Chitinase family GH18: evolutionary insights from the genomic history of a diverse protein family

Background  

Chitinases (EC.3.2.1.14) hydrolyze the β-1,4-linkages in chitin, an abundant N-acetyl-β-D-glucosamine polysaccharide that is a structural component of protective biological matrices such as insect exoskeletons and fungal cell walls. The glycoside hydrolase 18 (GH18) family of chitinases is an ancient gene family widely expressed in archea, prokaryotes and eukaryotes. Mammals are not known to synthesize chitin or metabolize it as a nutrient, yet the human genome encodes eight GH18 family members. Some GH18 proteins lack an essential catalytic glutamic acid and are likely to act as lectins rather than as enzymes. This study used comparative genomic analysis to address the evolutionary history of the GH18 multiprotein family, from early eukaryotes to mammals, in an effort to understand the forces that shaped the human genome content of chitinase related proteins.     

Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

ABSTRACT: BACKGROUND: In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease. RESULTS: We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores. CONCLUSIONS: The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.     

Functional and topological characterization of protein interaction networks

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.     

Identification and characterization of inulinases by bioinformatics analysis of bacterial glycoside hydrolases family 32 (GH32)

The glycoside hydrolase family contains enzymes that break the glycosidic bonds of carbohydrates by hydrolysis. Inulinase is one of the most important industrial enzymes in the family of Glycoside Hydrolases 32 (GH32). In this study, to identify and classify bacterial inulinases initially, 16,002 protein sequences belonging to the GH32 family were obtained using various databases. The inulin-effective enzymes (endoinulinase and exoinulinase) were identified. Eight endoinulinases (EC 3.2.1.7) and 4318 exoinulinases (EC 3.2.1.80) were found. Then, the localization of endoinulinase and exoinulinase enzymes in the cell was predicted. Among them, two extracellular endoinulinases and 1232 extracellular exoinulinases were found. The biochemical properties of 363 enzymes of the genus Arthrobacter, Bacillus, and Streptomyces (most abundant) showed that exoinulinases have an acid isoelectric point up to the neutral range due to their amino acid length. That is, the smaller the protein (336 aa), the more acidic the pI (4.39), and the larger the protein (1207 aa), the pI is in the neutral range (8.84). Also, a negative gravitational index indicates the hydrophilicity of exoinulinases. Finally, considering the biochemical properties affecting protein stability and post-translational changes studies, one enzyme for endoinulinase and 40 enzymes with desirable characteristics were selected to identify their enzyme production sources. To screen and isolate enzyme-containing strains, now with the expansion of databases and the development of bioinformatics tools, it is possible to classify, review and analyze a lot of data related to different enzyme-producing strains. Although, in laboratory studies, a maximum of 20 to 30 strains can be examined. Therefore, when more strains are examined, finally, strains with more stable and efficient enzymes were selected and introduced for laboratory activities. The findings of this study can help researchers to select the appropriate gene source from introduced strains for cloning and expression heterologous inulinase, or to extract native inulinase from introduced strains.     

Determinants of sweetness in proteins: a topological approach

Sweet taste in mammals is accounted for by a single receptor that shares homology with a metabotropic glutamate receptor. Most sweeteners are small molecular weight molecules that interact with small cavities in the so-called Venus Flytrap domains of the sweet receptor. The mechanism of action of larger molecules such as sweet proteins is, however, more difficult to interpret. The first and still the only general mechanism proposed for the action of sweet proteins, the "wedge model," hypothesizes that proteins bind to an external binding site of the active conformation of the sweet receptor. Here, I have extended the concept that inspired the wedge model using a combination of structural analysis, bioinformatics tools, and a relatively large dataset of mutations of the two most extensively studied sweet proteins, monellin and brazzein. I show here that it is possible to single out, among the ensemble yielded by low-resolution docking, a unique complex that satisfies simple topological constraints. These models of the complexes of monellin and brazzein are fully consistent with experimental evidence, thus providing predicting power for further validation of the wedge model.     

Identifying disease-specific genes based on their topological significance in protein networks

Background  

The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest.     

Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach

MOTIVATION: Network reconstruction of biological entities is very important for understanding biological processes and the organizational principles of biological systems. This work focuses on integrating both the literatures and microarray gene-expression data, and a combined literature mining and microarray analysis (LMMA) approach is developed to construct gene networks of a specific biological system. RESULTS: In the LMMA approach, a global network is first constructed using the literature-based co-occurrence method. It is then refined using microarray data through a multivariate selection procedure. An application of LMMA to the angiogenesis is presented. Our result shows that the LMMA-based network is more reliable than the co-occurrence-based network in dealing with multiple levels of KEGG gene, KEGG Orthology and pathway. AVAILABILITY: The LMMA program is available upon request.     

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.     

The mu transpososome through a topological lens

Phage Mu is the most efficient transposable element known, its high efficiency being conferred by an enhancer DNA element. Transposition is the end result of a series of well choreographed steps that juxtapose the enhancer and the two Mu ends within a nucleoprotein complex called the 'transpososome.' The particular arrangement of DNA and protein components lends extraordinary stability to the transpososome and regulates the frequency, precision, directionality, and mechanism of transposition. The structure of the transpososome, therefore, holds the key to understanding all of these attributes, and ultimately to explaining the runaway genetic success of transposable elements throughout the biological world. This review focuses on the path of the DNA within the Mu transpososome, as uncovered by recent topological analyses. It discusses why Mu topology cannot be analyzed by standard methods, and how knowledge of the geometry of site alignment during Flp and Cre site-specific recombination was harnessed to design a new methodology called 'difference topology.' This methodology has also revealed the order and dynamics of association of the three interacting DNA sites, as well as the role of the enhancer in assembly of the Mu transpososome.     

All-atom contact potential approach to protein thermostability analysis

In this paper we use all-atom potential energy to define and analyze the inter-residue contacts in mesophilic and thermophilic proteins. Fifteen families of proteins are selected and each family has two representative proteins with greatly different preferred environmental temperatures. We find that both the number and energy of the contacts defined in this way show stronger correlations with the preferred temperatures of proteins than other factors used before. We also find that the charged-polar and charged-nonpolar residue contacts not only have larger contact numbers but also have lower single contact energies. Furthermore, the most important is that most of the thermophilic proteins have more charged-polar and charged-nonpolar residue contacts than their mesophilic counterparts. This suggests that they may play an important role in the thermostability of proteins, except usual charged-charged and nonpolar-nonpolar residue contacts. Charged residues may exert their profound influence by forming contacts not only with other charged residues but also with polar or nonpolar residues, thus further increasing the strength of contact network and then the thermostability of proteins.     

Signalling through cerebral cavernous malformation protein networks

Cerebral cavernous malformations (CCMs) are neurovascular abnormalities characterized by thin, leaky blood vessels resulting in lesions that predispose to haemorrhages, stroke, epilepsy and focal neurological deficits. CCMs arise due to loss-of-function mutations in genes encoding one of three CCM complex proteins, KRIT1, CCM2 or CCM3. These widely expressed, multi-functional adaptor proteins can assemble into a CCM protein complex and (either alone or in complex) modulate signalling pathways that influence cell adhesion, cell contractility, cytoskeletal reorganization and gene expression. Recent advances, including analysis of the structures and interactions of CCM proteins, have allowed substantial progress towards understanding the molecular bases for CCM protein function and how their disruption leads to disease. Here, we review current knowledge of CCM protein signalling with a focus on three pathways which have generated the most interest—the RhoA–ROCK, MEKK3–MEK5–ERK5–KLF2/4 and cell junctional signalling pathways—but also consider ICAP1-β1 integrin and cdc42 signalling. We discuss emerging links between these pathways and the processes that drive disease pathology and highlight important open questions—key among them is the role of subcellular localization in the control of CCM protein activity.     

Evolving protein interaction networks through gene duplication

The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein-protein interactions is highly heterogeneous, with many proteins having few edges while a few of them are heavily connected. This particular topology is shared by other cellular networks, such as metabolic pathways, and it has been suggested to be responsible for the high mutational homeostasis displayed by the genome of some organisms. In this paper we explore a recent model of proteome evolution that has been shown to reproduce many of the features displayed by its real counterparts. The model is based on gene duplication plus re-wiring of the newly created genes. The statistical features displayed by the proteome of well-known organisms are reproduced and suggest that the overall topology of the protein maps naturally emerges from the two leading mechanisms considered by the model.     

A conformation ensemble approach to protein residue-residue contact

Background

Protein residue-residue contact prediction is important for protein model generation and model evaluation. Here we develop a conformation ensemble approach to improve residue-residue contact prediction. We collect a number of structural models stemming from a variety of methods and implementations. The various models capture slightly different conformations and contain complementary information which can be pooled together to capture recurrent, and therefore more likely, residue-residue contacts.

Results

We applied our conformation ensemble approach to free modeling targets from both CASP8 and CASP9. Given a diverse ensemble of models, the method is able to achieve accuracies of. 48 for the top L/5 medium range contacts and. 36 for the top L/5 long range contacts for CASP8 targets (L being the target domain length). When applied to targets from CASP9, the accuracies of the top L/5 medium and long range contact predictions were. 34 and. 30 respectively.

Conclusions

When operating on a moderately diverse ensemble of models, the conformation ensemble approach is an effective means to identify medium and long range residue-residue contacts. An immediate benefit of the method is that when tied with a scoring scheme, it can be used to successfully rank models.     

Signaling networks in Leishmania macrophages deciphered through integrated systems biology: a mathematical modeling approach

Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.     

Modeling biochemical networks: a cellular-automata approach

The potential of the cellular-automata (CA) method for modeling biological networks is demonstrated for the mitogen-activated protein kinase (MAPK) signaling cascade. The models derived reproduced the high signal amplification through the cascade and the deviation of the cascade enzymes from the Michaelis-Menten kinetics, evidencing cooperativity effects. The patterns of pathway change upon varying substrate concentrations and enzyme efficiencies were identified and used to show the ways for controlling pathway processes. Guidance in the selection of enzyme inhibition targets with minimum side effects is one outcome of the study.     

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.     

Enhanced protein fold recognition through a novel data integration approach

Background  

Protein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived from local sequence alignments. This raises the issue of finding the most efficient method for combining these different informative data sources and exploring their relative significance for protein fold classification. Kernel methods have been extensively used for biological data analysis. They can incorporate separate fold discriminatory features into kernel matrices which encode the similarity between samples in their respective data sources.