Roles for the two-hybrid system in exploration of the yeast protein interactome

ZmRPN1 confers quantitative variation in pollen number and boosts hybrid seed production in maize

The number of pollen grains is a critical determinant of reproductive success in seed plants and varies among species and individuals. However, in contrast with many mutant-screening studies relevant to anther and pollen development, the natural genetic basis for variations in pollen number remains largely unexplored. To address this issue, we carried out a genome-wide association study in maize, ultimately revealing that a large presence/absence variation in the promoter region of ZmRPN1 alters its expression level and thereby contributes to pollen number variation. Molecular analyses showed that ZmRPN1 interacts with ZmMSP1, which is known as a germline cell number regulator, and facilitates ZmMSP1 localization to the plasma membrane. Importantly, ZmRPN1 dysfunction resulted in a substantial increase in pollen number, consequently boosting seed production by increasing female-male planting ratio. Together, our findings uncover a key gene controlling pollen number, and therefore, modulation of ZmRPN1 expression could be efficiently used to develop elite pollinators for modern hybrid maize breeding.

The development of proximity labeling technology and its applications in mammals, plants, and microorganisms

Protein‒protein, protein‒RNA, and protein‒DNA interaction networks form the basis of cellular regulation and signal transduction, making it crucial to explore these interaction networks to understand complex biological processes. Traditional methods such as affinity purification and yeast two-hybrid assays have been shown to have limitations, as they can only isolate high-affinity molecular interactions under nonphysiological conditions or in vitro. Moreover, these methods have shortcomings for organelle isolation and protein subcellular localization. To address these issues, proximity labeling techniques have been developed. This technology not only overcomes the limitations of traditional methods but also offers unique advantages in studying protein spatial characteristics and molecular interactions within living cells. Currently, this technique not only is indispensable in research on mammalian nucleoprotein interactions but also provides a reliable approach for studying nonmammalian cells, such as plants, parasites and viruses. Given these advantages, this article provides a detailed introduction to the principles of proximity labeling techniques and the development of labeling enzymes. The focus is on summarizing the recent applications of TurboID and miniTurbo in mammals, plants, and microorganisms. Video Abstract.

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.

A network view of human immune system and virus-human interaction

The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.

Chromatographic separation strategies for precision mass spectrometry to study protein-protein interactions and protein phosphorylation

Investigating protein-protein interactions and protein phosphorylation can be of great significance when studying biological processes and human diseases at the molecular level. However, sample complexity, presence of low abundance proteins, and dynamic nature of the proteins often impede in achieving sufficient analytical depth in proteomics research. In this regard, chromatographic separation methodologies have played a vital role in the identification and quantification of proteins in complex sample mixtures. The combination of peptide and protein fractionation techniques with advanced high-performance mass spectrometry has allowed the researchers to successfully study the protein-protein interactions and protein phosphorylation. Several new fractionation strategies for large scale analysis of proteins and peptides have been developed to study protein-protein interactions and protein phosphorylation. These emerging chromatography methodologies have enabled the identification of several hundred protein complexes and even thousands of phosphorylation sites in a single study. In this review, we focus on current workflow strategies and chromatographic tools, highlighting their advantages and disadvantages, and examining their associated challenges and future potential.

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.

Preparation and In Vivo Use of an Activity-based Probe for N-acylethanolamine Acid Amidase

Activity-based protein profiling (ABPP) is a method for the identification of an enzyme of interest in a complex proteome through the use of a chemical probe that targets the enzyme's active sites. A reporter tag introduced into the probe allows for the detection of the labeled enzyme by in-gel fluorescence scanning, protein blot, fluorescence microscopy, or liquid chromatography-mass spectrometry. Here, we describe the preparation and use of the compound ARN14686, a click chemistry activity-based probe (CC-ABP) that selectively recognizes the enzyme N-acylethanolamine acid amidase (NAAA). NAAA is a cysteine hydrolase that promotes inflammation by deactivating endogenous peroxisome proliferator-activated receptor (PPAR)-alpha agonists such as palmitoylethanolamide (PEA) and oleoylethanolamide (OEA). NAAA is synthesized as an inactive full-length proenzyme, which is activated by autoproteolysis in the acidic pH of the lysosome. Localization studies have shown that NAAA is predominantly expressed in macrophages and other monocyte-derived cells, as well as in B-lymphocytes. We provide examples of how ARN14686 can be used to detect and quantify active NAAA ex vivo in rodent tissues by protein blot and fluorescence microscopy.

Global protein-protein interaction network of rice sheath blight pathogen

Rhizoctonia solani is the major pathogenic fungi of rice sheath blight. It is responsible for the most serious disease of rice (Oryza sativa L.) and causes significant yield losses in rice-growing countries. Identifying the protein-protein interaction (PPI) maps of R. solani can provide insights into the potential pathogenic mechanisms and assign putative functions to unknown genes. Here, we exploited a PPI map of R. solani anastomosis group 1 IA (AG-1 IA) based on the interolog and domain-domain interaction methods. We constructed a core subset of high-confidence protein networks consisting of 6705 interactions among 1773 proteins. The high quality of the network was revealed by comprehensive methods, including yeast two-hybrid experiments. Pathogenic interaction subnetwork, secreted proteins subnetwork, and mitogen-activated protein kinase (MAPK) cascade subnetwork and their interacting partners were constructed and analyzed. Moreover, to exactly predict the pathogenic factors, the expression levels of the interaction proteins were investigated by analyzing RNA sequences that consisted of samples from the entire infection progress. The PPIs offer an exceptionally rich source of data that can be used to understand the gene functions and biological processes of this serious disease at the system level.

Integrative approaches for finding modular structure in biological networks

A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

Reconstruction of the experimentally supported human protein interactome: what can we learn?

BACKGROUND

Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance.

RESULTS

First, we defined the UniProtKB manually reviewed human "complete" proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors.

CONCLUSIONS

Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human "complete" proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms.

A proteomics view of the molecular mechanisms and biomarkers of glaucomatous neurodegeneration

Despite improving understanding of glaucoma, key molecular players of neurodegeneration that can be targeted for treatment of glaucoma, or molecular biomarkers that can be useful for clinical testing, remain unclear. Proteomics technology offers a powerful toolbox to accomplish these important goals of the glaucoma research and is increasingly being applied to identify molecular mechanisms and biomarkers of glaucoma. Recent studies of glaucoma using proteomics analysis techniques have resulted in the lists of differentially expressed proteins in human glaucoma and animal models. The global analysis of protein expression in glaucoma has been followed by cell-specific proteome analysis of retinal ganglion cells and astrocytes. The proteomics data have also guided targeted studies to identify post-translational modifications and protein-protein interactions during glaucomatous neurodegeneration. In addition, recent applications of proteomics have provided a number of potential biomarker candidates. Proteomics technology holds great promise to move glaucoma research forward toward new treatment strategies and biomarker discovery. By reviewing the major proteomics approaches and their applications in the field of glaucoma, this article highlights the power of proteomics in translational and clinical research related to glaucoma and also provides a framework for future research to functionally test the importance of specific molecular pathways and validate candidate biomarkers.

The roles of whole-genome and small-scale duplications in the functional specialization of Saccharomyces cerevisiae genes

Researchers have long been enthralled with the idea that gene duplication can generate novel functions, crediting this process with great evolutionary importance. Empirical data shows that whole-genome duplications (WGDs) are more likely to be retained than small-scale duplications (SSDs), though their relative contribution to the functional fate of duplicates remains unexplored. Using the map of genetic interactions and the re-sequencing of 27 Saccharomyces cerevisiae genomes evolving for 2,200 generations we show that SSD-duplicates lead to neo-functionalization while WGD-duplicates partition ancestral functions. This conclusion is supported by: (a) SSD-duplicates establish more genetic interactions than singletons and WGD-duplicates; (b) SSD-duplicates copies share more interaction-partners than WGD-duplicates copies; (c) WGD-duplicates interaction partners are more functionally related than SSD-duplicates partners; (d) SSD-duplicates gene copies are more functionally divergent from one another, while keeping more overlapping functions, and diverge in their sub-cellular locations more than WGD-duplicates copies; and (e) SSD-duplicates complement their functions to a greater extent than WGD-duplicates. We propose a novel model that uncovers the complexity of evolution after gene duplication.

Using guanidine-hydrochloride for fast and efficient protein digestion and single-step affinity-purification mass spectrometry

Protein digestion is an integral part of the "shotgun" proteomics approach and commonly requires overnight incubation prior to mass spectrometry analysis. Quadruplicate "shotgun" proteomic analysis of whole yeast lysate demonstrated that Guanidine-Hydrochloride (Gnd-HCl) protein digestion can be optimally completed within 30 min with endoprotease Lys-C. No chemical artifacts were introduced when samples were incubated in Gnd-HCl at 95 °C, making Gnd-HCl an appropriate digestion buffer for shotgun proteomics. Current methodologies for investigating protein-protein interactions (PPIs) often require several preparation steps, which prolongs any parallel operation and high-throughput interaction analysis. Gnd-HCl allow the efficient elution and subsequent fast digestion of PPIs to provide a convenient high-throughput methodology for affinity-purification mass spectrometry (AP-MS) experiments. To validate the Gnd-HCl approach, label-free PPI analysis of several GFP-tagged yeast deubiquitinating enzymes was performed. The identification of known interaction partners demonstrates the utility of the optimized Gnd-HCl protocol that is also scalable to the 96 well-plate format.

Functional Proteomic Profiling of Phosphodiesterases Using SeraFILE Separations Platform

Functional proteomic profiling can help identify targets for disease diagnosis and therapy. Available methods are limited by the inability to profile many functional properties measured by enzymes kinetics. The functional proteomic profiling approach proposed here seeks to overcome such limitations. It begins with surface-based proteome separations of tissue/cell-line extracts, using SeraFILE, a proprietary protein separations platform. Enzyme kinetic properties of resulting subproteomes are then characterized, and the data integrated into proteomic profiles. As a model, SeraFILE-derived subproteomes of cyclic nucleotide-hydrolyzing phosphodiesterases (PDEs) from bovine brain homogenate (BBH) and rat brain homogenate (RBH) were characterized for cAMP hydrolysis activity in the presence (challenge condition) and absence of cGMP. Functional profiles of RBH and BBH were compiled from the enzyme activity response to the challenge condition in each of the respective subproteomes. Intersample analysis showed that comparable profiles differed in only a few data points, and that distinctive subproteomes can be generated from comparable tissue samples from different animals. These results demonstrate that the proposed methods provide a means to simplify intersample differences, and to localize proteins attributable to sample-specific responses. It can be potentially applied for disease and nondisease sample comparison in biomarker discovery and drug discovery profiling.

The study of protein–protein interactions in bacteria

Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.

Combining many interaction networks to predict gene function and analyze gene lists

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems.

The proteomic future: where mass spectrometry should be taking us

A newcomer to the -omics era, proteomics, is a broad instrument-intensive research area that has advanced rapidly since its inception less than 20 years ago. Although the 'wet-bench' aspects of proteomics have undergone a renaissance with the improvement in protein and peptide separation techniques, including various improvements in two-dimensional gel electrophoresis and gel-free or off-gel protein focusing, it has been the seminal advances in MS that have led to the ascension of this field. Recent improvements in sensitivity, mass accuracy and fragmentation have led to achievements previously only dreamed of, including whole-proteome identification, and quantification and extensive mapping of specific PTMs (post-translational modifications). With such capabilities at present, one might conclude that proteomics has already reached its zenith; however, 'capability' indicates that the envisioned goals have not yet been achieved. In the present review we focus on what we perceive as the areas requiring more attention to achieve the improvements in workflow and instrumentation that will bridge the gap between capability and achievement for at least most proteomes and PTMs. Additionally, it is essential that we extend our ability to understand protein structures, interactions and localizations. Towards these ends, we briefly focus on selected methods and research areas where we anticipate the next wave of proteomic advances.

The application of modular protein domains in proteomics

The ability of modular protein domains to independently fold and bind short peptide ligands both in vivo and in vitro has allowed a significant number of protein-protein interaction studies to take advantage of them as affinity and detection reagents. Here, we refer to modular domain based proteomics as "domainomics" to draw attention to the potential of using domains and their motifs as tools in proteomics. In this review we describe core concepts of domainomics, established and emerging technologies, and recent studies by functional category. Accumulation of domain-motif binding data should ultimately provide the foundation for domain-specific interactomes, which will likely reveal the underlying substructure of protein networks as well as the selectivity and plasticity of signal transduction.

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

Filtering and interpreting large-scale experimental protein-protein interaction data

Rarely acting in isolation, it is invariably the physical associations among proteins that define their biological activity, necessitating the study of the cellular meshwork of protein-protein interactions (PPI) before a full appreciation of gene function can be achieved. The past few years have seen a marked expansion in the both the sheer volume and number of organisms for which high-quality interaction data is available, with high-throughput interaction screening and detection techniques showing consistent improvement both in scale and sensitivity. Although techniques for large-scale PPI mapping are increasingly being applied to new organisms, including human, there is a corresponding need to rigorously evaluate, benchmark, and impartially filter the results. This chapter explores methods for PPI dataset evaluation, including a survey of previous techniques applied by landmark studies in the field and a discussion of promising new experimental approaches. We further outline practical suggestions and useful tools for interpreting newly generated PPI data. As the majority of large-scale experimental data has been generated for the budding yeast S. cerevisiae, most of the techniques and datasets described are from the perspective of this model unicellular eukaryote; however, extensions to other organisms including mammals are mentioned where possible.

A miniaturized sandwich immunoassay platform for the detection of protein-protein interactions

Background

Analysis of protein-protein interactions (PPIs) is a valuable approach for the characterization of huge networks of protein complexes or proteins of unknown function. Co-immunoprecipitation (coIP) using affinity resins coupled to protein A/G is the most widely used method for PPI detection. However, this traditional large scale resin-based coIP is too laborious and time consuming. To overcome this problem, we developed a miniaturized sandwich immunoassay platform (MSIP) by combining antibody array technology and coIP methods.

Results

Based on anti-FLAG antibody spotted aldehyde slides, MSIP enables simple, rapid and large scale detection of PPIs by fluorescent labeling anti-myc antibody. By analyzing well-known interacting and non-interacting protein pairs, MSIP was demonstrated to be highly accurate and reproducible. Compared to traditional resin-based coIP, MSIP results in higher sensitivity and enhanced throughput, with the additional benefit of digital read-outs. In addition, MSIP was shown to be a highly useful validation platform to confirm PPI candidates that have been identified from yeast two hybrid systems.

Conclusions

In conclusion, MSIP is proved to be a simple, cost-saving and highly efficient technique for the comprehensive study of PPIs.

Incomplete and noisy network data as a percolation process

We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that sampling and noise can have a profound effect on the perceived existence of a GCC and find that both processes can destroy it. We also show that the absence of a GCC puts a theoretical upper bound on the false-positive rate and relate our percolation analysis to experimental protein-protein interaction data.

The evolution of protein interaction networks in regulatory proteins

Interactions between proteins are essential for intracellular communication. They form complex networks which have become an important source for functional analysis of proteins. Combining phylogenies with network analysis, we investigate the evolutionary history of interaction networks from the bHLH, NR and bZIP transcription-factor families. The bHLH and NR networks show a hub-like structure with varying γ values. Mutation and gene duplication play an important role in adding and removing interactions. We conclude that in several of the protein families that we have studied, networks have primarily arisen by the development of heterodimerizing transcription factors, from an ancestral gene which interacts with any of the newly emerging proteins but also homodimerizes.

A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes

Using radiation hybrid genotyping data, 99% of all possible gene pairs across the mammalian genome were tested for interactions based on co-retention frequencies higher (attraction) or lower (repulsion) than chance. Gene interaction networks constructed from six independent data sets overlapped strongly. Combining the data sets resulted in a network of more than seven million interactions, almost all attractive. This network overlapped with protein-protein interaction networks on multiple measures and also confirmed the relationship between essentiality and centrality. In contrast to other biological networks, the radiation hybrid network did not show a scale-free distribution of connectivity but was Gaussian-like, suggesting a closer approach to saturation. The radiation hybrid (RH) network constitutes a platform for understanding the systems biology of the mammalian cell.

A review of current applications of mass spectrometry for neuroproteomics in epilepsy

The brain is unquestionably the most fascinating organ, and the hippocampus is crucial in memory storage and retrieval and plays an important role in stress response. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward an improved understanding of the complex molecular mechanisms that underlie functions of the brain and hippocampus is neuroproteomics. Mass spectrometry has been widely used to analyze biological samples, and has evolved into an indispensable tool for proteomics research. In this review, we present a general overview of the application of mass spectrometry in proteomics, summarize neuroproteomics and systems biology-based discovery of protein biomarkers for epilepsy, discuss the methodology needed to explore the epileptic hippocampus proteome, and also focus on applications of ingenuity pathway analysis (IPA) in disease research. This neuroproteomics survey presents a framework for large-scale protein research in epilepsy that can be applied for immediate epileptic biomarker discovery and the far-reaching systems biology understanding of the protein regulatory networks. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of epilepsy on society.

Human Ccr4-Not complexes contain variable deadenylase subunits

The Ccr4-Not complex is evolutionarily conserved and important for regulation of mRNA synthesis and decay. The composition of the yeast complex has been well described. Orthologues of the yeast Ccr4-Not components have been identified in human cells including multiple subunits with mRNA deadenylase activity. In the present study, we examine the composition of the human Ccr4-Not complex in an in-depth proteomic approach using stable cell lines expressing tagged CNOT proteins. We find at least four different variants of the human complex, consisting of seven stable core proteins and mutually exclusive associated mRNA deadenylase subunits. Interestingly, human CNOT4 is in a separate approximately 200 kDa complex. Furthermore, analyses of associated proteins indicate involvement of Ccr4-Not complexes in splicing, transport and localization of RNA molecules. Taken together, human Ccr4-Not complexes are heterogeneous in composition owing to differences in their deadenylase subunits, which may reflect the multi-functionality of these complexes in cellular processes.

Microarray profiling of phage-display selections for rapid mapping of transcription factor-DNA interactions

Modern computational methods are revealing putative transcription-factor (TF) binding sites at an extraordinary rate. However, the major challenge in studying transcriptional networks is to map these regulatory element predictions to the protein transcription factors that bind them. We have developed a microarray-based profiling of phage-display selection (MaPS) strategy that allows rapid and global survey of an organism's proteome for sequence-specific interactions with such putative DNA regulatory elements. Application to a variety of known yeast TF binding sites successfully identified the cognate TF from the background of a complex whole-proteome library. These factors contain DNA-binding domains from diverse families, including Myb, TEA, MADS box, and C2H2 zinc-finger. Using MaPS, we identified Dot6 as a trans-active partner of the long-predicted orphan yeast element Polymerase A & C (PAC). MaPS technology should enable rapid and proteome-scale study of bi-molecular interactions within transcriptional networks.

Specific interactions between protein transcription factors (TFs) and their DNA recognition sites are central to the regulation of gene expression. Inter-species conservation of these TF binding sites (TFBS), and their statistical enrichment in sets of co-expressed genes, facilitates their large-scale prediction through computational sequence analysis. A major challenge in characterizing these putative TFBS is the identification of the proteins that bind them. We have developed a new approach to this problem by expressing random genomically encoded protein fragments as fusions to the capsid of bacteriophage T7. We select this diverse phage-display “library” for binding surface-immobilized instances of the TFBS in the form of short double-stranded DNA. This in vitro selection strategy leads to the enrichment of phage whose capsid-fusion peptides interact with the specific DNA sequence. Because each phage carries the DNA encoding the peptide fusion, the identity of the enriched phage can be determined through population-level PCR amplification of DNA inserts and their hybridization to DNA microarrays. Here, we show that this technology efficiently reveals the identity of proteins that bind known and novel predicted regulatory elements. Its application to a predicted yeast element (PAC) reveals Dot6 as one of its interaction partners, both in vitro and within the yeast nucleus.

A rice kinase-protein interaction map

Plants uniquely contain large numbers of protein kinases, and for the vast majority of the 1,429 kinases predicted in the rice (Oryza sativa) genome, little is known of their functions. Genetic approaches often fail to produce observable phenotypes; thus, new strategies are needed to delineate kinase function. We previously developed a cost-effective high-throughput yeast two-hybrid system. Using this system, we have generated a protein interaction map of 116 representative rice kinases and 254 of their interacting proteins. Overall, the resulting interaction map supports a large number of known or predicted kinase-protein interactions from both plants and animals and reveals many new functional insights. Notably, we found a potential widespread role for E3 ubiquitin ligases in pathogen defense signaling mediated by receptor-like kinases, particularly by the kinases that may have evolved from recently expanded kinase subfamilies in rice. We anticipate that the data provided here will serve as a foundation for targeted functional studies in rice and other plants. The application of yeast two-hybrid and TAPtag analyses for large-scale plant protein interaction studies is also discussed.

Chemical biology approaches to probe the proteome

Understanding disease-associated cellular defects at a molecular level is critical for the development of pharmacological intervention strategies. Recent breakthroughs in microarray and sequencing technologies have provided powerful tools to rapidly reveal the cellular defects caused by alterations in the genome or transcriptome. However, the picture of how the cellular proteome is affected in a disease state and how changes in DNA and RNA affect protein function is often incomplete. This is perhaps not surprising because the functions of proteins are not just determined by primary sequence and abundance, but are under the control of many regulatory mechanisms. Here, we highlight several recent advances in proteomics technologies that are being developed to generate comprehensive human proteome maps and discuss them in the context of strategies that have been developed in simple model organisms. Chemical biology will play a critical role in drafting a map of the proteome with functional information. Chemical genetic approaches that use high-throughput small molecule screening have resulted in the public availability of small molecule datasets through web interfaces such as PubChem. With such approaches, the opportunities to investigate disease and to explore the proteome with chemistry are rapidly increasing. In addition, new tools are being developed to probe protein function. Here we highlight recent developments in chemical biology and the exciting opportunities that are arising with them.

Literature-curated protein interaction datasets

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.

Activity-Based Protein Profiling (ABPP) and Click Chemistry (CC)-ABPP by MudPIT Mass Spectrometry

Activity-based protein profiling (ABPP) is a chemical proteomic method for functional interrogation of enzymes within complex proteomes. This Unit presents a protocol for in vitro and in vivo labeling using ABPP and Click Chemistry (CC)-ABPP probes for in-depth profiling using the Multi-dimensional Protein Identification Technology (MudPIT) analysis platform.

Interaction networks: from protein functions to drug discovery. A review

Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains

Extensive efforts have been devoted to determining the binding specificity of Src homology 3 (SH3) domains usually in a case-by-case manner. A generic structure-based model is necessary to decipher the protein recognition code of the entire domain family. In this study, we have developed a general framework that combines molecular modeling and a machine learning algorithm to capture the energetic characteristics of the domain-peptide interactions and predict the binding specificity of the SH3 domain family. Our model is not trained for individual SH3 domains; rather it is a generic model for the entire domain family. Our model not only achieved satisfactory prediction accuracy but also provided structural insights into which residues are important for the binding specificity. The success of our framework on SH3 domains suggests that it is possible to establish a theoretical model to decipher the protein recognition code of any modular domain.

Mass spectrometry in systems biology: an overview

As an emerging field, systems biology is currently the talk of the town, which challenges our philosophy in comprehending biology. Instead of the reduction approach advocated in molecular biology, systems biology aims at systems-level understanding of correlations among molecular components. Such comprehensive investigation requires massive information from the "omics" cascade demanding high-throughput screening techniques. Being one of the most versatile analytical methods, mass spectrometry has already been playing a significant role at this early stage of systems biology. In this review, we documented the advances in modern mass spectrometry technologies as well as nascent inventions. Recent applications of mass spectrometry-based techniques and methodologies in genomics, proteomics, transcriptomics and metabolomics will be further elaborated individually. Undoubtedly, more applications of mass spectrometry in systems biology can be expected in the near future.

The interactome: predicting the protein-protein interactions in cells

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

Mapping the human protein interactome

Interactions are the essence of all biomolecules because they cannot fulfill their roles without interacting with other molecules. Hence, mapping the interactions of biomolecules can be useful for understanding their roles and functions. Furthermore, the development of molecular based systems biology requires an understanding of the biomolecular interactions. In recent years, the mapping of protein-protein interactions in different species has been reported, but few reports have focused on the large-scale mapping of protein-protein interactions in human. Here, we review the developments in protein interaction mapping and we discuss issues and strategies for the mapping of the human protein interactome.

Discrimination between stable and dynamic components of protein complexes by means of quantitative proteomics

To discriminate between stable and dynamic protein-protein interactions, we propose a strategy in which cells with and without tagged bait are differentially labeled with stable isotope and combined prior to complex purification. Mass-spectrometric analysis of the purified complexes identifies stable and dynamic components as those derived exclusively from the tagged cells and those from both cells, respectively. We successfully applied this strategy to analyze two yeast protein complexes, eIF2B-eIF2 and cyclin-Cdc28.

Resolving the network of cell signaling pathways using the evolving yeast two-hybrid system

In 1983, while investigators had identified a few human proteins as important regulators of specific biological outcomes, how these proteins acted in the cell was essentially unknown in almost all cases. Twenty-five years later, our knowledge of the mechanistic basis of protein action has been transformed by our increasingly detailed understanding of protein-protein interactions, which have allowed us to define cellular machines. The advent of the yeast two-hybrid (Y2H) system in 1989 marked a milestone in the field of proteomics. Exploiting the modular nature of transcription factors, the Y2H system allows facile measurement of the activation of reporter genes based on interactions between two chimeric or "hybrid" proteins of interest. After a decade of service as a leading platform for individual investigators to use in exploring the interaction properties of interesting target proteins, the Y2H system has increasingly been applied in high-throughput applications intended to map genome-scale protein-protein interactions for model organisms and humans. Although some significant technical limitations apply, Y2H has made a great contribution to our general understanding of the topology of cellular signaling networks.