Localized network centrality and essentiality in the yeast–protein interaction network

It has been suggested that a close relationship exists between gene essentiality and network centrality in protein–protein interaction networks. However, recent studies have reported somewhat conflicting results on this relationship. In this study, we investigated whether essential proteins could be inferred from network centrality alone. In addition, we determined which centrality measures describe the essentiality well. For this analysis, we devised new local centrality measures based on several well‐known centrality measures to more precisely describe the connection between network topology and essentiality. We examined two recent yeast protein–protein interaction networks using 40 different centrality measures. We discovered a close relationship between the path‐based localized information centrality and gene essentiality, which suggested underlying topological features that represent essentiality. We propose that two important features of the localized information centrality (proper representation of environmental complexity and the consideration of local sub‐networks) are the key factors that reveal essentiality. In addition, a random forest classifier showed reasonable performance at classifying essential proteins. Finally, the results of clustering analysis using centrality measures indicate that some network clusters are closely related with both particular biological processes and essentiality, suggesting that functionally related proteins tend to share similar network properties.     

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.     

Understanding gene essentiality by finely characterizing hubs in the yeast protein interaction network

The centrality-lethality rule, i.e., high-degree proteins or hubs tend to be more essential than low-degree proteins in the yeast protein interaction network, reveals that a protein’s central position indicates its important function, but whether and why hubs tend to be more essential have been heavily debated. Here, we integrated gene expression and functional module data to classify hubs into four types: non-co-expressed non-co-cluster hubs, non-co-expressed co-cluster hubs, co-expressed non-co-cluster hubs and co-expressed co-cluster hubs. We found that all the four hub types are more essential than non-hubs, but they also show different enrichments in essential proteins. Non-co-expressed non-co-cluster hubs play key role in organizing different modules formed by the other three hub types, but they are less important to the survival of the yeast cell. Among the four hub types, co-expressed co-cluster hubs, which likely correspond to the core components of stable protein complexes, are the most essential. These results demonstrated that our classification of hubs into four types could better improve the understanding of gene essentiality.     

Identification and characterization of protein subcomplexes in yeast

Protein complexes are major components of cellular organization. Based on large-scale protein complex data, we present the first statistical procedure to find insightful substructures in protein complexes: we identify protein subcomplexes (SCs), i.e., multiprotein assemblies residing in different protein complexes. Four protein complex datasets with different origins and variable reliability are separately analyzed. Our method identifies well-characterized protein assemblies with known functions, thereby confirming the utility of the procedure. In addition, we also identify hitherto unknown functional entities consisting of either functionally unknown proteins or proteins with different functional annotation. We show that SCs represent more reliable protein assemblies than the original complexes. Finally, we demonstrate unique properties of subcomplex proteins that underline the distinct roles of SCs: (i) SCs are functionally and spatially more homogeneous than complete protein complexes (this fact is utilized to predict functional roles and subcellular localizations for so far unannotated proteins); (ii) the abundance of subcomplex proteins is less variable than the abundance of other proteins; (iii) SCs are enriched with essential and synthetic lethal proteins; and (iv) mutations in SC-proteins have higher fitness effects than mutations in other proteins.     

Functional evolution of the yeast protein interaction network

Protein interactions are central to most biological processes. We investigated the dynamics of emergence of the protein interaction network of Saccharomyces cerevisiae by mapping origins of proteins on an evolutionary tree. We demonstrate that evolutionary periods are characterized by distinct connectivity levels of the emerging proteins. We found that the most-connected group of proteins dates to the eukaryotic radiation, and the more ancient group of pre-eukaryotic proteins is less connected. We show that functional classes have different average connectivity levels and that the time of emergence of these functional classes parallels the observed connectivity variation in evolution. We take these findings as evidence that the evolution of function might be the reason for the differences in connectivity throughout evolutionary time. We propose that the understanding of the mechanisms that generate the scale-free protein interaction network, and possibly other biological networks, requires consideration of protein function.     

A network of ubiquitin ligases is important for the dynamics of misfolded protein aggregates in yeast

Quality control ubiquitin ligases promote degradation of misfolded proteins by the proteasome. If the capacity of the ubiquitin/proteasome system is exceeded, then misfolded proteins accumulate in aggregates that are cleared by the autophagic system. To identify components of the ubiquitin/proteasome system that protect against aggregation, we analyzed a GFP-tagged protein kinase, Ste11ΔN(K444R)-GFP, in yeast strains deleted for 14 different ubiquitin ligases. We show that deletion of almost all of these ligases affected the proteostatic balance in untreated cells such that Ste11ΔN(K444R)-GFP aggregation was changed significantly compared with the levels found in wild type cells. By contrast, aggregation was increased significantly in only six E3 deletion strains when Ste11ΔN(K444R)-GFP folding was impaired due to inhibition of the molecular chaperone Hsp90 with geldanamycin. The increase in aggregation of Ste11ΔN(K444R)-GFP due to deletion of UBR1 and UFD4 was partially suppressed by deletion of UBR2 due to up-regulation of Rpn4, which controls proteasome activity. Deletion of UBR1 in combination with LTN1, UFD4, or DOA10 led to a marked hypersensitivity to azetidine 2-carboxylic acid, suggesting some redundancy in the networks of quality control ubiquitin ligases. Finally, we show that Ubr1 promotes clearance of protein aggregates when the autophagic system is inactivated. These results provide insight into the mechanics by which ubiquitin ligases cooperate and provide feedback regulation in the clearance of misfolded proteins.     

Similarities in protein amino acid composition in connection with principles of protein evolution

The amino acid composition of human alcohol dehydrogenase (ADH) was compared with alcohol dehydrogenases from different organisms and with other proteins. Similar amino acid sequences in human ADH (template protein) and in other proteins were determined by means of an original computer program. Analysis of amino acid motifs reveals that the ADHs from evolutionary more close organisms have more common amino acid sequences. The quantity measure of amino acid similarity was the number of similar motifs in analyzed protein per protein length. This value was measured for ADHs and for different proteins. For ADHs, this quotient was higher than for proteins with different functions; for vertebrates it correlated with evolutionary closeness. The similar operation of motif comparison was made with the help of program complex “MEME”. The analysis of ADHs revealed 4 motifs common to 6 of 10 tested organisms and no such motifs for proteins of different function. The conclusion is that general amino composition is more important for protein function than amino acid order and for enzymes of similar function it better correlates with evolutionary distance between organisms.     

Crystal structure of the protein kinase domain of yeast AMP-activated protein kinase Snf1

AMP-activated protein kinase (AMPK) is a master metabolic regulator, and is an important target for drug development against diabetes, obesity, and other diseases. AMPK is a hetero-trimeric enzyme, with a catalytic (alpha) subunit, and two regulatory (beta and gamma) subunits. Here we report the crystal structure at 2.2A resolution of the protein kinase domain (KD) of the catalytic subunit of yeast AMPK (commonly known as SNF1). The Snf1-KD structure shares strong similarity to other protein kinases, with a small N-terminal lobe and a large C-terminal lobe. Two negative surface patches in the structure may be important for the recognition of the substrates of this kinase.     

Backbone NMR resonance assignment of the catalytic subunit of cAMP-dependent protein kinase A in complex with AMP-PNP

The catalytic subunit of protein kinase A is involved with a number of signal transduction pathways and has been used as a benchmark to study the structural biology and biochemistry for the entire kinase family of enzymes. Here, we report the backbone assignment of the intact 41 kDa catalytic subunit bound to AMP-PNP. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Thoroughly sampling sequence space: large-scale protein design of structural ensembles

Modeling the inherent flexibility of the protein backbone as part of computational protein design is necessary to capture the behavior of real proteins and is a prerequisite for the accurate exploration of protein sequence space. We present the results of a broad exploration of sequence space, with backbone flexibility, through a novel approach: large-scale protein design to structural ensembles. A distributed computing architecture has allowed us to generate hundreds of thousands of diverse sequences for a set of 253 naturally occurring proteins, allowing exciting insights into the nature of protein sequence space. Designing to a structural ensemble produces a much greater diversity of sequences than previous studies have reported, and homology searches using profiles derived from the designed sequences against the Protein Data Bank show that the relevance and quality of the sequences is not diminished. The designed sequences have greater overall diversity than corresponding natural sequence alignments, and no direct correlations are seen between the diversity of natural sequence alignments and the diversity of the corresponding designed sequences. For structures in the same fold, the sequence entropies of the designed sequences cluster together tightly. This tight clustering of sequence entropies within a fold and the separation of sequence entropy distributions for different folds suggest that the diversity of designed sequences is primarily determined by a structure's overall fold, and that the designability principle postulated from studies of simple models holds in real proteins. This has important implications for experimental protein design and engineering, as well as providing insight into protein evolution.     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.     

Predicting protein folding cores by empirical potential functions

Theoretical and in vitro experiments suggest that protein folding cores form early in the process of folding, and that proteins may have evolved to optimize both folding speed and native-state stability. In our previous work (Chen et al., Structure, 14 (2006) 1401), we developed a set of empirical potential functions and used them to analyze interaction energies among secondary-structure elements in two β-sandwich proteins. Our work on this group of proteins demonstrated that the predicted folding core also harbors residues that form native-like interactions early in the folding reaction. In the current work, we have tested our empirical potential functions on structurally-different proteins for which the folding cores have been revealed by protein hydrogen-deuterium exchange experiments. Using a set of 29 unrelated proteins, which have been extensively studied in the literature, we demonstrate that the average prediction result from our method is significantly better than predictions based on other computational methods. Our study is an important step towards the ultimate goal of understanding the correlation between folding cores and native structures.     

基于序列特征网络的蛋白质结构类型研究

利用复杂网络的方法来探索序列特征因素对蛋白质结构的影响。由于蛋白质的序列对结构具有重要且复杂的影响,因此将蛋白质的结构以及序列特征之间的关系模拟成一个复杂系统,通过利用互相关系数、标准化互信息和传递熵等方法来建立以序列特征为节点的加权网络,进而利用网络中心性的方法来分析不同蛋白质结构类型对应加权网络的中心性分布的差异,探索不同结构类型蛋白质的序列特征差异。发现不同的蛋白质结构类型对应的序列特征网络既有共性又有差异,文章将针对每一种结构类型的网络中心性分布,以及不同结构类型之间的共性与差异进行详细地讨论。研究结果对蛋白质序列与结构之间关系的研究,特别是结构分类研究具有重要的意义。     

Transport protein synthesis in non-growing yeast cells

Addition of a metabolizable substrate (glucose, ethanol and, to a degree, trehalose) to non-growing baker's yeast cells causes a boost of protein synthesis, reaching maximum rate 20 min after addition of glucose and 40–50 min after ethanol or trehalose addition. The synthesis involves that of transport proteins for various solutes which appear in the following sequence: H⁺, l-proline, sulfate, l-leucine, phosphate, α-methyl-d-glucoside, 2-aminoisobutyrate. With the exception of the phosphate transport system, the K_t of the synthesized systems is the same as before stimulation. Glucose is usually the best stimulant, but ethanol matches it in the case of sulfate and exceeds it in the case of proline. This may be connected with ethanol's stimulating the synthesis of transport proteins both in mitochondria and in the cytosol while glucose acts on cytosolic synthesis alone. The stimulation is often repressed by ammonium ions (leucine, proline, sulfate, H⁺), by antimycin (proline, trehalose, sulfate, H⁺), by iodoacetamide (all systems tested), and by anaerobic preincubation (leucine, proline, trehalose, sulfate). It is practically absent in a respiration-deficient petite mutant, only little depressed in the op₁ mutant lacking ADP/ATP exchange in mitochondria, but totally suppressed (with the exception of transport of phosphate) in a low-phosphorus strain. The addition of glucose causes a drop in intracellular inorganic monophosphate by 30%, diphosphate by 45%, ATP by 70%, in total amino acids by nearly 50%, in transmembrane potential (absolute value) by about 50%, an increase of high-molecular-weight polyphosphate by 65%, of total cAMP by more than 100%, in the endogenous respiration rate by more than 100%, and a change of intracellular pH from 6.80 to 7.05. Ethanol caused practically no change in ATP, total amino acids, endogenous respiration, intracellular pH or transmembrane potential; a slight decrease in inorganic monophosphate and diphosphate and a sizeable increase in high-molecular-weight polyphosphate. The synthesis of the various transport proteins thus appears to draw its energy from different sources and with different susceptibility to inhibitors. It is much more stimulated in facultatively aerobic species (Saccharomyces cerevisiae, Endomyces magnusii) than in strictly aerobic ones (Rhodotorula glutinis, Candida parapsilosis) where an inhibition of transport activity is often observed after preincubation with metabolizable substrates.     

Reversal of peptide backbone direction may result in the mirroring of protein structure

In linear polypeptides, inversion of amino acid chirality (all- to all- ) achieves a mirroring of side chain positions and interactions in conformational space. A similar mirroring of side chain positions is independently achieved by a reversal of the direction of the peptide backbone (retro modification). Thus, while an all- chain could be expected to adopt a perfect ‘mirror image’ of the three-dimensional structure of its parent all- protein, the retro-all- chain could be expected to adopt a topological equivalent of such a mirror image, through the symmetry transformations of side chain interactions. These notions, supported by sequence analyses, modelling studies, and evidence relating to the activity of ‘retro-inverso’ peptides, are extended towards the proposal, that the backbone reversed chain of a large globular protein might recognize the chiral opposite of the parent protein's substrate(s).     

Physical and functional modularity of the protein network in yeast

While protein-protein interactions have been studied largely as a network graph without physicality, here we analyze two protein complex data sets of Saccharomyces cerevisiae to relate physical and functional modularity to the network topology. We study for the first time the number of different protein complexes as a function of the protein complex size and find that it follows an exponential decay with a characteristic number of about 7. This reflects the dynamics of complex formation and dissociation in the cell. The analysis of the protein usage by complexes shows an extensive sharing of subunits that is due to the particular organization of the proteome into physical complexes and functional modules. This promiscuity accounts for the high clustering in the protein net-work graph. Our results underscore the need to include the information contained in observed protein complexes into protein network analyses.     

Probabilistic reconstruction of ancestral protein sequences

Using a maximum-likelihood formalism, we have developed a method with which to reconstruct the sequences of ancestral proteins. Our approach allows the calculation of not only the most probable ancestral sequence but also of the probability of any amino acid at any given node in the evolutionary tree. Because we consider evolution on the amino acid level, we are better able to include effects of evolutionary pressure and take advantage of structural information about the protein through the use of mutation matrices that depend on secondary structure and surface accessibility. The computational complexity of this method scales linearly with the number of homologous proteins used to reconstruct the ancestral sequence.     

Genetic analyses of yeast protein serine/threonine phosphatases

Abstract Protein phosphorylation is an important regulatory phenomenon in yeasts just as in other eukaryotic cells and controls a wide variety of cellular processes. The importance of protein phosphatases as well as protein kinases as key elements in such control is becoming increasingly clear. Over the past four years since the first yeast protein phosphatase gene was isolated, many more such genes have been described and the number of genes encoding protein phosphatase catalytic subunits in Saccharomyces cerevisiae has comfortably entered double figures. Given the genetic approaches available, yeasts offer powerful systems for addressing the cellular roles of these enzymes. This review summarises the results of genetic studies aimed at determining the functions of protein serine/threoninc phosphatases in yeast.     

文本挖掘技术在整合蛋白与疾病关系资源中的应用

为了整合文献中大量的人类蛋白质与疾病相互关系的信息,通过文本挖掘和通路分析的方法从PubMed中的摘要提取出对应关系后,利用KEGG中的通路信息构建出人类蛋白质和疾病相互的一个网络效应,并构建了查询数据库,用户可以根据蛋白质名称、疾病名称、通路名称来进行多方面的查询。