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An assessment of the interactions between different receptor-mediated cellular signaling pathways may have revealed an unexpected degree of underlying compartmentalization and even simplicity in the cross-talk between these networks.
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Signaling cascades integrate extracellular stimuli primarily through regulated protein-protein interactions (PPIs). Intracellular signal transduction strictly depends on PPIs occurring at the membrane and in the cytosol. To monitor constitutive and regulated protein interactions within living mammalian cells, we have developed a biological assay termed split TEV. We engineered inactive fragments of the NIa protease from the tobacco etch virus (TEV protease) that regain activity only when coexpressed as fusion constructs with interacting proteins. Functional reconstitution of TEV protease fragments can be monitored with 'proteolysis-only' reporters, which can be previously silent fluorescent and luminescent reporter proteins. Additionally, proteolytically cleavable inactive transcription factors can be combined with any downstream reporter gene of choice to yield 'transcription-coupled' reporter systems. Thus, split TEV combines the advantages of split enzyme- and reporter gene-mediated assays, and provides full flexibility with regard to the final readout. In a first biological application, we monitored neuregulin-induced ErbB2/ErbB4 receptor tyrosine kinase heterodimerization.
Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry has become increasingly significant within recent years. The inherently ultra-high resolution and mass accuracy allow unequivocal assignments of chemical formulae to be made and further structural elucidation can be conducted through the utilization of tandem mass spectrometry techniques. With the advent of electrospray ionization (ESI), FT-ICR mass spectrometry has become a powerful tool for the investigation of biological macromolecules, such as the study of non-covalent interactions of proteins. In this article, the basic principles are highlighted, some of the techniques employed are described and examples of applications are provided, with particular respect being paid to the field of characterization of biomolecules.
Recently a state-space model with time delays for inferring gene regulatory networks was proposed. It was assumed that each regulation between two internal state variables had multiple time delays. This assumption caused underestimation of the model with many current gene expression datasets. In biological reality, one regulatory relationship may have just a single time delay, and not multiple time delays. This study employs Boolean variables to capture the existence of the time-delayed regulatory relationships in gene regulatory networks in terms of the state-space model. As the solution space of time delayed relationships is too large for an exhaustive search, a genetic algorithm (GA) is proposed to determine the optimal Boolean variables (the optimal time-delayed regulatory relationships). Coupled with the proposed GA, Bayesian information criterion (BIC) and probabilistic principle component analysis (PPCA) are employed to infer gene regulatory networks with time delays. Computational experiments are performed on two real gene expression datasets. The results show that the GA is effective at finding time-delayed regulatory relationships. Moreover, the inferred gene regulatory networks with time delays from the datasets improve the prediction accuracy and possess more of the expected properties of a real network, compared to a gene regulatory network without time delays.
We carried out a large-scale screen to identify interactions between integral membrane proteins of Saccharomyces cerevisiae by using a modified split-ubiquitin technique. Among 705 proteins annotated as integral membrane, we identified 1,985 putative interactions involving 536 proteins. To ascribe confidence levels to the interactions, we used a support vector machine algorithm to classify interactions based on the assay results and protein data derived from the literature. Previously identified and computationally supported interactions were used to train the support vector machine, which identified 131 interactions of highest confidence, 209 of the next highest confidence, 468 of the next highest, and the remaining 1,085 of low confidence. This study provides numerous putative interactions among a class of proteins that have been difficult to analyze on a high-throughput basis by other approaches. The results identify potential previously undescribed components of established biological processes and roles for integral membrane proteins of ascribed functions.
About 30 different nucleoporins (Nups) constitute the nuclear pore complex. We have affinity-purified 28 of these nuclear pore proteins and identified new nucleoporin interactions by this analysis. We found that Nup157 and Nup170, two members of the large structural Nups, and the Gly-Leu-Phe-Gly nucleoporin Nup145N specifically co-purified with members of the Nup84 complex. In addition, Nup145N co-enriched during Nup157 purification. By in vitro reconstitution, we demonstrate that Nup157 and Nup145N form a nucleoporin subcomplex. Moreover, we show that Nup157 and Nup145N bind to the heptameric Nup84 complex. This assembly thus represents approximately one-third of all nucleoporins. To characterize Nup157 structurally, we purified and analyzed it by electron microscopy. Nup157 is a hollow sphere that resembles a clamp or a gripping hand. Thus, we could reconstitute an interaction between a large structural Nup, an FG repeat Nup, and a major structural module of the nuclear pore complex.
A thorough analysis of the protein interaction partners of the yeast GTPase Gsp1p was carried out by a multidimensional chromatography strategy of strong cation exchange fractionation of peptides followed by reverse phase LC-ESI-MSMS using a QSTAR instrument. This dataset was then analyzed using the latest developmental version of Protein Prospector. The Prospector search results were also compared with results from the search engine "Mascot" using a new results comparison program within Prospector named "SearchCompare." The results from this study demonstrate that the high quality data produced on a quadrupole selecting, quadrupole collision cell, time-of-flight (QqTOF) geometry instrument allows for confident assignment of the vast majority of interpretable spectra by current search engines.
The proposed model is based on the measurement of the retention times of 346 tryptic peptides in the 560- to 4,000-Da mass range, derived from a mixture of 17 protein digests. These peptides were measured in HPLC-MALDI MS runs, with peptide identities confirmed by MS/MS. The model relies on summation of the retention coefficients of the individual amino acids, as in previous approaches, but additional terms are introduced that depend on the retention coefficients for amino acids at the N-terminal of the peptide. In the 17-protein mixture, optimization of two sets of coefficients, along with additional compensation for peptide length and hydrophobicity, yielded a linear dependence of retention time on hydrophobicity, with an R2 value about 0.94. The predictive capability of the model was used to distinguish peptides with close m/z values and for detailed peptide mapping of selected proteins. Its applicability was tested on columns of different sizes, from nano- to narrow-bore, and for direct sample injection, or injection via a pre-column. It can be used for accurate prediction of retention times for tryptic peptides on reversed-phase (300-A pore size) columns of different sizes with a linear water-ACN gradient and with TFA as the ion-pairing modifier.
High throughput analysis of protein-protein interactions is an important sector of hypothesis-generating research. Using an improved and automated version of the yeast two-hybrid system, we completed a large interaction screening project with a focus on nuclear receptors and their cofactors. A total of 425 independent yeast two-hybrid cDNA library screens resulted in 6425 potential interacting protein fragments involved in 1613 different interaction pairs. We show that simple statistical parameters can be used to narrow down the data set to a high confidence set of 377 interaction pairs where validated interactions are enriched to 61% of all pairs. Within the high confidence set, there are 64 novel proteins potentially binding to nuclear receptors or their cofactors. We discuss several examples of high interest, and we expect that communication of this huge data set will help to complement our knowledge of the protein interaction repertoire of this family of transcription factors and instigate the characterization of the various novel candidate interactors.
Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.
The interpretation of large-scale protein network data depends on our ability to identify significant substructures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths and trees in graphs to the problem of identifying pathways in protein interaction networks. We present linear-time algorithms for finding paths and trees in networks under several biologically motivated constraints. We apply our methodology to search for protein pathways in the yeast protein-protein interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths and trees in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in about three hours.
Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.
With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational cost.
Translating the overwhelming amount of data generated in high-throughput genomics experiments into biologically meaningful evidence, which may for example point to a series of biomarkers or hint at a relevant pathway, is a matter of great interest in bioinformatics these days. Genes showing similar experimental profiles, it is hypothesized, share biological mechanisms that if understood could provide clues to the molecular processes leading to pathological events. It is the topic of further study to learn if or how a priori information about the known genes may serve to explain coexpression. One popular method of knowledge discovery in high-throughput genomics experiments, enrichment analysis (EA), seeks to infer if an interesting collection of genes is 'enriched' for a Consortium particular set of a priori Gene Ontology Consortium (GO) classes. For the purposes of statistical testing, the conventional methods offered in EA software implicitly assume independence between the GO classes. Genes may be annotated for more than one biological classification, and therefore the resulting test statistics of enrichment between GO classes can be highly dependent if the overlapping gene sets are relatively large. There is a need to formally determine if conventional EA results are robust to the independence assumption. We derive the exact null distribution for testing enrichment of GO classes by relaxing the independence assumption using well-known statistical theory. In applications with publicly available data sets, our test results are similar to the conventional approach which assumes independence. We argue that the independence assumption is not detrimental.
MOTIVATION: Microarray experiments comparing expression levels of all genes in yeast for hundreds of mutants allow us to examine properties of gene regulatory networks on a genomic scale. We can investigate questions such as network modularity, connectivity, and look for genes with particular roles in the network structure. RESULTS: We have built genome-wide disruption networks for yeast, using a representation of gene expression data as directed labelled graphs. Nodes represent genes and arcs connect nodes if the disruption of the source gene significantly alters the expression of the target gene. We are interested in features of the resulting disruption networks that are robust over a range of significance cutoffs. The networks show a significant overlap with analogous networks constructed from scientific literature. In disruption networks the number of arcs adjacent to different nodes are distributed roughly according to a power-law, like in many complex systems where the robustness against perturbations is important. The networks are dominated by a single large component and do not have an obvious modular structure. Genes with the highest outdegrees often encode proteins with regulatory functions, whereas genes with the highest indegrees are predominantly involved in metabolism. The local structure of the networks is meaningful, genes involved in the same cellular processes are close together in the network. AVAILABILITY: http://www.ebi.ac.uk/microarray/networks
We propose a new formulation for the problem of ab initio metabolic pathway reconstruction. Given a set of biochemical reactions together with their substrates and products, we consider the reactions as transfers of atoms between the chemical compounds and we look for successions of reactions transferring a maximal (or preset) number of atoms between a given source and sink compound. We state this problem as the one of finding a composition of partial injections that maximizes the image size. First, we study the theoretical complexity of this problem, state some related problems and then give a practical algorithm to solve them. Finally, we present two applications of this approach to the reconstruction of the tryptophan biosynthesis pathway and to the glycolysis.