Shared components of protein complexes--versatile building blocks or biochemical artefacts?

Predicted networks of protein-protein interactions in Stegodyphus mimosarum by cross-species comparisons

BACKGROUND

Stegodyphus mimosarum is a candidate model organism belonging to the class Arachnida in the phylum Arthropoda. Studies on the biology of S. mimosarum over the past several decades have consisted of behavioral research and comparison of gene sequences based on the assembled genome sequence. Given the lack of systematic protein analyses and the rich source of information in the genome, we predicted the relationships of proteins in S. mimosarum by bioinformatics comparison with genome-wide proteins from select model organisms using gene mapping.

RESULTS

The protein-protein interactions (PPIs) of 11 organisms were integrated from four databases (BioGrid, InAct, MINT, and DIP). Here, we present comprehensive prediction and analysis of 3810 proteins in S. mimosarum with regard to interactions between proteins using PPI data of organisms. Interestingly, a portion of the protein interactions conserved among Saccharomyces cerevisiae, Homo sapiens, Arabidopsis thaliana, and Drosophila melanogaster were found to be associated with RNA splicing. In addition, overlap of predicted PPIs in reference organisms, Gene Ontology, and topology models in S. mimosarum are also reported.

CONCLUSIONS

Addition of Stegodyphus, a spider representative of interactomic research, provides the possibility of obtaining deeper insights into the evolution of PPI networks among different animal species. This work largely supports the utility of the "stratus clouds" model for predicted PPIs, providing a roadmap for integrative systems biology in S. mimosarum.

Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource

Background

Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results

The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion

Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of “guilt-by-association” models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0524-1) contains supplementary material, which is available to authorized users.

A proteomic strategy for global analysis of plant protein complexes

Global analyses of protein complex assembly, composition, and location are needed to fully understand how cells coordinate diverse metabolic, mechanical, and developmental activities. The most common methods for proteome-wide analysis of protein complexes rely on affinity purification-mass spectrometry or yeast two-hybrid approaches. These methods are time consuming and are not suitable for many plant species that are refractory to transformation or genome-wide cloning of open reading frames. Here, we describe the proof of concept for a method allowing simultaneous global analysis of endogenous protein complexes that begins with intact leaves and combines chromatographic separation of extracts from subcellular fractions with quantitative label-free protein abundance profiling by liquid chromatography-coupled mass spectrometry. Applying this approach to the crude cytosolic fraction of Arabidopsis thaliana leaves using size exclusion chromatography, we identified hundreds of cytosolic proteins that appeared to exist as components of stable protein complexes. The reliability of the method was validated by protein immunoblot analysis and comparisons with published size exclusion chromatography data and the masses of known complexes. The method can be implemented with appropriate instrumentation, is applicable to any biological system, and has the potential to be further developed to characterize the composition of protein complexes and measure the dynamics of protein complex localization and assembly under different conditions.

Induction of endoplasmic reticulum-derived replication-competent membrane structures by West Nile virus non-structural protein 4B

Replication of flaviviruses (family Flaviviridae) occurs in specialized virus-induced membrane structures (IMS). The cellular composition of these IMS varies for different flaviviruses implying different organelle origins for IMS biogenesis. The role of flavivirus non-structural (NS) proteins for the alteration of IMS remains controversial. In this report, we demonstrate that West Nile virus strain New York 99 (WNV_NY99) remodels the endoplasmic reticulum (ER) membrane to generate specialized IMS. Within these structures, we observed an element of the cis-Golgi, viral double-stranded RNA, and viral-envelope, NS1, NS4A and NS4B proteins using confocal immunofluorescence microscopy. Biochemical analysis and microscopy revealed that NS4B lacking the 2K-signal peptide associates with the ER membrane where it initiates IMS formation in WNV-infected cells. Co-transfection studies indicated that NS4A and NS4B always remain co-localized in the IMS and are associated with the same membrane fractions, suggesting that these proteins function cooperatively in virus replication and may be an ideal target for antiviral drug discovery.

A predicted functional gene network for the plant pathogen Phytophthora infestans as a framework for genomic biology

BACKGROUND

Associations between proteins are essential to understand cell biology. While this complex interplay between proteins has been studied in model organisms, it has not yet been described for the oomycete late blight pathogen Phytophthora infestans.

RESULTS

We present an integrative probabilistic functional gene network that provides associations for 37 percent of the predicted P. infestans proteome. Our method unifies available genomic, transcriptomic and comparative genomic data into a single comprehensive network using a Bayesian approach. Enrichment of proteins residing in the same or related subcellular localization validates the biological coherence of our predictions. The network serves as a framework to query existing genomic data using network-based methods, which thus far was not possible in Phytophthora. We used the network to study the set of interacting proteins that are encoded by genes co-expressed during sporulation. This identified potential novel roles for proteins in spore formation through their links to proteins known to be involved in this process such as the phosphatase Cdc14.

CONCLUSIONS

The functional association network represents a novel genome-wide data source for P. infestans that also acts as a framework to interrogate other system-wide data. In both capacities it will improve our understanding of the complex biology of P. infestans and related oomycete pathogens.

Predicting permanent and transient protein-protein interfaces

Protein-protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs.

Metabolic adaptation and protein complexes in prokaryotes

Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations.

Different evolutionary modifications as a guide to rewire two-component systems

Two-component systems (TCS) are short signalling pathways generally occurring in prokaryotes. They frequently regulate prokaryotic stimulus responses and thus are also of interest for engineering in biotechnology and synthetic biology. The aim of this study is to better understand and describe rewiring of TCS while investigating different evolutionary scenarios. Based on large-scale screens of TCS in different organisms, this study gives detailed data, concrete alignments, and structure analysis on three general modification scenarios, where TCS were rewired for new responses and functions: (i) exchanges in the sequence within single TCS domains, (ii) exchange of whole TCS domains; (iii) addition of new components modulating TCS function. As a result, the replacement of stimulus and promotor cassettes to rewire TCS is well defined exploiting the alignments given here. The diverged TCS examples are non-trivial and the design is challenging. Designed connector proteins may also be useful to modify TCS in selected cases.

Setting Up a Bioluminescence Resonance Energy Transfer High throughput Screening Assay to Search for Protein/Protein Interaction Inhibitors in Mammalian Cells

Each step of the cell life and its response or adaptation to its environment are mediated by a network of protein/protein interactions termed "interactome." Our knowledge of this network keeps growing due to the development of sensitive techniques devoted to study these interactions. The bioluminescence resonance energy transfer (BRET) technique was primarily developed to allow the dynamic monitoring of protein/protein interactions (PPI) in living cells, and has widely been used to study receptor activation by intra- or extra-molecular conformational changes within receptors and activated complexes in mammal cells. Some interactions are described as crucial in human pathological processes, and a new class of drugs targeting them has recently emerged. The BRET method is well suited to identify inhibitors of PPI and here is described why and how to set up and optimize a high throughput screening assay based on BRET to search for such inhibitory compounds. The different parameters to take into account when developing such BRET assays in mammal cells are reviewed to give general guidelines: considerations on the targeted interaction, choice of BRET version, inducibility of the interaction, kinetic of the monitored interaction, and of the BRET reading, influence of substrate concentration, number of cells and medium composition used on the Z' factor, and expected interferences from colored or fluorescent compounds.

Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism--networked buffering--is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems.

Exploring functional roles of multibinding protein interfaces

Cellular processes are highly interconnected and many proteins are shared in different pathways. Some of these shared proteins or protein families may interact with diverse partners using the same interface regions; such multibinding proteins are the subject of our study. The main goal of our study is to attempt to decipher the mechanisms of specific molecular recognition of multiple diverse partners by promiscuous protein regions. To address this, we attempt to analyze the physicochemical properties of multibinding interfaces and highlight the major mechanisms of functional switches realized through multibinding. We find that only 5% of protein families in the structure database have multibinding interfaces, and multibinding interfaces do not show any higher sequence conservation compared with the background interface sites. We highlight several important functional mechanisms utilized by multibinding families. (a) Overlap between different functional pathways can be prevented by the switches involving nearby residues of the same interfacial region. (b) Interfaces can be reused in pathways where the substrate should be passed from one protein to another sequentially. (c) The same protein family can develop different specificities toward different binding partners reusing the same interface; and finally, (d) inhibitors can attach to substrate binding sites as substrate mimicry and thereby prevent substrate binding.

Evolutionary flexibility of protein complexes

Background

Proteins play a key role in cellular life. They do not act alone but are organised in complexes. Throughout the life of a cell, complexes are dynamic in their composition due to attachments and shared components. Experimental and computational evidence indicate that consecutive addition and secondary losses of components played a major role in the evolution of some complexes, mostly without affecting the core function. Here, we analysed in a large scale approach whether this flexibility in evolution is only limited to a distinct number of complexes or represents a more general trend.

Results

Focussing on human protein complexes, we based our analysis on a manually curated dataset from HPRD. In total, 1,060 complexes with 6,136 proteins from 2,187 unique genes were considered. We computed interologs in 25 different species and predicted the composition of complexes. Over the analysed species, the composition of most complexes was highly flexible and only 25% of all genes were never lost. Even if one component was lost at a particular point in time, the fraction of observed second, independent losses of additional components was high (75% of all complexes affected). Still, loss of whole complexes happened rarely. This biological signal deviated significantly from random models. We exemplified this trend on the anaphase promoting complex (APC) where a core is highly conserved throughout all metazoans, but flexibility in certain components is observable.

Conclusion

Consecutive additions and losses of distinct units is a fundamental process in the evolution of protein complexes. These evolutionary events affecting genes coding for units in human protein complexes showed a significantly different phylogenetic pattern compared to randomly selected genes. Determination of taxon specific attachments or losses might be linked to specific cellular or morphological features. Thus, protein complexes contain not only structural and functional, but also evolutionary cores.

Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology

The increasing call for an overall picture of the interactions between the components of a biological system that give rise to the observed function is often summarized by the expression systems biology. Both the interpretative and predictive capabilities of holistic models of biochemical systems, however, depend to a large extent on the level of physico-chemical knowledge of the individual molecular interactions making up the network. This review is focused on the structure-based quantitative characterization of protein-protein interactions, ubiquitous in any biochemical pathway. Recently developed, fast and effective computational methods are reviewed, which allow the assessment of kinetic and thermodynamic features of the association-dissociation processes of protein complexes, both in water soluble and membrane environments. The performance and the accuracy of fast and semi-empirical structure-based methods have reached comparable levels with respect to the classical and more elegant molecular simulations. Nevertheless, the broad accessibility and lower computational cost provide the former methods with the advantageous possibility to perform systems-level analyses including extensive in silico mutagenesis screenings and large-scale structural predictions of multiprotein complexes.

Interaction networks for systems biology

Cellular functions are almost always the result of the coordinated action of several proteins, interacting in protein complexes, pathways or networks. Progress made in devising suitable tools for analysis of protein-protein interactions, have recently made it possible to chart interaction networks on a large-scale. The aim of this review is to provide a short overview of the most promising contributions of interaction networks to human biology, structural biology and human genetics.

Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution

The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various 'genomic-context' methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence.

The social network of a cell: recent advances in interactome mapping

Proteins very rarely act in isolation. Biomolecular interactions are central to all biological functions. In human, for example, interference with biomolecular networks often lead to disease. Protein-protein and protein-metabolite interactions have traditionally been studied one by one. Recently, significant progresses have been made in adapting suitable tools for the global analysis of biomolecular interactions. Here we review this suite of powerful technologies that enable an exponentially growing number of large-scale interaction datasets. These new technologies have already contributed to a more comprehensive cartography of several pathways relevant to human pathologies, offering a broader choice for therapeutic targets. Genome-wide scale analyses in model organisms reveal general organizational principles of eukaryotic proteomes. We also review the biochemical approaches that have been used in the past on a smaller scale for the quantification of the binding constant and the thermodynamics parameters governing biomolecular interaction. The adaptation of these technologies to the large-scale measurement of biomolecular interactions in (semi-)quantitative terms represents an important challenge.

Identifying protein complexes directly from high-throughput TAP data with Markov random fields

Background

Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.

Results

We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes.

Conclusion

We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes.

Pathway redundancy and protein essentiality revealed in the Saccharomyces cerevisiae interaction networks

The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here, we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Applying these tools on a large-scale Saccharomyces cerevisiae data set, our analysis reveals 140 between-pathway models that explain 3765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify ‘pivot' proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential and highly conserved. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins.

Similar Binding Sites and Different Partners: Implications to Shared Proteins in Cellular Pathways

We studied a data set of structurally similar interfaces that bind to proteins with different binding-site structures and different functions. Our multipartner protein interface clusters enable us to address questions like: What makes a given site bind different proteins? How similar/different are the interactions? And, what drives the apparently less-specific association? We find that proteins with common binding-site motifs preferentially use conserved interactions at similar interface locations, despite the different partners. Helices are major vehicles for binding different partners, allowing alternate ways to achieve favorable association. The binding sites are characterized by imperfect packing, planar architectures, bridging water molecules, and, on average, smaller size. Interestingly, analysis of the connectivity of these proteins illustrates that they have more interactions with other proteins. These findings are important in predicting "date hubs," if we assume that "date hubs" are shared proteins with binding sites capable of transient binding to multipartners, linking higher-order networks.

Stratus not altocumulus: a new view of the yeast protein interaction network

Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: “party” hubs are co-expressed and co-localized with their partners, whereas “date” hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.

Analysis of multi-validated protein interaction data reveals networks with greater interconnectivity than the more segregated structures seen in previously available data. To help visualize this, the authors draw comparisons between continuous stratus clouds and altocumulus clouds.

Capturing cellular machines by systematic screens of protein complexes

Two recent studies have provided the most complete screen for protein complexes in yeast to date, in which partners were identified for approximately half of the proteome. A comparison shows that these two datasets are complementary. In addition, one of the analyses points to a modular organization of the cellular protein network. These data will prove useful in defining principles and trends that arise when combining large-scale datasets of different natures, and in deriving properties of protein machines in cellular systems.

Yeast Protein Interactome topology provides framework for coordinated-functionality

The architecture of the network of protein–protein physical interactions in Saccharomyces cerevisiae is exposed through the combination of two complementary theoretical network measures, betweenness centrality and ‘Q-modularity’. The yeast interactome is characterized by well-defined topological modules connected via a small number of inter-module protein interactions. Should such topological inter-module connections turn out to constitute a form of functional coordination between the modules, we speculate that this coordination is occurring typically in a pairwise fashion, rather than by way of high-degree hub proteins responsible for coordinating multiple modules. The unique non-hub-centric hierarchical organization of the interactome is not reproduced by gene duplication-and-divergence stochastic growth models that disregard global selective pressures.

Analysis of and function predictions for previously conserved hypothetical or putative proteins in Blochmannia floridanus

BACKGROUND

There is an increasing interest to better understand endosymbiont capabilities in insects both from an ecological point of view and for pest control. Blochmannia floridanus provides important nutrients for its host, the ant Camponotus, while the bacterium in return is provided with a niche to proliferate. Blochmannia floridanus proteins and metabolites are difficult to study due to its endosymbiontic life style; however, its complete genome sequence became recently available.

RESULTS

Improved sequence analysis algorithms, databanks and gene and pathway context methods allowed us to reveal new information on various enzyme and pathways from the Blochmannia floridanus genome sequence [EMBL-ID BX248583]. Furthermore, these predictions are supported and linked to experimental data for instance from structural genomics projects (e.g. Bfl341, Bfl 499) or available biochemical data on proteins from other species which we show here to be related. We were able to assign a confirmed or at least a putative molecular function for 21 from 27 previously conserved hypothetical proteins. For 48 proteins of 66 with a previous putative assignment the function was further clarified. Several of these proteins occur in many proteobacteria and are found to be conserved even in the compact genome of this endosymbiont. To extend and re-test predictions and links to experimentally verified protein functions, functional clusters and interactions were assembled. These included septum initiation and cell division (Bfl165, Bfl303, Bfl248 et al.); translation; transport; the ubiquinone (Bfl547 et al.), the inositol and nitrogen pathways.

CONCLUSION

Taken together, our data allow a better and more complete description of the pathway capabilities and life style of this typical endosymbiont.

Interactome: gateway into systems biology

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

Structure, function, and evolution of transient and obligate protein-protein interactions

Recent analyses of high-throughput protein interaction data coupled with large-scale investigations of evolutionary properties of interaction networks have left some unanswered questions. To what extent do protein interactions act as constraints during evolution of the protein sequence? How does the type of interaction, specifically transient or obligate, play into these constraints? Are the mutations in the binding site of an interacting protein correlated with mutations in the binding site of its partner? We address these and other questions by relying on a carefully curated dataset of protein complex structures. Results point to the importance of distinguishing between transient and obligate interactions. We conclude that residues in the interfaces of obligate complexes tend to evolve at a relatively slower rate, allowing them to coevolve with their interacting partners. In contrast, the plasticity inherent in transient interactions leads to an increased rate of substitution for the interface residues and leaves little or no evidence of correlated mutations across the interface.

Variation and evolution of biomolecular systems: Searching for functional relevance

The availability of genome sequences and functional genomics data from multiple species enables us to compare the composition of biomolecular systems like biochemical pathways and protein complexes between species. Here, we review small- and large-scale, "genomics-based" approaches to biomolecular systems variation. In general, caution is required when comparing the results of bioinformatics analyses of genomes or of functional genomics data between species. Limitations to the sensitivity of sequence analysis tools and the noisy nature of genomics data tend to lead to systematic overestimates of the amount of variation. Nevertheless, the results from detailed manual analyses, and of large-scale analyses that filter out systematic biases, point to a large amount of variation in the composition of biomolecular systems. Such observations challenge our understanding of the function of the systems and their individual components and can potentially facilitate the identification and functional characterization of sub-systems within a system. Mapping the inter-species variation of complex biomolecular systems on a phylogenetic species tree allows one to reconstruct their evolution.