Protein interaction maps for model organisms

Human protein reference database--2006 update

Human Protein Reference Database (HPRD) (http://www.hprd.org) was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein-protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein-protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (http://www.genprot.org), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data.

Interactome: gateway into systems biology

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

The molecular make-up of a tumour: proteomics in cancer research

The enormous progress in proteomics, enabled by recent advances in MS (mass spectrometry), has brought protein analysis back into the limelight of cancer research, reviving old areas as well as opening new fields of study. In this review, we discuss the basic features of proteomic technologies, including the basics of MS, and we consider the main current applications and challenges of proteomics in cancer research, including (i) protein expression profiling of tumours, tumour fluids and tumour cells; (ii) protein microarrays; (iii) mapping of cancer signalling pathways; (iv) pharmacoproteomics; (v) biomarkers for diagnosis, staging and monitoring of the disease and therapeutic response; and (vi) the immune response to cancer. All these applications continue to benefit from further technological advances, such as the development of quantitative proteomics methods, high-resolution, high-speed and high-sensitivity MS, functional protein assays, and advanced bioinformatics for data handling and interpretation. A major challenge will be the integration of proteomics with genomics and metabolomics data and their functional interpretation in conjunction with clinical results and epidemiology.

Refined phylogenetic profiles method for predicting protein-protein interactions

MOTIVATION

The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method.

RESULTS

In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.

Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues

A major advantage of the mouse model lies in the increasing information on its genome, transcriptome, and proteome, as well as in the availability of a fast growing number of targeted and induced mutant alleles. However, data from comparative transcriptome and proteome analyses in this model organism are very limited. We use DNA chip-based RNA expression profiling and 2D gel electrophoresis, combined with peptide mass fingerprinting of liver and kidney, to explore the feasibility of such comprehensive gene expression analyses. Although protein analyses mostly identify known metabolic enzymes and structural proteins, transcriptome analyses reveal the differential expression of functionally diverse and not yet described genes. The comparative analysis suggests correlation between transcriptional and translational expression for the majority of genes. Significant exceptions from this correlation confirm the complementarities of both approaches. Based on RNA expression data from the 200 most differentially expressed genes, we identify chromosomal colocalization of known, as well as not yet described, gene clusters. The determination of 29 such clusters may suggest that coexpression of colocalizing genes is probably rather common.

The ERG28-encoded protein, Erg28p, interacts with both the sterol C-4 demethylation enzyme complex as well as the late biosynthetic protein, the C-24 sterol methyltransferase (Erg6p)

In Saccharomyces cerevisiae, the C-24 sterol methyltransferase (Erg6p) converts zymosterol to fecosterol, an enzymatic step following C-4 demethylation of 4,4-dimethylzymosterol. Our previous study showed that an endoplasmic reticulum (ER) transmembrane protein, Erg28p, functions as a scaffold to tether the C-4 demethylation enzymatic complex (Erg25p-Erg26p-Erg27p) to the ER. To determine whether Erg28p also interacts with other ergosterol biosynthetic proteins, we compared protein levels of Erg3p, Erg6p, Erg7p, Erg11p and Erg25p in three pairs of erg28 and ERG28 strains. In erg28 strains, the Erg6p level in the ER fraction was decreased by about 50% relative to the wild-type strain, while ER protein levels of the four other ergosterol proteins showed no significant differences. Co-immunoprecipitation experiments, using an erg28 strain transformed with the epitope-tagged plasmid pERG28-HA and proteins detected with anti-HA and anti-Erg6p antibodies, indicated that Erg6p and Erg28p reciprocally co-immunoprecipitate. Further, the split ubiquitin yeast membrane two-hybrid system designed to detect protein interactions between membrane bound proteins also indicated an Erg28p-Erg6p interaction when pERG6-Cub was used as the bait and pERG28-NubG was used as the prey. We conclude that Erg28p may not only anchor the C-4 demethylation enzyme complex to the ER but also acts as a protein bridge to the Erg6p enzyme required for the next ergosterol biosynthetic step.

Accurate extraction of functional associations between proteins based on common interaction partners and common domains

MOTIVATION

Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction.

RESULT

In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.

WormBase as an integrated platform for the C. elegans ORFeome

The ORFeome project has validated and corrected a large number of predicted gene models in the nematode C. elegans, and has provided an enormous resource for proteome-scale studies. To make the resource useful to the research and teaching community, it needs to be integrated with other large-scale data sets, including the C. elegans genome, cell lineage, neurological wiring diagram, transcriptome, and gene expression map. This integration is also critical because the ORFeome data sets, like other 'omics' data sets, have significant false-positive and false-negative rates, and comparison to related data is necessary to make confidence judgments in any given data point. WormBase, the central data repository for information about C. elegans and related nematodes, provides such a platform for integration. In this report, we will describe how C. elegans ORFeome data are deposited in the database, how they are used to correct gene models, how they are integrated and displayed in the context of other data sets at the WormBase Web site, and how WormBase establishes connection with the reagent-based resources at the ORFeome project Web site.

Human ORFeome version 1.1: a platform for reverse proteomics

The advent of systems biology necessitates the cloning of nearly entire sets of protein-encoding open reading frames (ORFs), or ORFeomes, to allow functional studies of the corresponding proteomes. Here, we describe the generation of a first version of the human ORFeome using a newly improved Gateway recombinational cloning approach. Using the Mammalian Gene Collection (MGC) resource as a starting point, we report the successful cloning of 8076 human ORFs, representing at least 7263 human genes, as mini-pools of PCR-amplified products. These were assembled into the human ORFeome version 1.1 (hORFeome v1.1) collection. After assessing the overall quality of this version, we describe the use of hORFeome v1.1 for heterologous protein expression in two different expression systems at proteome scale. The hORFeome v1.1 represents a central resource for the cloning of large sets of human ORFs in various settings for functional proteomics of many types, and will serve as the foundation for subsequent improved versions of the human ORFeome.

ORFeome cloning and systems biology: standardized mass production of the parts from the parts-list

Together with metabolites, proteins and RNAs form complex biological systems through highly intricate networks of physical and functional interactions. Large-scale studies aimed at a molecular understanding of the structure, function, and dynamics of proteins and RNAs in the context of cellular networks require novel approaches and technologies. This Special Issue of Genome Research features strategies for the high-throughput construction and manipulation of complete sets of protein-encoding open reading frames (ORFeome), gene promoters (promoterome), and noncoding RNAs, as predicted from genome and transcriptome sequences. Here we discuss the use of a recombinational cloning system that allows efficiency, adaptability, and compatibility in the generation of ORFeome, promoterome, and other resources.

Analyzing cellular biochemistry in terms of molecular networks

One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.

Identification of a novel brain-specific and reelin-regulated gene that encodes a protein colocalized with synapsin

We carried out a screening of genes that are differentially expressed in normal mice and reeler mutants and are characterized by abnormal neuronal migration and neurite deployment due to defective Reelin signalling. A novel gene, provisionally named C61, was overexpressed in Reelin-deficient embryonic mouse brain RNA. C61 encodes a 3.7 kb mRNA that is brain specific and developmentally regulated, with predominant expression in differentiating neurons. The predicted protein is 664 amino acids long, and contains LAG1 and Ezrin/Radixin/Moesin-Myosin-Filament motifs, suggesting that it may function as an intracellular adaptor. From E14.5 to birth, C61 was highly expressed in all neuronal differentiation fields, with the highest signal in the telencephalic cortical plate and mitral cells in the olfactory bulb. When expressed as a GFP fusion protein in transfected non-neuronal cells and primary neurons, this protein localizes, respectively, to the nuclear membrane or axonal outgrowths, indicating a function in axonal traffic or signalling.

Versatile analysis of multiple macromolecular interactions by SPR imaging: application to p53 and DNA interaction

The greatest challenge in the postgenomic era is the description of proteome interactions, such as protein-protein or protein-DNA interactions. Surface plasmon resonance (SPR) is an optical technique in which binding of an analyte to the surface changes the refractive index at the surface/solution interface. Molecular interactions are analysed in real time without a labeling step. Currently, the limit to SPR imaging is the small number of reactions that can be simultaneously analysed. Using a novel grafting technology and a new imaging system, we increased the throughput of SPR imaging. The interaction between p53 and DNA was chosen as a paradigm for validation of this assay. Using a tagged DNA methodology, we simultaneously targeted multiple DNA sequences on a single chip. The interaction between p53 and these DNA sequences was monitored by SPR imaging. Qualitative and quantitative analysis provides results similar to those obtained with conventional technologies.

Selective inhibition of calcineurin-NFAT signaling by blocking protein-protein interaction with small organic molecules

Transient or reversible protein-protein interactions are commonly used to ensure efficient targeting of signaling enzymes to their cellular substrates. These interactions include direct binding to substrate, interaction with an accessory or scaffold protein, and positioning at subcellular locations in proximity to substrates. The existence of specialized targeting mechanisms raises the possibility of designing inhibitors that do not block enzyme activity per se, but rather interfere with targeting of the enzyme to one or more of its substrates within the cell. Here, we identify small organic molecules that specifically block targeting of the protein phosphatase calcineurin to its substrate nuclear factor of activated T cells (NFAT, also termed NFATc) and show that they are effective inhibitors of calcineurin-NFAT signaling.

Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network

To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.

Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis

Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized "pipeline schematics". We find that the properties of a protein that are most significant are: (i.) whether it is conserved across many organisms; (ii). the percentage composition of charged residues; (iii). the occurrence of hydrophobic patches; (iv). the number of binding partners it has; and (v). its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/.

A novel proteomic screen for peptide-protein interactions

Regulated interactions between short, unstructured amino acid sequences and modular protein domains are central to cell signaling. Here we use synthetic peptides in "active" (e.g. phosphorylated) and "control" (e.g. non-phosphorylated) forms as baits in affinity pull-down experiments to determine such interactions by quantitative proteomics. Stable isotope labeling by amino acids in cell culture distinguishes specific binders directly by the isotope ratios determined by mass spectrometry (Blagoev, B., Kratchmarova, I., Ong, S.-E., Nielsen, M., Foster, L. J., and Mann, M. (2003) Nat. Biotechnol. 21, 315-318). A tyrosine-phosphorylated peptide of the epidermal growth factor receptor specifically retrieved the Src homology domain (SH) 2- and SH3 domain-containing adapter protein Grb2. A proline-rich sequence of Son of Sevenless also specifically bound Grb2, demonstrating that the screen maintains specificity with low affinity interactions. The proline-rich Sos peptide retrieved only SH3 domain containing proteins as specific binding partners. Two of these, Pacsin 3 and Sorting Nexin 9, were confirmed by immunoprecipitation. Our data are consistent with a change in the role of Sos from Ras-dependent signaling to actin remodeling/endocytic signaling events by a proline-SH3 domain switch.

Development of human protein reference database as an initial platform for approaching systems biology in humans

Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.

From gene networks to gene function

We propose a novel method to identify functionally related genes based on comparisons of neighborhoods in gene networks. This method does not rely on gene sequence or protein structure homologies, and it can be applied to any organism and a wide variety of experimental data sets. The character of the predicted gene relationships depends on the underlying networks;they concern biological processes rather than the molecular function. We used the method to analyze gene networks derived from genome-wide chromatin immunoprecipitation experiments, a large-scale gene deletion study, and from the genomic positions of consensus binding sites for transcription factors of the yeast Saccharomyces cerevisiae. We identified 816 functional relationships between 159 genes and show that these relationships correspond to protein-protein interactions, co-occurrence in the same protein complexes, and/or co-occurrence in abstracts of scientific articles. Our results suggest functions for seven previously uncharacterized yeast genes: KIN3 and YMR269W may be involved in biological processes related to cell growth and/or maintenance, whereas IES6, YEL008W, YEL033W, YHL029C, YMR010W, and YMR031W-A are likely to have metabolic functions.

Integrating ‘omic’ information: a bridge between genomics and systems biology

The availability of genome sequences for several organisms, including humans, and the resulting first-approximation lists of genes, have allowed a transition from molecular biology to 'modular biology'. In modular biology, biological processes of interest, or modules, are studied as complex systems of functionally interacting macromolecules. Functional genomic and proteomic ('omic') approaches can be helpful to accelerate the identification of the genes and gene products involved in particular modules, and to describe the functional relationships between them. However, the data emerging from individual omic approaches should be viewed with caution because of the occurrence of false-negative and false-positive results and because single annotations are not sufficient for an understanding of gene function. To increase the reliability of gene function annotation, multiple independent datasets need to be integrated. Here, we review the recent development of strategies for such integration and we argue that these will be important for a systems approach to modular biology.

Fluorescence imaging of signaling networks

Receptor-triggered signaling processes exhibit complex cross-talk and feedback interactions, with many signaling proteins and second messengers acting locally within the cell. The flow of information in this input-output system can only be understood by tracking where and when local signaling activities are induced. Systematic strategies are therefore needed to measure the localization and translocation of all signaling proteins, and to develop fluorescent biosensors that can track local signaling activities in individual cells. Such a biosensor tool chest can be based on two types of green fluorescent protein constructs that either translocate or undergo fluorescence-resonance-energy transfer when local signaling occurs. Broad strategies to measure quantitative, dynamic parameters in signaling networks, together with perturbation approaches, are needed to develop comprehensive models of signaling networks*.

Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline

By integrating functional genomic and proteomic mapping approaches, biological hypotheses should be formulated with increasing levels of confidence. For example, yeast interactome and transcriptome data can be correlated in biologically meaningful ways. Here, we combine interactome mapping data generated for a multicellular organism with data from both large-scale phenotypic analysis ("phenome mapping") and transcriptome profiling. First, we generated a two-hybrid interactome map of the Caenorhabditis elegans germline by using 600 transcripts enriched in this tissue. We compared this map to a phenome map of the germline obtained by RNA interference (RNAi) and to a transcriptome map obtained by clustering worm genes across 553 expression profiling experiments. In this dataset, we find that essential proteins have a tendency to interact with each other, that pairs of genes encoding interacting proteins tend to exhibit similar expression profiles, and that, for approximately 24% of germline interactions, both partners show overlapping embryonic lethal or high incidence of males RNAi phenotypes and similar expression profiles. We propose that these interactions are most likely to be relevant to germline biology. Similar integration of interactome, phenome, and transcriptome data should be possible for other biological processes in the nematode and for other organisms, including humans.

Protein-interaction networks: from experiments to analysis

Functional proteomics approaches aim to characterize comprehensively the function of gene products, and provide a first-level understanding of cellular mechanisms. Here, we review recent techniques for the construction and prediction of large-scale protein-interaction networks, with a particular emphasis on computational processing steps and comparative assessment of the reliability and completeness of the various approaches. We also discuss the use of protein-interaction network information in functional annotation and in the generation of higher-level biological hypotheses on pathways.

Conventional and functional proteomics using large format two-dimensional gel electrophoresis 24 hours after controlled cortical impact in postnatal day 17 rats

Conventional and functional proteomics have significant potential to expand our understanding of traumatic brain injury (TBI) but have not yet been used. The purpose of the present study was to examine global hippocampal protein changes in postnatal day (PND) 17 immature rats 24 h after moderate controlled cortical impact (CCI). Silver nitrate stains or protein kinase B (PKB) phosphoprotein substrate antibodies were used to evaluate high abundance or PKB pathway signal transduction proteins representing conventional and functional proteomic approaches, respectively. Isoelectric focusing was performed over a nonlinear pH range of 3-10 with immobilized pH gradients (IPG strips) using supernatant from the most soluble cellular protein fraction of hippocampal tissue protein lysates from six paired sham and injured PND 17 rats. Approximately 1,500 proteins were found in each silver stained gel with 40% matching of proteins. Of these 600 proteins, 52% showed a twofold, 20% a fivefold, and 10% a 10-fold decrease or increase. Spot matching with existing protein databases revealed changes in important cytoskeletal and cell signalling proteins. PKB substrate protein phosphorylation was best seen in large format two-dimensional blots and known substrates of PKB such as glucose transporter proteins 3 and 4 and forkhead transcription factors, identified based upon molecular mass and charge, showed altered phosphorylation 24 h after injury. These results suggest that combined conventional and functional proteomic approaches are powerful, complementary and synergistic tools revealing multiple protein changes and posttranslational protein modifications that allow for more specific and comprehensive functional assessments after pediatric TBI.

Computational methods for the prediction of protein interactions

Establishing protein interaction networks is crucial for understanding cellular operations. Detailed knowledge of the 'interactome', the full network of protein-protein interactions, in model cellular systems should provide new insights into the structure and properties of these systems. Parallel to the first massive application of experimental techniques to the determination of protein interaction networks and protein complexes, the first computational methods, based on sequence and genomic information, have emerged.

Molecular recognition: identifying compounds and their targets

As a result of gene sequencing and proteomic efforts, thousands of new genes and proteins are now available as potential drug targets. The milieu of these proteins is complex and interactive; thousands of proteins activate, inhibit, and control each other's actions. The effect of blocking or activating a protein in a cell is far-reaching, and can affect whole, as well as adjacent pathways. This network of pathways is dynamic and a cellular response can change depending on the stimulus. In this section, the identification and role of individual proteins within the context of networked pathways, and the regulation of the activity of these proteins is discussed. Diverse chemical libraries, combinatorial libraries, natural products, as well as unnatural natural products that are derived from combinatorial biology (Chiu [2001] Proc. Natl. Acad. Sci. USA. 98:8548-8553), provide the chemical diversity in the search for new drugs to block new targets. Identifying new compounds that can become drugs is a long, expensive, and arduous task and potential targets must be carefully defined so as not to waste valuable resources. Equally important is the selection of compounds to be future drug candidates. Target selectivity in no way guarantees clinical efficacy, as the compound must meet pharmaceutical requirements, such as solubility, absorption, tissue distribution, and lack of toxicity. Thus matching biological diversity with chemical diversity involves something more than tight interactions, it involves interactions of the compounds with a variety host factors that can modulate its activity.

Transcriptional regulation: a genomic overview

The availability of the Arabidopsis thaliana genome sequence allows a comprehensive analysis of transcriptional regulation in plants using novel genomic approaches and methodologies. Such a genomic view of transcription first necessitates the compilation of lists of elements. Transcription factors are the most numerous of the different types of proteins involved in transcription in eukaryotes, and the Arabidopsis genome codes for more than 1,500 of them, or approximately 6% of its total number of genes. A genome-wide comparison of transcription factors across the three eukaryotic kingdoms reveals the evolutionary generation of diversity in the components of the regulatory machinery of transcription. However, as illustrated by Arabidopsis, transcription in plants follows similar basic principles and logic to those in animals and fungi. A global view and understanding of transcription at a cellular and organismal level requires the characterization of the Arabidopsis transcriptome and promoterome, as well as of the interactome, the localizome, and the phenome of the proteins involved in transcription.

One from column A and two from column B: the benefits of phage display in molecular-recognition studies

Recent uses of phage-displayed combinatorial peptide and cDNA libraries have proven invaluable in mapping protein-protein interactions, protein-drug interactions, and the generation of 'molecular therapeutics'. This article reviews some of the findings of the past year and points out some of the pros and cons of phage display as compared with those of yeast two-hybrid screening.

Combined functional genomic maps of the C. elegans DNA damage response

Many human cancers originate from defects in the DNA damage response (DDR). Although much is known about this process, it is likely that additional DDR genes remain to be discovered. To identify such genes, we used a strategy that combines protein-protein interaction mapping and large-scale phenotypic analysis in Caenorhabditis elegans. Together, these approaches identified 12 worm DDR orthologs and 11 novel DDR genes. One of these is the putative ortholog of hBCL3, a gene frequently altered in chronic lymphocytic leukemia. Thus, the combination of functional genomic mapping approaches in model organisms may facilitate the identification and characterization of genes involved in cancer and, perhaps, other human diseases.

Mining DNA microarray data using a novel approach based on graph theory

The recent demonstration that biochemical pathways from diverse organisms are arranged in scale-free, rather than random, systems [Jeong et al., Nature 407 (2000) 651-654], emphasizes the importance of developing methods for the identification of biochemical nexuses--the nodes within biochemical pathways that serve as the major input/output hubs, and therefore represent potentially important targets for modulation. Here we describe a bioinformatics approach that identifies candidate nexuses for biochemical pathways without requiring functional gene annotation; we also provide proof-of-principle experiments to support this technique. This approach, called Nexxus, may lead to the identification of new signal transduction pathways and targets for drug design.

Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae

Genomic and proteomic approaches can provide hypotheses concerning function for the large number of genes predicted from genome sequences. Because of the artificial nature of the assays, however, the information from these high-throughput approaches should be considered with caution. Although it is possible that more meaningful hypotheses could be formulated by integrating the data from various functional genomic and proteomic projects, it has yet to be seen to what extent the data can be correlated and how such integration can be achieved. We developed a 'transcriptome-interactome correlation mapping' strategy to compare the interactions between proteins encoded by genes that belong to common expression-profiling clusters with those between proteins encoded by genes that belong to different clusters. Using this strategy with currently available data sets for Saccharomyces cerevisiae, we provide the first global evidence that genes with similar expression profiles are more likely to encode interacting proteins. We show how this correlation between transcriptome and interactome data can be used to improve the quality of hypotheses based on the information from both approaches. The strategy described here may help to integrate other functional genomic and proteomic data, both in yeast and in higher organisms.

Exploring the protein interactome using comprehensive two-hybrid projects

Large-scale two-hybrid projects were used in an approach to examine protein-protein interactions. Despite the various limitations of this approach, these projects revealed a wealth of novel interactions, and the protein interactome may be much larger than expected.

Single-molecule observation of protein-protein interactions in the chaperonin system

We have analyzed the dynamics of the chaperonin (GroEL)-cochaperonin (GroES) interaction at the single-molecule level. In the presence of ATP and non-native protein, binding of GroES to the immobilized GroEL occurred at a rate that is consistent with bulk kinetics measurements. However, the release of GroES from GroEL occurred after a lag period ( approximately 3 s) that was not recognized in earlier bulk-phase studies. This observation suggests a new kinetic intermediate in the GroEL-GroES reaction pathway.

Mapping protein-protein interactions with combinatorial biology methods

Extremely diverse, DNA-encoded libraries of peptides and proteins have been constructed that include a linkage between each polypeptide and the encoding DNA. Library members can be selected by virtue of a particular binding specificity, and their protein sequence can be deduced from the sequence of the cognate DNA. Such combinatorial biology methods have proven invaluable in both identifying natural protein-protein interactions and also in mapping the specificities and energetics of these interactions in fine detail.

Imaging biochemistry inside cells

Proteins provide the building blocks for multicomponent molecular units, or pathways, from which higher cellular functions emerge. These units consist of either assemblies of physically interacting proteins or dispersed biochemical activities connected by rapidly diffusing second messengers, metabolic intermediates, ions or other proteins. It will probably remain within the realm of genetics to identify the ensemble of proteins that constitute these functional units and to establish the first-order connectivity. The dynamics of interactions within these protein machines can be assessed in living cells by the application of fluorescence spectroscopy on a microscopic level, using fluorescent proteins that are introduced within these functional units. Fluorescence is sensitive, specific and non-invasive, and the spectroscopic properties of a fluorescent probe can be analysed to obtain information on its molecular environment. The development and use of sensors based on the genetically encoded variants of green-fluorescent proteins has facilitated the observation of 'live' biochemistry on a microscopic level, with the advantage of preserving the cellular context of biochemical connectivity, compartmentalization and spatial organization. Protein activities and interactions can be imaged and localized within a single cell, allowing correlation with phenomena such as the cell cycle, migration and morphogenesis.