Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

Metal ions and carcinogenesis

Metals are essential for the normal functioning of living organisms. Their uses in biological systems are varied, but are frequently associated with sites of critical protein function, such as zinc finger motifs and electron or oxygen carriers. These functions only require essential metals in minute amounts, hence they are termed trace metals. Other metals are, however, less beneficial, owing to their ability to promote a wide variety of eleterious health effects, including cancer. Metals such as arsenic, for example, an produce a variety of diseases ranging from keratosis of the palms and feet to cancers in multiple target organs. The nature and type of metal-induced pathologies appear to be dependent on the concentration, speciation, and length of exposure. Unfortunately, human contact with metals is an inescapable consequence of human life, with exposures occurring from both occupational and environmental sources. A uniform mechanism of action for all harmful metals is unlikely, if not implausible, given the diverse chemical properties of each metal. In this chapter we will review the mechanisms of carcinogenesis of arsenic, cadmium, chromium, and nickel, the four known carcinogenic metals that are best understood. The key areas of speciation, bioavailability, and mechanisms of action are discussed with particular reference to the role of metals in alteration of gene expression and maintenance of genomic integrity.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

High torsional energy disulfides : relationship between cross-strand disulfides and right-handed staples

Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides are also present in non-enzymatic protein structures where they may act as switches of protein function. Here, we examine disulfides linking adjacent β-strands (cross-strand disulfides), which have been reported to be redox-active. Our previous work has established that these cross-strand disulfides have high torsional energies, a quantity likely to be related to the ease with which the disulfide is reduced. We examine the relationship between conformations of disulfides and their location in protein secondary structures. By identifying the overlap between cross-strand disulfides and various conformations, we wish to address whether the high torsional energy of a cross-strand disulfide is sufficient to confer redox activity or whether other factors, such as the presence of the cross-strand disulfide in a strained β-sheet, are required.

Disulfides as redox switches : from molecular mechanisms to functional significance

The molecular mechanisms underlying thiol-based redox control are poorly defined. Disulfide bonds between Cys residues are commonly thought to confer extra rigidity and stability to their resident protein, forming a type of proteinaceous spot weld. Redox biologists have been redefining the role of disulfides over the last 30–40 years. Disulfides are now known to form in the cytosol under conditions of oxidative stress. Isomerization of extracellular disulfides is also emerging as an important regulator of protein function. The current paradigm is that the disulfide proteome consists of two subproteomes: a structural group and a redox-sensitive group. The redoxsensitive group is less stable and often associated with regions of stress in protein structures. Some characterized redox-active disulfides are the helical CXXC motif, often associated with thioredoxin-fold proteins; and forbidden disulfides, a group of metastable disulfides that disobey elucidated rules of protein stereochemistry. Here we discuss the role of redox-active disulfides as switches in proteins.

Transcriptional profiling of growth perturbations of the human malaria parasite Plasmodium falciparum

Functions have yet to be defined for the majority of genes of Plasmodium falciparum, the agent responsible for the most serious form of human malaria. Here we report changes in P. falciparum gene expression induced by 20 compounds that inhibit growth of the schizont stage of the intraerythrocytic development cycle. In contrast with previous studies, which reported only minimal changes in response to chemically induced perturbations of P. falciparum growth, we find that ~59% of its coding genes display over three-fold changes in expression in response to at least one of the chemicals we tested. We use this compendium for guilt-by-association prediction of protein function using an interaction network constructed from gene co-expression, sequence homology, domain-domain and yeast two-hybrid data. The subcellular localizations of 31 of 42 proteins linked with merozoite invasion is consistent with their role in this process, a key target for malaria control. Our network may facilitate identification of novel antimalarial drugs and vaccines.

X-ray crystallography : applications in the biotechnology research

X-ray crystallography is essentially a form of very high resolution microscopy. It enables us to visualize protein structures at the atomic level and enhances our understanding of protein function. Specifically we can study how proteins interact with other molecules, how they undergo conformational changes, and how they perform catalysis in the case of enzymes. Armed with this information we can design novel drugs that target a particular protein, or rationally engineer an enzyme for a specific industrial process.

Glutaredoxin1 protects neuronal cells from copper-induced toxicity

Glutaredoxin1 (GRX1) is a glutathione (GSH)-dependent thiol oxidoreductase. The GRX1/GSH system is important for the protection of proteins from oxidative damage and in the regulation of protein function. Previously we demonstrated that GRX1/GSH regulates the activity of the essential copper-transporting P1B-Type ATPases (ATP7A, ATP7B) in a copper-responsive manner. It has also been established that GRX1 binds copper with high affinity and regulates the redox chemistry of the metallochaperone ATOX1, which delivers copper to the copper-ATPases. In this study, to further define the role of GRX1 in copper homeostasis, we examined the effects of manipulating GRX1 expression on copper homeostasis and cell survival in mouse embryonic fibroblasts and in human neuroblastoma cells (SH-SY5Y). GRX1 knockout led to cellular copper retention (especially when cultured with elevated copper) and reduced copper tolerance, while in GRX1-overexpressing cells challenged with elevated copper, there was a reduction in both intracellular copper levels and copper-induced reactive oxygen species, coupled with enhanced cell proliferation. These effects are consistent with a role for GRX1 in regulating ATP7A-mediated copper export, and further support a new function for GRX1 in neuronal copper homeostasis and in protection from copper-mediated oxidative injury.

Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community.

The structure of communities in scale-free networks

Scale-free networks are often used to model a wide range of real-world networks, such as social, technological, and biological networks. Understanding the structure of scale-free networks evolves into a big data problem for business, management, and protein function prediction. In the past decade, there has been a surge of interest in exploring the properties of scale-free networks. Two interesting properties have attracted much attention: the assortative mixing and community structure. However, these two properties have been studied separately in either theoretical models or real-world networks. In this paper, we show that the structural features of communities are highly related with the assortative mixing in scale-free networks. According to the value of assortativity coefficient, scale-free networks can be categorized into assortative, disassortative, and neutral networks, respectively. We systematically analyze the community structure in these three types of scale-free networks through six metrics: node embeddedness, link density, hub dominance, community compactness, the distribution of community sizes, and the presence of hierarchical communities. We find that the three types of scale-free networks exhibit significant differences in these six metrics of community structures. First, assortative networks present high embeddedness, meaning that many links lying within communities but few links lying between communities. This leads to the high link density of communities. Second, disassortative networks exhibit great hubs in communities, which results in the high compactness of communities that nodes can reach each other via short paths. Third, in neutral networks, a big portion of links act as community bridges, so they display sparse and less compact communities. In addition, we find that (dis)assortative networks show hierarchical community structure with power-law-distributed community sizes, while neutral networks present no hierarchy. Understanding the structure of communities from the angle of assortative mixing patterns of nodes can provide insights into the network structure and guide us in modeling information propagation in different categories of scale-free networks.

A similarity search algorithm to predict protein structures

Accurate prediction of protein structures is very important for many applications such as drug discovery and biotechnology. Building side chains is an essential to get any reliable prediction of the protein structure for any given a protein main chain conformation. Most of the methods that predict side chain conformations use statistically generated data from known protein structures. It is a computationally intractable problem to search suitable side chains from all possible rotamers simultaneously using information of known protein structures. Reducing the number of possibility is a main issue to predict side chain conformation. This paper proposes an enumeration based similarity search algorithm to predict side chain conformations. By introducing “beam search” technique, a significant number of unrelated side chain rotamers can easily be eliminated. As a result, we can search for suitable residue side chains from all possible side chain conformations.

The last 10 amino acid residues beyond the hydrophobic motif are critical for the catalytic competence and function of protein kinase Cá*

The segment C-terminal to the hydrophobic motif at the V5 domain of protein kinase C (PKC) is the least conserved both in length and in amino acid identity among all PKC isozymes. By generating serial truncation mutants followed by biochemical and functional analyses, we show here that the very C terminus of PKCα is critical in conferring the full catalytic competence to the kinase and for transducing signals in cells. Deletion of one C-terminal amino acid residue caused the loss of ~60% of the catalytic activity of the mutant PKCα, whereas deletion of 10 C-terminal amino acid residues abrogated the catalytic activity of PKCα in immune complex kinase assays. The PKCα C-terminal truncation mutants were found to lose their ability to activate mitogen-activated protein kinase, to rescue apoptosis induced by the inhibition of endogenous PKC in COS cells, and to augment melatonin-stimulated neurite outgrowth. Furthermore, molecular dynamics simulations revealed that the deletion of 1 or 10 C-terminal residues results in the deformation of the V5 domain and the ATP-binding pocket, respectively. Finally, PKCα immunoprecipitated using an antibody against its C terminus had only marginal catalytic activity compared with that of the PKCα immunoprecipitated by an antibody against its N terminus. Therefore, the very C-terminal tail of PKCα is a novel determinant of the catalytic activity of PKC and a promising target for selective modulation of PKCα function. Molecules that bind preferentially to the very C terminus of distinct PKC isozymes and suppress their catalytic activity may constitute a new class of selective inhibitors of PKC.

Determinants of female fertility in Taiwan, 1966–2001 : empirical evidence from cointegration and variance decomposition analysis

This study investigates the determinants of the fertility rate in Taiwan over the period 1966–2001. Consistent with theory, the key explanatory variables in Taiwan's fertility model are real income, infant mortality rate, female education and female labor force participation rate. The test for cointegration is based on the recently developed bounds testing procedure while the long-run and short-run elasticities are based on the autoregressive distributed lag model. Among our key results, female education and female labor force participation rate are found to be the key determinants of fertility in Taiwan in the long run. The variance decom-position analysis indicates that in the long run approximately 45percent of the variation in fertility is explained by the combined impact of female labor force participation, mortality and income, implying that socioeconomic development played an important role in the fertility transition in Taiwan. This result is consistent with the traditional structural hypothesis.

An empirical analysis of Fiji’s import demand function

Purpose – This paper aims to estimate a disaggregated import demand model for Fiji using relative prices, total consumption, investment expenditure and export expenditure variables for the period 1970 to 2000.

Design/methodology/approach – The recently developed bounds testing approach to cointegration to test for a long run relationship is used, while the autoregressive distributed lag model is used to estimate short run and long run elasticities. These methodologies are shown to perform well in small sample sizes, particularly given that the bounds F-test critical values for small sample sizes generated by Narayan in 2004 and 2005 are used.

Findings – Amongst the key results it is found: a long run cointegration relationship among the variables when import demand is the dependent variable; and import demand to be inelastic and statistically significant at the 1 per cent level with respect to all the explanatory variables in both the long-run and the short-run.

Originality/value – The disaggregated import demand model estimated here provides a complete picture of the determinants of Fiji's imports. This model can be used by Fijian policy makers to draw pertinent policies and forecast import demand for Fiji.

Study mode and student university choice : an empirical investigation

The thesis investigated the importance of study mode in university choice relative to other considerations. Study mode was found to be more important than either university or tuition fees. However, differences existed across segments. Also identified were segments based on students' preferences for combinations of face-to-face and distance study modes.