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.     

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.     

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.     

The organisational structure of protein networks: revisiting the centrality–lethality hypothesis

Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the STRING provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality–lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the STRING database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality–lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality–lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes.     

Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality

The centrality-lethality rule, which notes that high-degree nodes in a protein interaction network tend to correspond to proteins that are essential, suggests that the topological prominence of a protein in a protein interaction network may be a good predictor of its biological importance. Even though the correlation between degree and essentiality was confirmed by many independent studies, the reason for this correlation remains illusive. Several hypotheses about putative connections between essentiality of hubs and the topology of protein-protein interaction networks have been proposed, but as we demonstrate, these explanations are not supported by the properties of protein interaction networks. To identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality, we performed a rigorous analysis of six variants of the genomewide protein interaction network for Saccharomyces cerevisiae obtained using different techniques. We demonstrated that the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. Moreover, we rejected two previously proposed explanations for the centrality-lethality rule, one relating the essentiality of hubs to their role in the overall network connectivity and another relying on the recently published essential protein interactions model.     

Drug response prediction using graph representation learning and Laplacian feature selection

Background

Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein–protein interaction (PPI) data, computationally identifying essential proteins from protein–protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed.

Results

In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism.

Conclusions

We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.

     

Characteristics of Plant Essential Genes Allow for within- and between-Species Prediction of Lethal Mutant Phenotypes

Essential genes represent critical cellular components whose disruption results in lethality. Characteristics shared among essential genes have been uncovered in fungal and metazoan model systems. However, features associated with plant essential genes are largely unknown and the full set of essential genes remains to be discovered in any plant species. Here, we show that essential genes in Arabidopsis thaliana have distinct features useful for constructing within- and cross-species prediction models. Essential genes in A. thaliana are often single copy or derived from older duplications, highly and broadly expressed, slow evolving, and highly connected within molecular networks compared with genes with nonlethal mutant phenotypes. These gene features allowed the application of machine learning methods that predicted known lethal genes as well as an additional 1970 likely essential genes without documented phenotypes. Prediction models from A. thaliana could also be applied to predict Oryza sativa and Saccharomyces cerevisiae essential genes. Importantly, successful predictions drew upon many features, while any single feature was not sufficient. Our findings show that essential genes can be distinguished from genes with nonlethal phenotypes using features that are similar across kingdoms and indicate the possibility for translational application of our approach to species without extensive functional genomic and phenomic resources.     

Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes

Background

Computational approaches aided by computer science have been used to predict essential proteins and are faster than expensive, time-consuming, laborious experimental approaches. However, the performance of such approaches is still poor, making practical applications of computational approaches difficult in some fields. Hence, the development of more suitable and efficient computing methods is necessary for identification of essential proteins.

Method

In this paper, we propose a new method for predicting essential proteins in a protein interaction network, local interaction density combined with protein complexes (LIDC), based on statistical analyses of essential proteins and protein complexes. First, we introduce a new local topological centrality, local interaction density (LID), of the yeast PPI network; second, we discuss a new integration strategy for multiple bioinformatics. The LIDC method was then developed through a combination of LID and protein complex information based on our new integration strategy. The purpose of LIDC is discovery of important features of essential proteins with their neighbors in real protein complexes, thereby improving the efficiency of identification.

Results

Experimental results based on three different PPI(protein-protein interaction) networks of Saccharomyces cerevisiae and Escherichia coli showed that LIDC outperformed classical topological centrality measures and some recent combinational methods. Moreover, when predicting MIPS datasets, the better improvement of performance obtained by LIDC is over all nine reference methods (i.e., DC, BC, NC, LID, PeC, CoEWC, WDC, ION, and UC).

Conclusions

LIDC is more effective for the prediction of essential proteins than other recently developed methods.     

Centrality measures and the importance of generalist species in pollination networks

Studies of complex networks show that nodes with high centrality scores are important to network structure and stability. Following this rationale, centrality measures can be used to (i) identify keystone species in ecological networks, a major issue in community ecology, and (ii) differentiate the keystone species concept, e.g. species may play a key role in a network for different topological reasons. In 34 pollination communities we examine the relationship between the generalization level of species (ND) and two complementary centrality indices: closeness (CC) and betweenness centrality (BC). CC measures the proximity of a species to all other species in the community, while BC describes the importance of a species as a connector. Most networks had a linear ND–CC relationship with a minimum CC value of 0.41. Hence, species were close to each and will be likely to be rapidly affected by disturbances. Contrarily, in most networks, the ND–BC relationships were power-law distributed with exponents larger than one. Only 59% of the species were connectors (BC > 0). In particular, there was a connector threshold value of ND = 0.46. Species above this threshold represent ～40%, almost all of which were connectors. These results indicate that in pollination systems the most generalized species are usually network keystone species, playing at least two roles: (i) interact closely with most other species (high CC) and (ii) connect otherwise unconnected subnetworks (high BC). We discuss the implications of centrality measures to community-based conservation ecology.     

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Background  

In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).     

Phosphorylation of Conserved PIN Motifs Directs Arabidopsis PIN1 Polarity and Auxin Transport

Polar cell-to-cell transport of auxin by plasma membrane–localized PIN-FORMED (PIN) auxin efflux carriers generates auxin gradients that provide positional information for various plant developmental processes. The apical-basal polar localization of the PIN proteins that determines the direction of auxin flow is controlled by reversible phosphorylation of the PIN hydrophilic loop (PINHL). Here, we identified three evolutionarily conserved TPRXS(N/S) motifs within the PIN1HL and proved that the central Ser residues were phosphorylated by the PINOID (PID) kinase. Loss-of-phosphorylation PIN1:green fluorescent protein (GFP) (Ser to Ala) induced inflorescence defects, correlating with their basal localization in the shoot apex, and induced internalization of PIN1:GFP during embryogenesis, leading to strong embryo defects. Conversely, phosphomimic PIN1:GFP (Ser to Glu) showed apical localization in the shoot apex but did not rescue pin1 inflorescence defects. Both loss-of-phosphorylation and phosphomimic PIN1:GFP proteins were insensitive to PID overexpression. The basal localization of loss-of-phosphorylation PIN1:GFP increased auxin accumulation in the root tips, partially rescuing PID overexpression-induced root collapse. Collectively, our data indicate that reversible phosphorylation of the conserved Ser residues in the PIN1HL by PID (and possibly by other AGC kinases) is required and sufficient for proper PIN1 localization and is thus essential for generating the differential auxin distribution that directs plant development.     

Evolutionarily Conserved Network Properties of Intrinsically Disordered Proteins

BackgroundIntrinsically disordered proteins (IDPs) lack a stable tertiary structure in isolation. Remarkably, however, a substantial portion of IDPs undergo disorder-to-order transitions upon binding to their cognate partners. Structural flexibility and binding plasticity enable IDPs to interact with a broad range of partners. However, the broader network properties that could provide additional insights into the functional role of IDPs are not known.ResultsHere, we report the first comprehensive survey of network properties of IDP-induced sub-networks in multiple species from yeast to human. Our results show that IDPs exhibit greater-than-expected modularity and are connected to the rest of the protein interaction network (PIN) via proteins that exhibit the highest betweenness centrality and connect to fewer-than-expected IDP communities, suggesting that they form critical communication links from IDP modules to the rest of the PIN. Moreover, we found that IDPs are enriched at the top level of regulatory hierarchy.ConclusionOverall, our analyses reveal coherent and remarkably conserved IDP-centric network properties, namely, modularity in IDP-induced network and a layer of critical nodes connecting IDPs with the rest of the PIN.     

Analysis of Vascular Development in the hydra Sterol Biosynthetic Mutants of Arabidopsis

Background

The control of vascular tissue development in plants is influenced by diverse hormonal signals, but their interactions during this process are not well understood. Wild-type sterol profiles are essential for growth, tissue patterning and signalling processes in plant development, and are required for regulated vascular patterning.

Methodology/Principal Findings

Here we investigate the roles of sterols in vascular tissue development, through an analysis of the Arabidopsis mutants hydra1 and fackel/hydra2, which are defective in the enzymes sterol isomerase and sterol C-14 reductase respectively. We show that defective vascular patterning in the shoot is associated with ectopic cell divisions. Expression of the auxin-regulated AtHB8 homeobox gene is disrupted in mutant embryos and seedlings, associated with variably incomplete vascular strand formation and duplication of the longitudinal axis. Misexpression of the auxin reporter proIAA2∶GUS and mislocalization of PIN proteins occurs in the mutants. Introduction of the ethylene-insensitive ein2 mutation partially rescues defective cell division, localization of PIN proteins, and vascular strand development.

Conclusions

The results support a model in which sterols are required for correct auxin and ethylene crosstalk to regulate PIN localization, auxin distribution and AtHB8 expression, necessary for correct vascular development.     

Pathway-oriented action of dietary essential oils to prevent muscle protein oxidation and texture deterioration of farmed rainbow trout

Recently, great attention has been directed towards the use of essential oils from aromatic plants as antimicrobials and antioxidant in food matrix. Fish is well known to be a high perishable food. Indeed, fish muscle is susceptible to suffer protein and lipid oxidation during frozen storage, which can lead to the development of softening and undesirable volatile molecules. However, the possible inclusion of essential oils in fish feed for preserving fish flesh quality during storage is still unclear. For this reason, the potential protective effects of the incorporation of a dietary essential oil constituted by eucalyptol, carvacrol and thymol, to rainbow trout’s (Oncorhynchus mykiss) feed were here investigated. Frozen fish fillets resulting from trout fed the essential oil showed a significant protection of specific muscle proteins against the oxidation produced during frozen storage at –10ºC for 6 months. Essential oil-enriched feed decreased carbonylation of specific myofibrillar (α-actinins-1 and -3, myosin heavy chain, myomesin-1, pyruvate kinase, tropomyosin, troponin-T and actin) and sarcoplasmic proteins (glycogen phosphorylase, creatine kinase, fructose-bisphosphate aldolase A and phosphoglycerate mutase 2). Essential oils also increased actin stability and preserved muscle protein solubility and water holding capacity. In addition, essential oils inhibited the onset of lipid oxidation and rancidity, resulting in frozen fish with superior textural quality and sensory scores. As a final conclusion, the inclusion of essential oils in farmed rainbow trout feed is largely efficient for increasing fish quality and shelf life during frozen storage, mainly through a selective-antioxidant effect on muscle proteins.     

Predicting essential proteins based on subcellular localization,orthology and PPI networks

Background

Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods.

Results

Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION.

Conclusions

We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.

     

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.     

Differential roles of auxin efflux carrier PIN proteins in hypocotyl phototropism of etiolated Arabidopsis seedlings depend on the direction of light stimulus

In a recent study, we demonstrated that although the auxin efflux carrier PIN-FORMED (PIN) proteins, such as PIN3 and PIN7, are required for the pulse-induced first positive phototropism in etiolated Arabidopsis hypocotyls, they are not necessary for the continuous-light-induced second positive phototropism when the seedlings are grown on the surface of agar medium, which causes the hypocotyls to separate from the agar surface. Previous reports have shown that hypocotyl phototropism is slightly impaired in pin3 single mutants when they are grown along the surface of agar medium, where the hypocotyls always contact the agar, producing some friction. To clarify the possible involvement of PIN3 and PIN7 in continuous-light-induced phototropism, we investigated hypocotyl phototropism in the pin3 pin7 double mutant grown along the surface of agar medium. Intriguingly, the phototropic curvature was slightly impaired in the double mutant when the phototropic stimulus was presented on the adaxial side of the hook, but was not impaired when the phototropic stimulus was presented on the abaxial side of the hook. These results indicate that PIN proteins are required for continuous-light-induced second positive phototropism, depending on the direction of the light stimulus, when the seedlings are in contact with agar medium.