Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

Protein bipartivity and essentiality in the yeast protein-protein interaction network

Protein-protein interaction networks (PINs) are structured by means of a few highly connected proteins linked to a large number of less-connected ones. Essential proteins have been found to be more abundant among these highly connected proteins. Here we demonstrate that the likelihood that removal of a protein in a PIN will prove lethal to yeast correlates with the lack of bipartivity of the protein. A protein is bipartite if it can be partitioned in such a way that there are two groups of proteins with intergroup, but not intragroup, interactions. The abundance of essential proteins found among the least bipartite proteins clearly exceeds that found among the most connected ones. For instance, among the top 50 proteins ranked by their lack of bipartivity 62% are essential proteins. However, this percentage is only 38% for proteins ranked according to their number of interactions. Protein bipartivity also surpasses another 5 measures of protein centrality in yeast PIN in identifying essential proteins and doubles the number of essential proteins selected at random. We propose a possible mechanism for the evolution of essential proteins in yeast PIN based on the duplication-divergence scheme. We conclude that a replica protein evolving from a nonbipartite target will also be nonbipartite with high probability. Consequently, these new replicas evolving from nonbipartite (essential) targets will with high probability be essential.     

Identification of Essential Proteins Based on Ranking Edge-Weights in Protein-Protein Interaction Networks

Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges.     

Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression

The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.     

A New Method for the Discovery of Essential Proteins

Background

Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value.

Method

In this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality.

Conclusions

We demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.     

Functional Analysis of the Hydrophilic Loop in Intracellular Trafficking of Arabidopsis PIN-FORMED Proteins

Different PIN-FORMED proteins (PINs) contribute to intercellular and intracellular auxin transport, depending on their distinctive subcellular localizations. Arabidopsis thaliana PINs with a long hydrophilic loop (HL) (PIN1 to PIN4 and PIN7; long PINs) localize predominantly to the plasma membrane (PM), whereas short PINs (PIN5 and PIN8) localize predominantly to internal compartments. However, the subcellular localization of the short PINs has been observed mostly for PINs ectopically expressed in different cell types, and the role of the HL in PIN trafficking remains unclear. Here, we tested whether a long PIN-HL can provide its original molecular cues to a short PIN by transplanting the HL. The transplanted long PIN2-HL was sufficient for phosphorylation and PM trafficking of the chimeric PIN5:PIN2-HL but failed to provide the characteristic polarity of PIN2. Unlike previous observations, PIN5 showed clear PM localization in diverse cell types where PIN5 is natively or ectopically expressed and even polar PM localization in one cell type. Furthermore, in the root epidermis, the subcellular localization of PIN5 switched from PM to internal compartments according to the developmental stage. Our results suggest that the long PIN-HL is partially modular for the trafficking behavior of PINs and that the intracellular trafficking of PIN is plastic depending on cell type and developmental stage.     

NetAlign: a web-based tool for comparison of protein interaction networks

NetAlign is a web-based tool designed to enable comparative analysis of protein interaction networks (PINs). NetAlign compares a query PIN with a target PIN by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs), which may derive from a common ancestor and disclose conserved topological organization of interactions in evolution. To exemplify the application of NetAlign, we perform two genome-scale comparisons with (1) the Escherichia coli PIN against the Helicobacter pylori PIN and (2) the Saccharomyces cerevisiae PIN against the Caenorrhabditis elegans PIN. Many of the identified CoNSs correspond to known complexes; therefore, cross-species PIN comparison provides a way for discovery of conserved modules. In addition, based on the species-to-species differences in CoNSs, we reformulate the problems of protein-protein interaction (PPI) prediction and species divergence from a network perspective. AVAILABILITY: http://www1.ustc.edu.cn/lab/pcrystal/NetAlign.     

Differential Auxin-Transporting Activities of PIN-FORMED Proteins in Arabidopsis Root Hair Cells

The Arabidopsis (Arabidopsis thaliana) genome includes eight PIN-FORMED (PIN) members that are molecularly diverged. To comparatively examine their differences in auxin-transporting activity and subcellular behaviors, we expressed seven PIN proteins specifically in Arabidopsis root hairs and analyzed their activities in terms of the degree of PIN-mediated root hair inhibition or enhancement and determined their subcellular localization. Expression of six PINs (PIN1–PIN4, PIN7, and PIN8) in root hair cells greatly inhibited root hair growth, most likely by lowering auxin levels in the root hair cell by their auxin efflux activities. The auxin efflux activity of PIN8, which had not been previously demonstrated, was further confirmed using a tobacco (Nicotiana tabacum) cell assay system. In accordance with these results, those PINs were localized in the plasma membrane, where they likely export auxin to the apoplast and formed internal compartments in response to brefeldin A. These six PINs conferred different degrees of root hair inhibition and sensitivities to auxin or auxin transport inhibitors. Conversely, PIN5 mostly localized to internal compartments, and its expression in root hair cells rather slightly stimulated hair growth, implying that PIN5 enhanced internal auxin availability. These results suggest that different PINs behave differentially in catalyzing auxin transport depending upon their molecular activity and subcellular localization in the root hair cell.Auxin plays a critical role in plant development and growth by forming local concentration gradients. Local auxin gradients, created by the polar cell-to-cell movement of auxin, are implicated in primary axis formation, root meristem patterning, lateral organ formation, and tropic movements of shoots and roots (for recent review, see Vanneste and Friml, 2009). The cell-to-cell movement of auxin is achieved by auxin influx and efflux transporters such as AUXIN-RESISTANT1 (AUX1)/LIKE-AUX1 for influx and PIN-FORMED (PIN) and the P-glycoprotein (PGP) of ABCB (ATP-binding cassette-type transporter subfamily B) for efflux. Since diffusive efflux of the natural auxin indole-3-acetic acid (IAA; pKa = 4.75) is not favorable and PINs are localized in the plasma membrane in a polar manner, PINs act as rate-limiting factors for cellular auxin efflux and polar auxin transport through the plant body. These PINs'' properties explain why representative physiological effects of auxin transport are associated with PINs.Auxin flows from young aerial parts all the way down to the root tip columella in which an auxin maximum is formed for root stem cell maintenance and moves up toward the root differentiation zone through root epidermal cells, where a part of it travels back to the root tip via cortical cells (Blilou et al., 2005). This directional auxin flow is supported by the polar localization of PINs: PIN1, PIN3, and PIN7 at the basal side of stele cells (Friml et al., 2002a, 2002b; Blilou et al., 2005), PIN4 at the basal side in root stem cells (Friml et al., 2002a), and PIN2 at the upper side of root epidermis and at the basal side of the root cortex (Luschnig et al., 1998; Müller et al., 1998). Another interesting aspect of PIN-mediated auxin transport is the dynamics in directionality of auxin flow due to environmental stimuli-directed changes of subcellular PIN polarity, as exemplified for PIN3, whose subcellular localization changes in response to the gravity vector (Friml et al., 2002b).An intriguing question is how different PIN proteins have different subcellular polarities, which might be attributable to PIN-specific molecular properties, cell-type-specific factors, or both. The different PIN subcellular polarities in different cell types seemingly indicate that cell-type-specific factors are involved in polarity. In the case of PIN1, however, both classes of factors appear to affect its subcellular localization because when expressed under the PIN2 promoter, PIN1 localizes to the upper or basal side of root epidermal cells, depending on the GFP insertion site of the protein (Wiśniewska et al., 2006). A recent study demonstrated that the polar targeting of PIN proteins is modulated by phosphorylation/dephosphorylation of the central hydrophilic loop of PINs, which is mediated by PINOID (PID; a Ser/Thr protein kinase)/PP2A phosphatase (Michniewicz et al., 2007). The central hydrophilic domain of PINs might provide the molecule-specific cue for PIN polarity, together with as yet unknown cell-specific factors. Different recycling behaviors of PINs, which show variable sensitivities to brefeldin A (BFA), also imply different molecular characters among PIN species. Most PIN1 proteins are internalized by BFA treatment, whereas considerable amounts of PIN2 remain in the plasma membrane in addition to internal accumulation after BFA treatment. Recycling and basal polar targeting of PIN1 is dependent on the BFA-sensitive guanine nucleotide exchange factor for adenosyl ribosylation factors (ARF GEFs), GNOM, which is the major target of BFA. In contrast, apical targeting and recycling of PIN2 is independent of GNOM and controlled by BFA-resistant ARF GEFs (Geldner et al., 2003; Kleine-Vehn and Friml, 2008).In contrast to their distinct subcellular localizations, the differential auxin-transporting activities of PINs remain to be studied. The divergent primary structures of PIN proteins are not only indicative of differential subcellular polarity, but also would represent their differential catalytic activities for auxin transport. The auxin efflux activities of Arabidopsis (Arabidopsis thaliana) PINs have been demonstrated using Arabidopsis and heterologous systems: PIN1 and PIN5 in Arabidopsis cells (Petrásek et al., 2006; Mravec et al., 2009); PIN2, PIN3, PIN4, PIN6, and PIN7 in tobacco (Nicotiana tabacum) Bright Yellow-2 (BY-2) cells (Lee and Cho, 2006; Petrásek et al., 2006; Mravec et al., 2008); PIN1, PIN2, PIN5, and PIN7 in yeast (Saccharomyces cerevisiae) cells (Petrásek et al., 2006; Blakeslee et al., 2007; Mravec et al., 2009; Yang and Murphy, 2009); and PIN1, PIN2, and PIN7 in HeLa cells (Petrásek et al., 2006; Blakeslee et al., 2007). Among the eight Arabidopsis PIN members, PIN1, PIN2, PIN3, PIN4, PIN6, and PIN7, which share a similar molecular structure in terms of the presence of a long central loop (hereafter called long-looped PINs; Fig. 1A; Supplemental Fig. S1), have been shown to catalyze auxin efflux at the cellular level. On the other hand, PIN5 and PIN8 possess a very short putative central loop (hereafter called short-looped PINs). Although PIN5 was recently shown to be localized in the endoplasmic reticulum (ER) and proposed to transport auxin metabolites into the ER lumen, its cellular function regarding its intracellular auxin-transporting activity has not been shown, and the auxin-transporting activity of PIN8 has yet to be demonstrated. In spite of the same transport directionality (auxin efflux) and similar molecular structures, the long-looped PINs exhibit sequence divergence not only in their central loop, but also in certain residues of the transmembrane domains. This structural divergence of long-looped PINs might be indicative of their differential auxin-transporting activities, which have not yet been quantitatively compared.Open in a separate windowFigure 1.Differential activities of PINs in the Arabidopsis root hair. A, Two distinctive PIN groups with different central hydrophilic loop sizes. Topology of PIN proteins was predicted by four different programs as described in Supplemental Figure S1. Numbers above indicate the number of transmembrane helices for each N- and C-terminal region, and numbers below indicate the number of amino acid residues of the central hydrophilic domain. B, Representative root images of control (Cont; Columbia-0) and root-hair-specific PIN-overexpressing (PINox; ProE7:PIN-GFP or ProE7:PIN [−]) plants. Bar = 100 μm for all. C, Root hair lengths of control and PINox plants. Six to 12 independent transgenic lines (average = 8.3), and 42 to 243 roots (average = 86.8) and 336 to 2,187 root hairs (average = 727.8) per construct, were observed for the estimation of root hair length. Data represent means ± se. The root hair lengths of PIN5ox lines were significantly longer than those of the control (P = 0.016 for PIN5ox; P < 0.0001 for PIN5-GFP1ox and PIN5-GFP2ox).To comparatively assess the cytological behaviors and molecular activities of different PIN members, it would be favorable to use a single assay system that provides a consistent cellular environment and enables quantitative estimation of PIN activity. In previous studies, we adopted the root hair single cell system to quantitatively assay auxin-transporting or regulatory activities of PINs, PGPs, AUX1, and PID (Lee and Cho, 2006; Cho et al., 2007a). Root hair growth is proportional to internal auxin levels in the root hair cell. Therefore, auxin efflux inhibits and auxin influx enhances root hair growth (Cho et al., 2007b; Lee and Cho, 2008). In addition, the use of a root-hair-specific promoter (Cho and Cosgrove, 2002; Kim et al., 2006) for expression of auxin transporters enables the transporters'' biological effect to be pinpointed to only the root hair cell, thus excluding probable non-cell-autonomous effects that could be caused by the general expression of auxin transporters.In this study, we expressed five long-looped PINs (PIN1, PIN2, PIN3, PIN4, and PIN7) and two short-looped PINs (PIN5 and PIN8) in root hair cells and compared their auxin-transporting activities and cytological dynamics. To directly measure the radiolabeled auxin-transporting activities of PIN5 and PIN8, we used an additional assay system, tobacco suspension cells. Our data revealed that PINs have differential molecular activities and pharmacological responses and that the short-looped and long-looped PINs have different subcellular localizations.     

New approach to evaluating the effects of a drug on protein complexes with quantitative proteomics,using the SILAC method and bioinformatic approach

ABSTRACT

Protein–protein interactions (PPIs) lead the formation of protein complexes that perform biochemical reactions that maintain the living state of the living cell. Although therapeutic drugs should influence the formation of protein complexes in addition to PPI network, the methodology analyzing such influences remain to be developed. Here, we demonstrate that a new approach combining HPLC (high performance liquid chromatography) for separating protein complexes, and the SILAC (stable isotope labeling using amino acids in cell culture) method for relative protein quantification, enable us to identify the protein complexes influenced by a drug. We applied this approach to the analysis of thalidomide action on HepG2 cells, assessed the identified proteins by clustering data analyses, and assigned 135 novel protein complexes affected by the drug. We propose that this approach is applicable to elucidating the mechanisms of actions of other therapeutic drugs on the PPI network, and the formation of protein complexes.     

Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks

Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN). However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs). To do this, a method based on Localization Specificity for Essential protein Detection (LSED), was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs) for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster). Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.     

Combining functional and topological properties to identify core modules in protein interaction networks

Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.     

Phospholipase A2 Is Required for PIN-FORMED Protein Trafficking to the Plasma Membrane in the Arabidopsis Root

Phospholipase A₂ (PLA₂), which hydrolyzes a fatty acyl chain of membrane phospholipids, has been implicated in several biological processes in plants. However, its role in intracellular trafficking in plants has yet to be studied. Here, using pharmacological and genetic approaches, the root hair bioassay system, and PIN-FORMED (PIN) auxin efflux transporters as molecular markers, we demonstrate that plant PLA₂s are required for PIN protein trafficking to the plasma membrane (PM) in the Arabidopsis thaliana root. PLA₂α, a PLA₂ isoform, colocalized with the Golgi marker. Impairments of PLA₂ function by PLA₂α mutation, PLA₂-RNA interference (RNAi), or PLA₂ inhibitor treatments significantly disrupted the PM localization of PINs, causing internal PIN compartments to form. Conversely, supplementation with lysophosphatidylethanolamine (the PLA₂ hydrolytic product) restored the PM localization of PINs in the pla₂α mutant and the ONO-RS-082–treated seedling. Suppression of PLA₂ activity by the inhibitor promoted accumulation of trans-Golgi network vesicles. Root hair–specific PIN overexpression (PINox) lines grew very short root hairs, most likely due to reduced auxin levels in root hair cells, but PLA₂ inhibitor treatments, PLA₂α mutation, or PLA₂-RNAi restored the root hair growth of PINox lines by disrupting the PM localization of PINs, thus reducing auxin efflux. These results suggest that PLA₂, likely acting in Golgi-related compartments, modulates the trafficking of PIN proteins.     

Protein-protein interactions in plants

The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.     

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Eschericia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.     

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation,Essentiality and Function

BackgroundProtein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.MethodsOn a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).ResultsWe show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).ConclusionThe genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.     

Improved flower pollination algorithm for identifying essential proteins

Background

Essential proteins are necessary for the survival and development of cells. The identification of essential proteins can help to understand the minimal requirements for cellular life and it also plays an important role in the disease genes study and drug design. With the development of high-throughput techniques, a large amount of protein-protein interactions data is available to predict essential proteins at the network level. Hitherto, even though a number of essential protein discovery methods have been proposed, the prediction precision still needs to be improved.

Methods

In this paper, we propose a new algorithm, improved Flower Pollination algorithm (FPA) for identifying Essential proteins, named FPE. Different from other existing essential protein discovery methods, we apply FPA which is a new intelligent algorithm imitating pollination behavior of flowering plants in nature to identify essential proteins. Analogous to flower pollination is to find optimal reproduction from the perspective of biological evolution, and the identification of essential proteins is to discover a candidate essential protein set by analyzing the corresponding relationships between FPA algorithm and the prediction of essential proteins, and redefining the positions of flowers and specific pollination process. Moreover, it has been proved that the integration of biological and topological properties can get improved precision for identifying essential proteins. Consequently, we develop a GSC measurement in order to judge the essentiality of proteins, which takes into account not only the Gene expression data, Subcellular localization and protein Complexes information, but also the network topology.

Results

The experimental results show that FPE performs better than the state-of-the-art methods (DC, SC, IC, EC, LAC, NC, PeC, WDC, UDoNC and SON) in terms of the prediction precision, precision-recall curve and jackknife curve for identifying essential proteins and also has high stability.

Conclusions

We confirm that FPE can be used to effectively identify essential proteins by the use of nature-inspired algorithm FPA and the combination of network topology with gene expression data, subcellular localization and protein complexes information. The experimental results have shown the superiority of FPE for the prediction of essential proteins.