Protein localization in proteomics

Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator and Drugs: Insights from Cellular Trafficking

The eukaryotic cell is organized into membrane-delineated compartments that are characterized by specific cadres of proteins sustaining biochemically distinct cellular processes. The appropriate subcellular localization of proteins is key to proper organelle function and provides a physiological context for cellular processes. Disruption of normal trafficking pathways for proteins is seen in several genetic diseases, where a protein's absence for a specific subcellular compartment leads to organelle disruption, and in the context of an individual, a disruption of normal physiology. Importantly, several drug therapies can also alter protein trafficking, causing unwanted side effects. Thus, a deeper understanding of trafficking pathways needs to be appreciated as novel therapeutic modalities are proposed. Despite the promising efficacy of novel therapeutic agents, the intracellular bioavailability of these compounds has proved to be a potential barrier, leading to failures in treatments for various diseases and disorders. While endocytosis of drug moieties provides an efficient means of getting material into cells, the subsequent release and endosomal escape of materials into the cytosol where they need to act has been a barrier. An understanding of cellular protein/lipid trafficking pathways has opened up strategies for increasing drug bioavailability. Approaches to enhance endosomal exit have greatly increased the cytosolic bioavailability of drugs and will provide a means of investigating previous drugs that may have been shelved due to their low cytosolic concentration.

Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology

Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.

MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria

Subcellular localization is a key functional characteristic of proteins. It is determined by signals encoded in the protein sequence. The experimental determination of subcellular localization is laborious. Thus, a number of computational methods have been developed to predict the protein location from sequence. However predictions made by different methods often disagree with each other and it is not always clear which algorithm performs best for the given cellular compartment. We benchmarked primary subcellular localization predictors for proteins from Gram-negative bacteria, PSORTb3, PSLpred, CELLO, and SOSUI-GramN, on a common dataset that included 1056 proteins. We found that PSORTb3 performs best on the average, but is outperformed by other methods in predictions of extracellular proteins. This motivated us to develop a meta-predictor, which combines the primary methods by using the logistic regression models, to take advantage of their combined strengths, and to eliminate their individual weaknesses. MetaLocGramN runs the primary methods, and based on their output classifies protein sequences into one of five major localizations of the Gram-negative bacterial cell: cytoplasm, plasma membrane, periplasm, outer membrane, and extracellular space. MetaLocGramN achieves the average Matthews correlation coefficient of 0.806, i.e. 12% better than the best individual primary method. MetaLocGramN is a meta-predictor specialized in predicting subcellular localization for proteins from Gram-negative bacteria. According to our benchmark, it performs better than all other tools run independently. MetaLocGramN is a web and SOAP server available for free use by all academic users at the URL http://iimcb.genesilico.pl/MetaLocGramN. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Systems biology beyond networks: generating order from disorder through self-organization

Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated.

High-throughput subcellular protein localization using transfected-cell arrays. Subcellular protein localization using cell arrays

Knowledge of protein localization within the cellular environment is critical for understanding the function of the protein and its regulatory networks. Protein localization data, however, have traditionally been accumulated from single or small-scale experiments. Transfected-cell arrays (TCAs) represent a robust alternative for the high-throughput analysis of gene/protein functions in mammalian cells. For protein localization studies, TCAs not only allow for the transfection and expression of over 1,000 genes in a single experiment but also make it feasible for simultaneous co-localization analyses of different subcellular compartments. In this chapter, we have described a protein co-localization protocol using transfected human cell arrays for a large set of cellular compartments, including the nucleus, ER, Golgi apparatus, mitochondrion, lysosome, peroxisome, and the microtubules, intermediate filaments and actin filaments. The application of these "organelle-co-localized cell arrays" facilitates the precise determination of the localizations of numerous recombinant proteins in a single experiment, making it currently the most efficient technique for high-throughput protein co-localization screening.

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/ .

Spinning-disk confocal microscopy of yeast

Spinning-disk confocal microscopy is an imaging technique that combines the out-of-focus light rejection of confocal microscopy with the high sensitivity of wide-field microscopy. Because of its unique features, it is well suited to high-resolution imaging of yeast and other small cells. Elimination of out-of-focus light significantly improves the image contrast and signal-to-noise ratio, making it easier to resolve and quantitate small, dim structures in the cell. These features make spinning-disk confocal microscopy an excellent technique for studying protein localization and dynamics in yeast. In this review, I describe the rationale behind using spinning-disk confocal imaging for yeast, hardware considerations when assembling a spinning-disk confocal scope, and methods for strain preparation and imaging. In particular, I discuss choices of objective lens and camera, choice of fluorescent proteins for tagging yeast genes, and methods for sample preparation.

Transglutaminase 2: a multi-functional protein in multiple subcellular compartments

Transglutaminase 2 (TG2) is a multifunctional protein that can function as a transglutaminase, G protein, kinase, protein disulfide isomerase, and as an adaptor protein. These multiple biochemical activities of TG2 account for, at least in part, its involvement in a wide variety of cellular processes encompassing differentiation, cell death, inflammation, cell migration, and wound healing. The individual biochemical activities of TG2 are regulated by several cellular factors, including calcium, nucleotides, and redox potential, which vary depending on its subcellular location. Thus, the microenvironments of the subcellular compartments to which TG2 localizes, such as the cytosol, plasma membrane, nucleus, mitochondria, or extracellular space, are important determinants to switch on or off various TG2 biochemical activities. Furthermore, TG2 interacts with a distinct subset of proteins and/or substrates depending on its subcellular location. In this review, the biological functions and molecular interactions of TG2 will be discussed in the context of the unique environments of the subcellular compartments to which TG2 localizes.

Bioinformatics predictions of localization and targeting

One of the major challenges in the post-genomic era with hundreds of genomes sequenced is the annotation of protein structure and function. Computational predictions of subcellular localization are an important step toward this end. The development of computational tools that predict targeting and localization has, therefore, been a very active area of research, in particular since the first release of the groundbreaking program PSORT in 1991. The most reliable means of annotating protein structure and function remains homology-based inference, i.e. the transfer of experimental annotations from one protein to its homologs. However, annotations about localization demonstrate how much can be gained from advanced machine learning: more proteins can be annotated more reliably. Contemporary computational tools for the annotation of protein targeting include automatic methods that mine the textual information from the biological literature and molecular biology databases. Some machine learning-based methods that accurately predict features of sorting signals and that use sequence-derived features to predict localization have reached remarkable levels of performance. Sustained prediction accuracy has increased by more than 30 percentage points over the last decade. Here, we review some of the most recent methods for the prediction of subcellular localization and protein targeting that contributed toward this breakthrough.

Construction of functional interaction networks through consensus localization predictions of the human proteome

Characterizing the subcellular localization of a protein provides a key clue for understanding protein function. However, different protein localization prediction programs often deliver conflicting results regarding the localization of the same protein. As the number of available localization prediction programs continues to grow, there is a need for a consensus prediction approach. To address this need, we developed a consensus localization prediction method called ConLoc based on a large-scale, systematic integration of 13 available programs that make predictions for five major subcellular localizations (cytosol, extracellular, mitochondria, nucleus, and plasma membrane). The ability of ConLoc to accurately predict protein localization was substantially better than existing programs. Using ConLoc prediction, we built a localization-guided functional interaction network of the human proteome and mapped known disease associations within this network. We found a high degree of shared disease associations among functionally interacting proteins that are localized to the same cellular compartment. Thus, the use of consensus localization prediction, such as ConLoc, is a new approach for the identification of novel disease associated genes.

Prediction of disease-related mutations affecting protein localization

BACKGROUND

Eukaryotic cells contain numerous compartments, which have different protein constituents. Proteins are typically directed to compartments by short peptide sequences that act as targeting signals. Translocation to the proper compartment allows a protein to form the necessary interactions with its partners and take part in biological networks such as signalling and metabolic pathways. If a protein is not transported to the correct intracellular compartment either the reaction performed or information carried by the protein does not reach the proper site, causing either inactivation of central reactions or misregulation of signalling cascades, or the mislocalized active protein has harmful effects by acting in the wrong place.

RESULTS

Numerous methods have been developed to predict protein subcellular localization with quite high accuracy. We applied bioinformatics methods to investigate the effects of known disease-related mutations on protein targeting and localization by analyzing over 22,000 missense mutations in more than 1,500 proteins with two complementary prediction approaches. Several hundred putative localization affecting mutations were identified and investigated statistically.

CONCLUSION

Although alterations to localization signals are rare, these effects should be taken into account when analyzing the consequences of disease-related mutations.

Protein-protein interaction as a predictor of subcellular location

Background

Many biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.

Results

Using publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence (empirical approach, multiplicity of databases, and multiplicity of literature citations) and three based on type of biological evidence (inferred biological process, domain-domain interactions, and orthology relationships), with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins.

Conclusion

Understanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions.

Protein subcellular localization prediction using artificial intelligence technology

Proteins perform many important tasks in living organisms, such as catalysis of biochemical reactions, transport of nutrients, and recognition and transmission of signals. The plethora of aspects of the role of any particular protein is referred to as its "function." One aspect of protein function that has been the target of intensive research by computational biologists is its subcellular localization. Proteins must be localized in the same subcellular compartment to cooperate toward a common physiological function. Aberrant subcellular localization of proteins can result in several diseases, including kidney stones, cancer, and Alzheimer's disease. To date, sequence homology remains the most widely used method for inferring the function of a protein. However, the application of advanced artificial intelligence (AI)-based techniques in recent years has resulted in significant improvements in our ability to predict the subcellular localization of a protein. The prediction accuracy has risen steadily over the years, in large part due to the application of AI-based methods such as hidden Markov models (HMMs), neural networks (NNs), and support vector machines (SVMs), although the availability of larger experimental datasets has also played a role. Automatic methods that mine textual information from the biological literature and molecular biology databases have considerably sped up the process of annotation for proteins for which some information regarding function is available in the literature. State-of-the-art methods based on NNs and HMMs can predict the presence of N-terminal sorting signals extremely accurately. Ab initio methods that predict subcellular localization for any protein sequence using only the native amino acid sequence and features predicted from the native sequence have shown the most remarkable improvements. The prediction accuracy of these methods has increased by over 30% in the past decade. The accuracy of these methods is now on par with high-throughput methods for predicting localization, and they are beginning to play an important role in directing experimental research. In this chapter, we review some of the most important methods for the prediction of subcellular localization.

Bayesian methods for proteomics

Biological and medical data have been growing exponentially over the past several years [1, 2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad, complex data [4-6]. A Bayesian approach can help capture such information and incorporate it seamlessly through a rigorous, probabilistic framework. This paper starts with a review of the background mathematics behind the Bayesian methodology: from parameter estimation to Bayesian networks. The article then goes on to discuss how emerging Bayesian approaches have already been successfully applied to research across proteomics, a field for which Bayesian methods are particularly well suited [7-9]. After reviewing the literature on the subject of Bayesian methods in biological contexts, the article discusses some of the recent applications in proteomics and emerging directions in the field.

Fluorescent proteins and their use in marine biosciences, biotechnology, and proteomics

This review explores the field of fluorescent proteins (FPs) from the perspective of their marine origins and their applications in marine biotechnology and proteomics. FPs occur in hydrozoan, anthozoan, and copepodan species, and possibly in other metazoan niches as well. Many FPs exhibit unique photophysical and photochemical properties that are the source of exciting research opportunities and technological development. Wild-type FPs can be enhanced by mutagenetic modifications leading to variants with optimized fluorescence and new functionalities. Paradoxically, the benefits from ocean-derived FPs have been realized, first and foremost, for terrestrial organisms. In recent years, however, FPs have also made inroads into aquatic biosciences, primarily as genetically encoded fluorescent fusion tags for optical marking and tracking of proteins, organelles, and cells. Examples of FPs and applications summarized here testify to growing utilization of FP-based platform technologies in basic and applied biology of aquatic organisms. Hydra, sea squirt, zebrafish, striped bass, rainbow trout, salmonids, and various mussels are only a few of numerous instances where FPs have been used to address questions relevant to evolutionary and developmental research and aquaculture.

Site-specific fluorescent labeling of poly-histidine sequences using a metal-chelating cysteine

Coupling genetically encoded target sequences with specific and selective labeling strategies has made it possible to utilize fluorescence spectroscopy in complex mixtures to investigate the structure, function, and dynamics of proteins. Thus, there is a growing need for a repertoire of such labeling approaches to deploy based on a given application and to utilize in combination with one another by orthogonal reactivity. We have developed a simple approach to synthesize a fluorescent probe that binds to a poly-histidine sequence. The amino group of cysteine was converted into nitrilotriacetate to create a metal-chelating cysteine molecule, Cys-nitrilotriacetate. Two Cys-nitrilotriacetate molecules were then cross-linked using dibromobimane to generate a fluorophore capable of binding a His-tag on a protein, NTA(2)-BM. NTA(2)-BM is a potential fluorophore for selective tagging of proteins in vivo.

Leishmania mexicana amazonensis: Development of a peptide tag useful for labeling and purifying biotinylated recombinant proteins

A number of peptide tags are available to facilitate the characterization of recombinant proteins. We have tested the bacterial oxaloacetate decarboxylase biotinylation domain for its efficacy in tagging recombinant proteins in vivo in Leishmania. To achieve efficient biotinylation, Leishmania also had to be co-transformed with the gene for bacterial biotin protein ligase (birA gene product). The recombinant chimeric protein could be detected on blots probed with avidin-horseradish peroxidase and purified on immobilized monomeric avidin resins.

Automated Image Analysis of Fluorescence Microscopic Images to Identify Protein-protein Interactions

The identification of protein-protein interactions along with their spatial and temporal localization is vital data for assigning functional information to proteins. Historically, these data sets obtained from fluorescence microscopy, have been analyzed manually, a process that is both time consuming and tedious. The development of an automated system that can measure the location dynamics of the interaction between two proteins inside a live cell is a high priority. This paper describes an automated image analysis system used to identify the interactions between two proteins of interest fused to either GFP or DIV IVA, a bacterial cell division protein that localizes to the cell poles [1]. Upon the induction of DIV IVA fusion protein expression, the GFP-fusion protein will be recruited to the cell poles if a positive interaction occurs. Advanced image processing and feature extraction algorithms are discussed in detail and a statistical feature set used to quantify the image-based information is developed.

Eclipsed distribution: A phenomenon of dual targeting of protein and its significance

One of the surprises from genome sequencing projects is the apparently small number of predicted genes in different eukaryotic cells, particularly human. One possible reason for this 'shortage' of genes is multiple distribution of proteins; a single protein is targeted to more than one subcellular compartment and consequently participates in different biochemical pathways and might have completely different functions. Indeed, in recent years, there have been reports on proteins that were found to be localized in cellular compartments other than those initially attributed to them. Furthermore, the phenomenon of highly uneven isoprotein distribution was recently observed and termed 'eclipsed distribution'. In these cases, the amount of one of the isoproteins, in one of the locations, is significantly minute and its detection by standard biochemical and visualization methods is masked by the presence of the dominant isoprotein. In fact, the minute amounts of eclipsed proteins can be essential. Since detecting eclipsed distribution is difficult, we assume that this phenomenon is probably more common than currently recorded. Hence, developing methods for localization and functional detection of eclipsed proteins is a challenge in cell biology research. Finally, eclipsed distribution may lead to cellular pathologies as has been suggested to occur in human disorders such as Prion diseases and Alzheimer. This review provides a short description of the eclipsed distribution phenomenon followed by an overview of protein distribution mechanisms, examples of eclipsed distribution and experimental approaches for revealing these elusive proteins.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

Integrating forward and reverse proteomics to unravel protein function

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.

Cell array-based intracellular localization screening reveals novel functional features of human chromosome 21 proteins

Background

Trisomy of human chromosome 21 (Chr21) results in Down's syndrome, a complex developmental and neurodegenerative disease. Molecular analysis of Down's syndrome, however, poses a particular challenge, because the aneuploid region of Chr21 contains many genes of unknown function. Subcellular localization of human Chr21 proteins may contribute to further understanding of the functions and regulatory mechanisms of the genes that code for these proteins. Following this idea, we used a transfected-cell array technique to perform a rapid and cost-effective analysis of the intracellular distribution of Chr 21 proteins.

Results

We chose 89 genes that were distributed over the majority of 21q, ranging from RBM11 (14.5 Mb) to MCM3AP (46.6 Mb), with part of them expressed aberrantly in the Down's syndrome mouse model. Open reading frames of these genes were cloned into a mammalian expression vector with an amino-terminal His₆ tag. All of the constructs were arrayed on glass slides and reverse transfected into HEK293T cells for protein expression. Co-localization detection using a set of organelle markers was carried out for each Chr21 protein. Here, we report the subcellular localization properties of 52 proteins. For 34 of these proteins, their localization is described for the first time. Furthermore, the alteration in cell morphology and growth as a result of protein over-expression for claudin-8 and claudin-14 genes has been characterized.

Conclusion

The cell array-based protein expression and detection approach is a cost-effective platform for large-scale functional analyses, including protein subcellular localization and cell phenotype screening. The results from this study reveal novel functional features of human Chr21 proteins, which should contribute to further understanding of the molecular pathology of Down's syndrome.

Technologies to shape the future: proteomics applications in anesthesiology and critical care medicine

Broadly speaking, proteomics is concerned with the simultaneous characterization of the features (for example, the concentration or activity) of the many different proteins that are typically found in biological or clinical specimens. The field is being driven forward both by innovative biotechnology companies and by academicians who are introducing the technology required for the parallel identification of individual proteins. The technology currently relies heavily on two-dimensional gel electrophoresis combined with mass spectrometry, but protein microarray chips are rapidly becoming a reality. Protein biomarkers are increasingly being recognized as crucially important for the study of disease processes, both from diagnostic and prognostic points of view. Proteome level studies will therefore be used increasingly both to identify and follow the course of various pathological conditions. In the specialty of anesthesiology, this technology will allow an improved understanding of the mechanisms of action of many of the drugs that are routinely administered in the operating room and also the effects of these therapeutic drugs on protein expression. In addition, proteomic studies will increasingly be used for both diagnostic and prognostic purposes in the intensive care unit and the chronic pain clinic.

Metabolic engineering in the -omics era: elucidating and modulating regulatory networks

The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent "-omics" approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications.

Automated analysis of fluorescence microscopy images to identify protein-protein interactions

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction. Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.

Engineering and characterization of a superfolder green fluorescent protein

Existing variants of green fluorescent protein (GFP) often misfold when expressed as fusions with other proteins. We have generated a robustly folded version of GFP, called 'superfolder' GFP, that folds well even when fused to poorly folded polypeptides. Compared to 'folding reporter' GFP, a folding-enhanced GFP containing the 'cycle-3' mutations and the 'enhanced GFP' mutations F64L and S65T, superfolder GFP shows improved tolerance of circular permutation, greater resistance to chemical denaturants and improved folding kinetics. The fluorescence of Escherichia coli cells expressing each of eighteen proteins from Pyrobaculum aerophilum as fusions with superfolder GFP was proportional to total protein expression. In contrast, fluorescence of folding reporter GFP fusion proteins was strongly correlated with the productive folding yield of the passenger protein. X-ray crystallographic structural analyses helped explain the enhanced folding of superfolder GFP relative to folding reporter GFP.

Refining protein subcellular localization

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality.

Eukaryotic cells are divided into various morphologically and functionally distinct compartments. Proteins must be targeted to the appropriate compartment to ensure proper function. Understanding protein subcellular localization is important to help understand not only the function of individual proteins but also the organization of the cell as a whole. Bioinformatic predictors of localization can provide such information quickly for large numbers of proteins. The authors of this paper have created a localization predictor called PSLT2 that considers the combinatorial presence of protein motifs and domains as well as protein interactions in yeast proteins. PSLT2 can predict the localization of all yeast proteins to nine different compartments: the endoplasmic reticulum, Golgi apparatus, cytosol, nucleus, peroxisome, plasma membrane, lysosome, mitochondrion, and extracellular space. The authors also investigated how to identify and predict proteins that localize to more than one compartment. They compared the localization predictions of PSLT2 to those determined through high-throughput tagging and microscopy experiments for yeast proteins. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway.

Miniaturization in functional genomics and proteomics

Proteins are the key components of the cellular machinery responsible for processing changes that are ordered by genomic information. Analysis of most human proteins and nucleic acids is important in order to decode the complex networks that are likely to underlie many common diseases. Significant improvements in current technology are also required to dissect the regulatory processes in high-throughtput and with low cost. Miniaturization of biological assays is an important prerequisite to achieve these goals in the near future.

Mimicking Cellular Sorting Improves Prediction of Subcellular Localization

Predicting the native subcellular compartment of a protein is an important step toward elucidating its function. Here we introduce LOCtree, a hierarchical system combining support vector machines (SVMs) and other prediction methods. LOCtree predicts the subcellular compartment of a protein by mimicking the mechanism of cellular sorting and exploiting a variety of sequence and predicted structural features in its input. Currently LOCtree does not predict localization for membrane proteins, since the compositional properties of membrane proteins significantly differ from those of non-membrane proteins. While any information about function can be used by the system, we present estimates of performance that are valid when only the amino acid sequence of a protein is known. When evaluated on a non-redundant test set, LOCtree achieved sustained levels of 74% accuracy for non-plant eukaryotes, 70% for plants, and 84% for prokaryotes. We rigorously benchmarked LOCtree in comparison to the best alternative methods for localization prediction. LOCtree outperformed all other methods in nearly all benchmarks. Localization assignments using LOCtree agreed quite well with data from recent large-scale experiments. Our preliminary analysis of a few entirely sequenced organisms, namely human (Homo sapiens), yeast (Saccharomyces cerevisiae), and weed (Arabidopsis thaliana) suggested that over 35% of all non-membrane proteins are nuclear, about 20% are retained in the cytosol, and that every fifth protein in the weed resides in the chloroplast.

A high-throughput screening of genes that encode proteins transported into the endoplasmic reticulum in mammalian cells

The compartments of eukaryotic cells maintain a distinct protein composition to perform a variety of specialized functions. We developed a new method for identifying the proteins that are transported to the endoplasmic reticulum (ER) in living mammalian cells. The principle is based on the reconstitution of two split fragments of enhanced green fluorescent protein (EGFP) by protein splicing with DnaE from Synechocystis PCC6803. Complementary DNA (cDNA) libraries fused to the N-terminal halves of DnaE and EGFP are introduced in mammalian cells with retroviruses. If an expressed protein is transported into the ER, the N-terminal half of EGFP meets its C-terminal half in the ER, and full-length EGFP is reconstituted by protein splicing. The fluorescent cells are isolated using fluorescence-activated cell sorting and the cDNAs are sequenced. The developed method was able to accurately identify cDNAs that encode proteins transported to the ER. We identified 27 novel proteins as the ER-targeting proteins. The present method overcomes the limitation of the previous GFP- or epitope-tagged methods, using which it was difficult to identify the ER-targeting proteins in a high-throughput manner.

Protein localization studies in the age of ‘Omics’

As thousands of new genes are identified in genomics efforts, the rush is on to learn something about the functional roles of the proteins encoded by those genes. Clues to protein functions, activation states and protein-protein interactions have been revealed in focused studies of protein localization. With technical breakthroughs such as GFP protein tagging and recombinase cloning systems, large-scale screens of protein localization are now being undertaken.