provide suggestions to further analysis in order to prove the multifunctionality. As far as we know, nobody has verified whether PPI databases actually disclose moonlighting proteins. In the present work we check whether well-established moonlighting proteins present in PPI databases connect with their known partners and, therefore, a careful inspection of these databases could help to suggest their different functions. The results of our research suggest that PPI databases could be a valuable tool to suggest multifunctionality.