Protein-protein Interaction Networks: from Interactions to Networks

Network-based artificial intelligence approaches for advancing personalized psychiatry

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.

Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein-protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.

Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal ways to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis.

Functional interaction of nicotinic acetylcholine receptors and Na+/K+ ATPase from Locusta migratoria manilensis (Meyen)

Associated proteins are important for the correct functioning of nicotinic acetylcholine receptors (nAChRs). In the present study, a neonicotinoid-agarose affinity column was used to isolate related proteins from a solubilized membrane preparation from the nervous system of Locusta migratoria manilensis (Meyen). 1530 peptides were identified and most of them were involved in the membranous structure, molecular interaction and cellular communication. Among these peptides, Na⁺/K⁺ ATPase had the highest MASCOT score and were involved in the molecular interaction, which suggested that Na⁺/K⁺ ATPase and nAChRs might have strong and stable interactions in insect central nervous system. In the present study, functional interactions between nAChRs and Na⁺/K⁺ ATPase were examined by heterologous expression in Xenopus oocytes. The results showed that the activated nAChRs increased pump currents of Na⁺/K⁺ ATPase, which did not require current flow through open nAChRs. In turn, Na⁺/K⁺ ATPase significantly increased agonist sensitivities of nAChRs in a pump activity-independent manner and reduced the maximum current (I_max) of nAChRs. These findings provide novel insights concerning the functional interactions between insect nAChRs and Na⁺/K⁺ ATPase.

Identifying biological pathways that underlie primordial short stature using network analysis

Mutations in CUL7, OBSL1 and CCDC8, leading to disordered ubiquitination, cause one of the commonest primordial growth disorders, 3-M syndrome. This condition is associated with i) abnormal p53 function, ii) GH and/or IGF1 resistance, which may relate to failure to recycle signalling molecules, and iii) cellular IGF2 deficiency. However the exact molecular mechanisms that may link these abnormalities generating growth restriction remain undefined. In this study, we have used immunoprecipitation/mass spectrometry and transcriptomic studies to generate a 3-M 'interactome', to define key cellular pathways and biological functions associated with growth failure seen in 3-M. We identified 189 proteins which interacted with CUL7, OBSL1 and CCDC8, from which a network including 176 of these proteins was generated. To strengthen the association to 3-M syndrome, these proteins were compared with an inferred network generated from the genes that were differentially expressed in 3-M fibroblasts compared with controls. This resulted in a final 3-M network of 131 proteins, with the most significant biological pathway within the network being mRNA splicing/processing. We have shown using an exogenous insulin receptor (INSR) minigene system that alternative splicing of exon 11 is significantly changed in HEK293 cells with altered expression of CUL7, OBSL1 and CCDC8 and in 3-M fibroblasts. The net result is a reduction in the expression of the mitogenic INSR isoform in 3-M syndrome. From these preliminary data, we hypothesise that disordered ubiquitination could result in aberrant mRNA splicing in 3-M; however, further investigation is required to determine whether this contributes to growth failure.

Intermolecular interactions of the malate synthase of Paracoccidioides spp

Background

The fungus Paracoccidioides spp is the agent of paracoccidioidomycosis (PCM), a pulmonary mycosis acquired by the inhalation of fungal propagules. Paracoccidioides malate synthase (PbMLS) is important in the infectious process of Paracoccidioides spp because the transcript is up-regulated during the transition from mycelium to yeast and in yeast cells during phagocytosis by murine macrophages. In addition, PbMLS acts as an adhesin in Paracoccidioides spp. The evidence for the multifunctionality of PbMLS indicates that it could interact with other proteins from the fungus and host. The objective of this study was to identify and analyze proteins that possibly bind to PbMLS (PbMLS-interacting proteins) because protein interactions are intrinsic to cell processes, and it might be possible to infer the function of a protein through the identification of its ligands.

Results

The search for interactions was performed using an in vivo assay with a two-hybrid library constructed in S. cerevisiae; the transcripts were sequenced and identified. In addition, an in vitro assay using pull-down GST methodology with different protein extracts (yeast, mycelium, yeast-secreted proteins and macrophage) was performed, and the resulting interactions were identified by mass spectrometry (MS). Some of the protein interactions were confirmed by Far-Western blotting using specific antibodies, and the interaction of PbMLS with macrophages was validated by indirect immunofluorescence and confocal microscopy. In silico analysis using molecular modeling, dynamics and docking identified the amino acids that were involved in the interactions between PbMLS and PbMLS-interacting proteins. Finally, the interactions were visualized graphically using Osprey software.

Conclusion

These observations indicate that PbMLS interacts with proteins that are in different functional categories, such as cellular transport, protein biosynthesis, modification and degradation of proteins and signal transduction. These data suggest that PbMLS could play different roles in the fungal cell.

Boolean modeling of biological regulatory networks: a methodology tutorial

Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.

Proteomics in evolutionary ecology: linking the genotype with the phenotype

The study of the proteome (proteomics), which includes the dynamics of protein expression, regulation, interactions and its function, has played a less prominent role in evolutionary and ecological investigations in comparison with the study of the genome and transcriptome. There are, however, a number of arguments suggesting that this situation should change. First, the proteome is closer to the phenotype than the genome or the transcriptome, and as such may be more directly responsive to natural selection, and thus closely linked to adaptation. Second, there is evidence of a low correlation between protein and transcript expression levels across genes in many different organisms. Finally, there have been some recent important technological improvements in proteomics methods that make them feasible, practical and useful to address a wide range of evolutionary questions even in nonmodel organisms. The different proteomic methods, their limitations and problems when interpreting empirical data are described and discussed. In addition, the proteomic literature pertaining to evolutionary ecology is reviewed with examples, and potential applications of proteomics in a variety of evolutionary contexts are outlined. New proteomic research trends such as the study of posttranslational modifications and protein-protein interactions, as well as the combined use of the different -omics approaches, are discussed in relation to the development of a more functional and integrated perspective, needed for achieving a more comprehensive knowledge of evolutionary change.

Molecular motions in drug design: the coming age of the metadynamics method

Metadynamics is emerging as a useful free energy method in physics, chemistry and biology. Recently, it has been applied also to investigate ligand binding to biomolecules of pharmacological interest. Here, after introducing the basic idea of the method, we review applications to challenging targets for pharmaceutical intervention. We show that this methodology, especially when combined with a variety of other computational approaches such as molecular docking and/or molecular dynamics simulation, may be useful to predict structure and energetics of ligand/target complexes even when the targets lack a deep binding cavity, such as DNA and proteins undergoing fibrillation in neurodegenerative diseases. Furthermore, the method allows investigating the routes of molecular recognition and the associated binding energy profiles, providing a molecular interpretation to experimental data.

Intracellular complexes of the beta2 subunit of the nicotinic acetylcholine receptor in brain identified by proteomics

Nicotine acetylcholine receptors (nAChRs) comprise a family of ligand-gated channels widely expressed in the mammalian brain. The beta2 subunit is an abundant protein subunit critically involved in the cognitive and behavioral properties of nicotine as well as in the mechanisms of nicotine addiction. In this work, we used matrix-assisted laser desorption ionization time-of-flight tandem mass spectrometry (MALDI-TOF-TOF MS/MS) to uncover protein interactions of the intracellular loop of the beta2 subunit and components of immunoprecipitated beta2-nAChR complexes from mouse brain. Using the beta2-knockout mouse to exclude nonspecific binding to the beta2 antibody, we identify 21 nAChR-interacting proteins (NIPs) expressed in brain. Western blot analysis confirmed the association between the beta2 subunit and candidate NIPs. Based on their functional profiles, the hypothesis is suggested that the identified NIPs can regulate the trafficking and signaling of the beta2-nAChR. Interactions of the beta2 subunit with NIPs such as G protein alpha, G protein-regulated inducer of neurite outgrowth 1, and G protein-activated K(+) channel 1 suggest a link between nAChRs and cellular G protein pathways. These findings reveal intracellular interactions of the beta2 subunit and may contribute to the understanding of the mechanisms of nAChR signaling and trafficking in neurons.

Systems analysis of chaperone networks in the malarial parasite Plasmodium falciparum

Molecular chaperones participate in the maintenance of cellular protein homeostasis, cell growth and differentiation, signal transduction, and development. Although a vast body of information is available regarding individual chaperones, few studies have attempted a systems level analysis of chaperone function. In this paper, we have constructed a chaperone interaction network for the malarial parasite, Plasmodium falciparum. P. falciparum is responsible for several million deaths every year, and understanding the biology of the parasite is a top priority. The parasite regularly experiences heat shock as part of its life cycle, and chaperones have often been implicated in parasite survival and growth. To better understand the participation of chaperones in cellular processes, we created a parasite chaperone network by combining experimental interactome data with in silico analysis. We used interolog mapping to predict protein-protein interactions for parasite chaperones based on the interactions of corresponding human chaperones. This data was then combined with information derived from existing high-throughput yeast two-hybrid assays. Analysis of the network reveals the broad range of functions regulated by chaperones. The network predicts involvement of chaperones in chromatin remodeling, protein trafficking, and cytoadherence. Importantly, it allows us to make predictions regarding the functions of hypothetical proteins based on their interactions. It allows us to make specific predictions about Hsp70-Hsp40 interactions in the parasite and assign functions to members of the Hsp90 and Hsp100 families. Analysis of the network provides a rational basis for the anti-malarial activity of geldanamycin, a well-known Hsp90 inhibitor. Finally, analysis of the network provides a theoretical basis for further experiments designed toward understanding the involvement of this important class of molecules in parasite biology.

A split enhanced green fluorescent protein-based reporter in yeast two-hybrid system

We have developed a novel reporter system involving a yeast two-hybrid assay, which utilizes the reconstitution of the split EGFP reporter in order to characterize the relevant protein-protein interactions. To our knowledge, this study represents the first application of the split EGFP system as a read-out in a yeast two-hybrid assay. In comparison with the existing two-hybrid system, the bait and prey vectors were improved with regard to the reporter and the replication control element. As a result, the reconstituted EGFP has been observed to evidence a restored fluorescence upon protein-protein interactions in yeast, thereby allowing for the characterization of its interactor. The use of a split EGFP reporter has some salient advantages. Firstly, no substrates are required for the production of fluorescence. Secondly, low copy number plasmids may help to solve the protein toxicity problem, via the reduction of expression. Thirdly, this technique may prove useful in overcoming the autoactivation problem, due to the fact that the read-out of the yeast two-hybrid system is transcription-independent. Collectively, our results showed that the split EGFP reporter system might potentially be applied in yeast two-hybrid assays for the high-throughput screening of protein-protein interactions, with a simple and direct fluorescent read-out.

Capillary electrophoresis of signaling molecules

The emerging field of quantitative systems biology uses high-throughput bioanalytical measurements to gain a deeper understanding of biological phenomena. With the advent of instrumentation platforms, capillary electrophoresis spans a very wide range of biological applications. This short article focuses on the exploitation of capillary electrophoresis for the systems-level analysis of cell signaling molecules.

Filamentous temperature-sensitive Z (FtsZ) isoforms specifically interact in the chloroplasts and in the cytosol of Physcomitrella patens

Plant filamentous temperature-sensitive Z (FtsZ) proteins have been reported to be involved in biological processes related to plastids. However, the precise functions of distinct isoforms are still elusive. Here, the intracellular localization of the FtsZ1-1 isoform in a moss, Physcomitrella patens, was examined. Furthermore, the in vivo interaction behaviour of four distinct FtsZ isoforms was investigated. Localization studies of green fluorescent protein (GFP)-tagged FtsZ1-1 and fluorescence resonance energy transfer (FRET) analyses employing all dual combinations of four FtsZ isoforms were performed in transient protoplast transformation assays. FtsZ1-1 is localized to network structures inside the chloroplasts and exerts influence on plastid division. Interactions between FtsZ isoforms occur in distinct ordered structures in the chloroplasts as well as in the cytosol. The results expand the view of the involvement of Physcomitrella FtsZ proteins in chloroplast and cell division. It is concluded that duplication and diversification of ftsZ genes during plant evolution were the main prerequisites for the successful remodelling and integration of the prokaryotic FtsZ-dependent division mechanism into the cellular machineries of distinct complex processes in plants.

Capturing protein-protein complexes at equilibrium: the holdup comparative chromatographic retention assay

The popular pulldown chromatographic assay detects complexes mediated by fusion proteins retained on affinity resin. The main limitation of this method is that it does not analyze complexes at equilibrium but after several washing steps. Consequently, fast-dissociating complexes may remain undetected. Here, we present the holdup assay, based on the principle of comparative chromatographic retention which eliminates the use of washing steps. The assay evaluates fractions of free and bound species at equilibrium. We used human papillomavirus oncoprotein E6, an E6-binding peptide and an E6-binding PDZ domain, to test several protocols utilizing pure proteins or expression extracts. The holdup assay is faster and more informative than the pulldown assay. It detects fast-dissociating complexes and it is also suited for evaluating equilibrium constants. It is potentially adaptable for automated determination of affinity constants and high-throughput analysis of interactions between proteins and other proteins, peptides, nucleic acids, or small regulatory molecules.

Surface plasmon resonance imaging analysis of protein–protein interactions using on-chip-expressed capture protein

A surface plasmon resonance (SPR) imaging system, combined with a microwell gold chip for on-chip cell cultivation, was used to monitor protein-protein interactions. In particular, we developed an on-chip microscale cell cultivation system that integrates cell culture and on-chip analysis of protein-protein interactions on a single microwell chip in a time- and labor-saving manner. To assess the performance of this system in the analysis of protein-protein interactions, we conducted a series of protein-protein interaction analyses by measuring the binding of the yeast GAL4 dimerization domain (GAL4DD) to the GAL11 protein (GAL11P). Our system was found to enable the simple and rapid analysis of protein-protein interactions, requiring no special cell culturing equipment or recombinant protein expression prior to the immobilization of the purified proteins onto the chip. Our results demonstrate that the combination of an on-chip cell cultivation system and an SPR imaging system can be a useful tool to study protein-protein interactions without the need for time-consuming and labor-intensive protein preparation steps as well as fluorescent or other labeling of the interactants.

Interaction proteomics

The term proteome is traditionally associated with the identification of a large number of proteins within complex mixtures originating from a given organelle, cell or even organism. Current proteome investigations are basically focused on two major areas, expression proteomics and functional proteomics. Both approaches rely on the fractionation of protein mixtures essentially by two-dimensional polyacrylamide gel electrophoresis (2D-gel) and the identification of individual protein bands by mass spectrometric techniques (2D-MS). Functional proteomics approaches are basically addressing two main targets, the elucidation of the biological function of unknown proteins and the definition of cellular mechanisms at the molecular level. In the cell many processes are governed not only by the relative abundance of proteins but also by rapid and transient regulation of activity, association and localization of proteins and protein complexes. The association of an unknown protein with partners belonging to a specific protein complex involved in a particular process would then be strongly suggestive of its biological function. The identification of interacting proteins in stable complexes in a cellular system is essentially achieved by affinity-based procedures. Different strategies relying on this simple concept have been developed and a brief overview of the main approaches presently used in functional proteomics studies is described.

Capillary electrophoresis at the omics level: Towards systems biology

Emerging systems biology aims at integrating the enormous amount of existing omics data in order to better understand their functional relationships at a whole systems level. These huge datasets can be obtained through advances in high-throughput, sensitive, precise, and accurate analytical instrumentation and technological innovation. Separation sciences play an important role in revealing biological processes at various omic levels. From the perspective of systems biology, CE is a strong candidate for high-throughput, sensitive data generation which is capable of tackling the challenges in acquiring qualitative and quantitative knowledge through a system-level study. This review focuses on the applicability of CE to systems-based analytical data at the genomic, transcriptomic, proteomic, and metabolomic levels.

Emerging tools for real-time label-free detection of interactions on functional protein microarrays

The availability of extensive genomic information and content has spawned an era of high-throughput screening that is generating large sets of functional genomic data. In particular, the need to understand the biochemical wiring within a cell has introduced novel approaches to map the intricate networks of biological interactions arising from the interactions of proteins. The current technologies for assaying protein interactions--yeast two-hybrid and immunoprecipitation with mass spectrometric detection--have met with considerable success. However, the parallel use of these approaches has identified only a small fraction of physiologically relevant interactions among proteins, neglecting all nonprotein interactions, such as with metabolites, lipids, DNA and small molecules. This highlights the need for further development of proteome scale technologies that enable the study of protein function. Here we discuss recent advances in high-throughput technologies for displaying proteins on functional protein microarrays and the real-time label-free detection of interactions using probes of the local index of refraction, carbon nanotubes and nanowires, or microelectromechanical systems cantilevers. The combination of these technologies will facilitate the large-scale study of protein interactions with proteins as well as with other biomolecules.

The potential of proteomics and peptidomics for allergy and asthma research

Progress in the field of proteomics, the branch of biology that studies the full set of proteins derived from a given genome, is moving fast. Two-dimensional gel electrophoresis (2DG) separation of complex protein mixtures and the subsequent analysis of isolated protein spots by mass spectrometry allow fast and accurate identification of proteins. The comparison of spots from different samples separated on customized 2D gels allows the detection of punctual differences in their mobility and facilitates tracing back differences in protein expression, presence of isoforms, splice variants and posttranslational modifications by mass spectrometry. In spite of significant analytical challenges owing to the high complexity of the proteome and the challenge deriving from the necessity to process huge amounts of raw data generated by mass spectrometric profiling, proteomics has evolved to an indispensable tool in life sciences. A restricted window of the proteome that consists of peptides and small proteins not easily manageable by conventional gel electrophoresis prompted the development of separation methods based on liquid chromatography. This new research field termed peptidomics already contributed, together with proteomics to enlarge our knowledge about biological processes and supported by sophisticated bioinformatics tools, to the discovery of new diagnostic and therapeutic targets. The technological capabilities of biophysical separation, mass spectrometry and bioinformatics form the basis of discovery programs that aim at mining the proteome starting from microgram amounts of protein extracts derived from body fluids and tissues. Proteomics and peptidomics have a great potential to speed up allergy and asthma research, where disease- and tissue-specific samples are easy to obtain.

An analysis of extensible modelling for functional genomics data

Background

Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time.

Results

In this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure.

Conclusion

From our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines.

Fluorescence-Based Cloning of a Protein Tyrosine Kinase with a Yeast Tribrid System

Post-translational modifications of proteins control myriad biological functions. However, relatively few methods exist for the identification of the enzymes that catalyze these modifications. To expand this repertoire, we report a yeast genetic approach that enables the identification of protein tyrosine kinases (PTKs) from cDNA libraries. Yeasts were transformed with four vectors encoding: 1) a potentially universal PTK substrate fused to the LexA DNA binding domain, 2) the Grb2-SH2 domain fused to the B42 activation domain, 3) a fluorescent reporter gene controlled by LexA DNA sites, and 4) a Jurkat cDNA library. Transient expression of PTKs, such as the lymphocyte-specific kinase Fyn, resulted in phosphorylation of the DNA-bound substrate, recruitment of the Grb2-SH2 domain, and activation of the fluorescent reporter gene. This brief induction of protein expression circumvented the potential toxicity of PTKs to the yeast. Fluorescence activated cell sorting (FACS) enabled isolation of PTKs, and these enzymes were further characterized by flow cytometry and immunoblotting. This approach provides a potentially general method for the identification and evaluation of enzymes involved in the post-translational modification of proteins.

Functional proteomics

BACKGROUND

With the increase in the number of genome sequencing projects, there is a concomitant exponential growth in the number of protein sequences whose function is still unknown. Functional proteomics constitutes an emerging research area in the proteomic field whose approaches are addressed towards two major targets: the elucidation of the biological function of unknown proteins and the definition of cellular mechanisms at the molecular level.

METHODS

The identification of interacting proteins in stable complexes in vivo is essentially achieved by affinity-based procedures. The basic idea is to express the protein of interest with a suitable tag to be used as a bait to fish its specific partners out from a cellular extract. Individual components within the multi-protein complex can then be identified by mass spectrometric methodologies.

RESULTS AND CONCLUSIONS

The association of an unknown protein with partners belonging to a specific protein complex involved in a particular mechanism is strongly suggestive of the biological function of the protein. Moreover, the identification of protein partners interacting with a given protein will lead to the description of cellular mechanisms at the molecular level. The next goal will be to generate animal models bearing a tagged form of the bait protein.

Confirmation of human protein interaction data by human expression data

BACKGROUND

With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome.

RESULTS

We investigated the quantitative relationship between expression levels of genes encoding interacting proteins and genes encoding random protein pairs. Therefore we studied 1369 interacting human protein pairs and human gene expression levels of 155 arrays. We were able to establish a statistically significantly higher correlation between the expression levels of genes whose proteins interact compared to random protein pairs. Additionally we were able to provide evidence that genes encoding proteins belonging to the same GO-class show correlated expression levels.

CONCLUSION

This finding is concurrent with the naive hypothesis that the scales of production of interacting proteins are linked because an efficient interaction demands that involved proteins are available to some degree. The goal of further research in this field will be to understand the biological mechanisms behind this observation.

Managing genomic and proteomic knowledge

Genomic and proteomic platform data constitute a hugely important resource to current efforts in disease understanding, systems biology and drug discovery. We review prerequisites for the adequate management of 'omic' data, the means by which such data are analyzed and converted to knowledge relevant to drug discovery and issues crucial to the integration of such data, particularly with chemical, genetic and clinical data.:

The use of enzyme mixtures for complex biosyntheses

Biocatalysis is currently employed to produce known substances more economically and for the synthesis of new compounds. Increased production or novel product synthesis can be achieved through the use of mutant organisms, tailored enzymes or novel combinations of enzymes in reactors. These complex biosyntheses, once only in the realm of the biopharmaceutical industry, have now been embraced by the food and textile industries and are finding geochemical and environmental applications. New uses are dictating novel methods of manufacture that utilize knowledge of systems level biology. Increased understanding of the functional interaction of proteins and protein-protein networks is also altering the practice of in vitro biosynthesis.

Interaction networks: coordinating responses to xenobiotic exposure

In the last decade the increased usage of '-omic' technologies, plus the sequencing of over 800 complete genomes has led to a vast increase in the amount of information available to the researcher for examining cellular responses to xenobiotics. Much effort has been put into the identification and analysis of expression profiles associated with pathobiological conditions and/or xenobiotic exposure. These profiles are commonly used in two applications. Firstly, comparative profile experiments are used to classify pathobiological states and for the screening of novel chemical entities to predict their action(s) on the body. Secondly, mechanistic investigations will gain information on the molecular mechanisms underlying toxic responses/pathobiological states. During the course of such analysis it has become increasingly clear that a series of highly refined interaction networks exist within the body, regulating both the sensitivity and selectivity of the body's response to pathobiological states/xenobiotic exposure. These interaction networks exist at several levels: Firstly, within individual cells, the interaction between factors that transmit xenobiotics signals will determine the overall cellular response. Secondly, intraorgan communication occurs between the different cell types/sub-types which makes up an organ, coordinating the overall organ response. Finally, interorgan interactions provide axes of response through the body.

Tackling the plant proteome: practical approaches, hurdles and experimental tools

The study of complex biological questions through comparative proteomics is becoming increasingly attractive to plant biologists as the rapidly expanding plant genomic and expressed sequence tag databases provide improved opportunities for protein identification. This review focuses on practical issues associated with comparative proteomic analysis, including the challenges of effective protein extraction and separation from plant tissues, the pros and cons of two-dimensional gel-based analysis and the problems of identifying proteins from species that are not recognized models for functional genomic studies. Specific points are illustrated using data from an ongoing study of the tomato and pepper fruit proteomes.