Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Focus on plant proteomics.

Proteomics covers the systematic analysis of proteins expressed by a genome, from the identification of their primary amino-acid sequence to the determination of their relative amounts, their state of modification and association with other proteins or molecules of different types. Tremendous progress has been made in this field in the past few years, especially in plant biology, mostly due to major developments of mass spectrometry dedicated to protein analyses and advanced information technology. The aim of this special issue of Plant Physiology and Biochemistry devoted to Plant Proteomics is not to present a comprehensive coverage of this rapidly expanding field but to focus on the representation of some key aspects to illustrate the importance of proteomics in plant functional genomics.     

Do protein-protein interaction databases identify moonlighting proteins?

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

Approaching Drosophila development through proteomic tools and databases: At the hub of the post-genomic era

The past decade has witnessed an explosion in the growth of proteomics. The completion of numerous genome sequences, the development of powerful protein analytical technologies, as well as the design of innovative bioinformatics tools have marked the beginning of a new post-genomic era. Proteomics, the large-scale analysis of proteins in an organism, organ or organelle encompasses different aspects: (1) the identification, analysis of post-translational modifications and quantification of proteins; (2) the study of protein-protein interactions; and (3) the functional analysis of interactome networks. Here, we briefly summarize the emerging analytical tools and databases that are paving the way for studying Drosophila development by proteomic approaches.     

Proteomic studies in plants

Proteomics is a leading technology for the high-throughput analysis of proteins on a genome-wide scale. With the completion of genome sequencing projects and the development of analytical methods for protein characterization, proteomics has become a major field of functional genomics. The initial objective of proteomics was the large-scale identification of all protein species in a cell or tissue. The applications are currently being extended to analyze various functional aspects of proteins such as post-translational modifications, protein-protein interactions, activities and structures. Whereas the proteomics research is quite advanced in animals and yeast as well as Escherichia coli, plant proteomics is only at the initial phase. Major studies of plant proteomics have been reported on subcellular proteomes and protein complexes (e.g. proteins in the plasma membranes, chloroplasts, mitochondria and nuclei). Here several plant proteomics studies will be presented, followed by a recent work using multidimensional protein identification technology (MudPIT).     

Mitochondria in the pathogenesis of diabetes: a proteomic view

Diabetes mellitus is a complex metabolic disorder characterized by chronic hyperglycemia due to absolute or relative lack of insulin. Though great efforts have been made to investigate the pathogenesis of diabetes, the underlying mechanism behind the development of diabetes and its complications remains unexplored. Cumulative evidence has linked mitochondrial modification to the pathogenesis of diabetes and its complications and they are also observed in various tissues affected by diabetes. Proteomics is an attractive tool for the study of diabetes since it allows researchers to compare normal and diabetic samples by identifying and quantifying the differentially expressed proteins in tissues, cells or organelles. Great progress has already been made in mitochondrial proteomics to elucidate the role of mitochondria in the pathogenesis of diabetes and its complications. Further studies on the changes of mitochondrial protein specifically post-translational modifications during the diabetic state using proteomic tools, would provide more information to better understand diabetes.     

Molecular biologist's guide to proteomics.

The emergence of proteomics, the large-scale analysis of proteins, has been inspired by the realization that the final product of a gene is inherently more complex and closer to function than the gene itself. Shortfalls in the ability of bioinformatics to predict both the existence and function of genes have also illustrated the need for protein analysis. Moreover, only through the study of proteins can posttranslational modifications be determined, which can profoundly affect protein function. Proteomics has been enabled by the accumulation of both DNA and protein sequence databases, improvements in mass spectrometry, and the development of computer algorithms for database searching. In this review, we describe why proteomics is important, how it is conducted, and how it can be applied to complement other existing technologies. We conclude that currently, the most practical application of proteomics is the analysis of target proteins as opposed to entire proteomes. This type of proteomics, referred to as functional proteomics, is always driven by a specific biological question. In this way, protein identification and characterization has a meaningful outcome. We discuss some of the advantages of a functional proteomics approach and provide examples of how different methodologies can be utilized to address a wide variety of biological problems.     

Molecular Biologist's Guide to Proteomics

The emergence of proteomics, the large-scale analysis of proteins, has been inspired by the realization that the final product of a gene is inherently more complex and closer to function than the gene itself. Shortfalls in the ability of bioinformatics to predict both the existence and function of genes have also illustrated the need for protein analysis. Moreover, only through the study of proteins can posttranslational modifications be determined, which can profoundly affect protein function. Proteomics has been enabled by the accumulation of both DNA and protein sequence databases, improvements in mass spectrometry, and the development of computer algorithms for database searching. In this review, we describe why proteomics is important, how it is conducted, and how it can be applied to complement other existing technologies. We conclude that currently, the most practical application of proteomics is the analysis of target proteins as opposed to entire proteomes. This type of proteomics, referred to as functional proteomics, is always driven by a specific biological question. In this way, protein identification and characterization has a meaningful outcome. We discuss some of the advantages of a functional proteomics approach and provide examples of how different methodologies can be utilized to address a wide variety of biological problems.     

The reconstruction and analysis of tissue specific human metabolic networks

Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .     

Common interchange standards for proteomics data: Public availability of tools and schema

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparision, exchange and verification. To this end, a Level 1 Molecular Interaction XML data exchange format has been developed which has been accepted for publication and is freely available at the PSI website (http.//psidev.sf.net/). Several major protein interaction databases are already making data available in this format. A draft XML interchange format for mass spectrometry data has been written and is currently undergoing evaluation whilst work is ongoing to develop a proteomics data integration model, MIAPE.     

Vectorial proteomics

Vectorial proteomics is a methodology for the differential identification and characterization of proteins and their domains exposed to the opposite sides of biological membranes. Proteomics of membrane vesicles from defined isolated membranes automatically determine cellular localization of the identified proteins and reduce complexity of protein characterizations. The enzymatic shaving of naturally-oriented, or specifically-inverted sealed membrane vesicles, release the surface-exposed peptides from membrane proteins. These soluble peptides are amenable to various chromatographic separations and to sequencing by mass spectrometry, which provides information on the topology of membrane proteins and on their posttranslational modifications. The membrane shaving techniques have made a breakthrough in the identification of in vivo protein phosphorylation sites in membrane proteins form plant photosynthetic and plasma membranes, and from caveolae membrane vesicles of human fat cells. This approach has also allowed investigation of dynamics for in vivo protein phosphorylation in membranes from cells exposed to different conditions. Vectorial proteomics of membrane vesicles with retained peripheral proteins identify extrinsic proteins associated with distinct membrane surfaces, as well as a variety of posttranslational modifications in these proteins. The rapid integration of versatile vectorial proteomics techniques in the functional characterization of biological membranes is anticipated to bring significant insights in cell biology.     

Intact protein profiling in breast cancer biomarker discovery: Protein identification issue and the solutions based on 3D protein separation,bottom‐up and top‐down mass spectrometry

Proteomics profiling of intact proteins based on MALDI‐TOF MS and derived platforms has been used in cancer biomarker discovery studies. This approach suffers from a number of limitations such as low resolution, low sensitivity, and that no knowledge is available on the identity of the respective proteins in the discovery mode. Nevertheless, it remains the most high‐throughput, untargeted mode of clinical proteomics studies to date. Here we compare key protein separation and MS techniques available for protein biomarker identification in this type of studies and define reasons of uncertainty in protein peak identity. As a result of critical data analysis, we consider 3D protein separation and identification workflows as optimal procedures. Subsequently, we present a new protocol based on 3D LC‐MS/MS with top‐down at high resolution that enabled the identification of HNRNP A2/B1 intact peptide as correlating with the estrogen receptor expression in breast cancer tissues. Additional development of this general concept toward next generation, top‐down based protein profiling at high resolution is discussed.     

An efficient algorithm for large-scale detection of protein families

Detection of protein families in large databases is one of the principal research objectives in structural and functional genomics. Protein family classification can significantly contribute to the delineation of functional diversity of homologous proteins, the prediction of function based on domain architecture or the presence of sequence motifs as well as comparative genomics, providing valuable evolutionary insights. We present a novel approach called TRIBE-MCL for rapid and accurate clustering of protein sequences into families. The method relies on the Markov cluster (MCL) algorithm for the assignment of proteins into families based on precomputed sequence similarity information. This novel approach does not suffer from the problems that normally hinder other protein sequence clustering algorithms, such as the presence of multi-domain proteins, promiscuous domains and fragmented proteins. The method has been rigorously tested and validated on a number of very large databases, including SwissProt, InterPro, SCOP and the draft human genome. Our results indicate that the method is ideally suited to the rapid and accurate detection of protein families on a large scale. The method has been used to detect and categorise protein families within the draft human genome and the resulting families have been used to annotate a large proportion of human proteins.     

Thermodynamic databases for proteins and protein-nucleic acid interactions

Thermodynamic data regarding proteins and their interactions are important for understanding the mechanisms of protein folding, protein stability, and molecular recognition. Although there are several structural databases available for proteins and their complexes with other molecules, databases for experimental thermodynamic data on protein stability and interactions are rather scarce. Thus, we have developed two electronically accessible thermodynamic databases. ProTherm, Thermodynamic Database for Proteins and Mutants, contains numerical data of several thermodynamic parameters of protein stability, experimental methods and conditions, along with structural, functional, and literature information. ProNIT, Thermodynamic Database for Protein-Nucleic Acid Interactions, contains thermodynamic data for protein-nucleic acid binding, experimental conditions, structural information of proteins, nucleic acids and the complex, and literature information. These data have been incorporated into 3DinSight, an integrated database for structure, function, and properties of biomolecules. A WWW interface allows users to search for data based on various conditions, with different display and sorting options, and to visualize molecular structures and their interactions. These thermodynamic databases, together with structural databases, help researchers gain insight into the relationship among structure, function, and thermodynamics of proteins and their interactions, and will become useful resources for studying proteins in the postgenomic era.     

Bioinformatics in proteomics

Several genome sequencing projects have recently been completed and the majority of human coding regions have been sequenced. In the next step many of the further studies will concentrate on proteins. Proteomics methods are essential for studying protein expression, activity, regulation and modifications. Bioinformatics is an integral part of proteomics research. The recent developments and applications in proteomics are discussed including mass spectrometry data analysis and interpretation, analysis and storage of the gel images to databases, gel comparison, and advanced methods to study e.g. protein co-expression, protein-protein interactions, as well as metabolic and cellular pathways. The significance of informatics in proteomics will gradually increase because of the advent of high-throughput methods relying on powerful data analysis.     

Proteome of a methicillin-resistant Staphylococcus aureus clinical strain of sequence type ST398

Proteomics is a powerful tool to analyze the differences in gene expression of bacterial strains. Staphylococcus aureus has long been recognized as an important pathogen in human disease. In order to investigate this pathogen, the proteome of a clinical methicillin-resistant S. aureus (MRSA) strain of the sequence type ST398 was determined using 2-DE. Using 2-DE we obtained a total of 105 spots the MRSA strain. Furthermore in correlation with bioinformatic databases, they allowed accurate identification and characterization of proteins, resulting in 227 identified proteins. There were found proteins related to basic function of the cell, but also proteins related to virulence like catalase, specific of S. aureus species, and proteins related to antibiotic resistance. Proteins associated with antibiotic resistance or virulence factors are related to genomic databases. The most abundant classes identified involved glycolysis, energy production, one-carbon metabolism, and oxidation-reduction process, all of which reflect an active metabolism. These results highlight the importance of proteomics to deepen in the knowledge of protein expression of MRSA strain of the lineage ST398, microorganism with diverse and important resistance mechanisms. With this proteome map we have an essential tool for a better understanding of this pathogen and providing new data for protein databases. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Challenges in mass spectrometry-based proteomics

During the last decade, protein analysis and proteomics have been established as new tools for understanding various biological problems. As the identification of proteins after classical separation techniques, such as two-dimensional gel electrophoresis, have become standard methods, new challenges arise in the field of proteomics. The development of "functional proteomics" combines functional characterization, like regulation, localization and modification, with the identification of proteins for deeper insight into cellular functions. Therefore, different mass spectrometric techniques for the analysis of post-translational modifications, such as phosphorylation and glycosylation, have been established as well as isolation and separation methods for the analysis of highly complex samples, e.g. protein complexes or cell organelles. Furthermore, quantification of protein levels within cells is becoming a focus of interest as mass spectrometric methods for relative or even absolute quantification have currently not been available. Protein or genome databases have been an essential part of protein identification up to now. Thus, de novo sequencing offers new possibilities in protein analytical studies of organisms not yet completely sequenced. The intention of this review is to provide a short overview about the current capabilities of protein analysis when addressing various biological problems.     

Chlamydomonas reinhardtii proteomics

Proteomics, based on the expanding genomic resources, has begun to reveal new details of Chlamydomonas reinhardtii biology. In particular, analyses focusing on subproteomes have already provided new insight into the dynamics and composition of the photosynthetic apparatus, the chloroplast ribosome, the oxidative phosphorylation machinery of the mitochondria, and the flagellum. It assisted to discovered putative new components of the circadian clockwork as well as shed a light on thioredoxin protein-protein interactions. In the future, quantitative techniques may allow large scale comparison of protein expression levels. Advances in software algorithms will likely improve the use of genomic databases for mass spectrometry (MS) based protein identification and validation of gene models that have been predicted from the genomic DNA sequences. Although proteomics has only been recently applied for exploring C. reinhardtii biology, it will likely be utilized extensively in the near future due to the already existing genetic, genomic, and biochemical tools.