共查询到20条相似文献,搜索用时 15 毫秒
1.
Bilachi S Ravindranath Venkatappa Krishnamurthy Venkatarangaiah Krishna Kahale Bommaiah Lingaiah Vasudevanayaka 《Bioinformation》2013,9(12):605-609
Chlamydophila pneumoniae, the causative agent of chronic obstructive pulmonary disease (COPD), is presently the fifth mortality
causing chronic disease in the world. The understanding of disease and treatment options are limited represents a severe concern
and a need for better therapeutics. With the advancements in the field of complete genome sequencing and computational
approaches development have lead to metabolic pathway analysis and protein-protein interaction network which provides vital
evidence to the protein function and has been appropriate to the fields such as systems biology and drug discovery. Protein
interaction network analysis allows us to predict the most potential drug targets among large number of the non-homologous
proteins involved in the unique metabolic pathway. A computational comparative metabolic pathway analysis of the host H.
sapiens and the pathogen C pneumoniae AR39 has been carried out at three level analyses. Firstly, metabolic pathway analysis was
performed to identify unique metabolic pathways and non-homologous proteins were identified. Secondly, essentiality of the
proteins was checked, where these proteins contribute to the growth and survival of the organism. Finally these proteins were
further subjected to predict protein interaction networks. Among the total 65 pathways in the C pneumoniae AR39 genome 10 were
identified as the unique metabolic pathways which were not found in the human host, 32 enzymes were predicted as essential and
these proteins were considered for protein interaction analysis, later using various criteria''s we have narrowed down to prioritize
ribonucleotide-diphosphate reductase subunit beta as a potential drug target which facilitate for the successful entry into drug
designing. 相似文献
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Background
Whole genome duplication (WGD) occurs widely in angiosperm evolution. It raises the intriguing question of how interacting networks of genes cope with this dramatic evolutionary event.Results
In study of the Arabidopsis metabolic network, we assigned each enzyme (node) with topological centralities (in-degree, out-degree and between-ness) to measure quantitatively their centralities in the network. The Arabidopsis metabolic network is highly modular and separated into 11 interconnected modules, which correspond well to the functional metabolic pathways. The enzymes with higher in-out degree and between-ness (defined as hub and bottleneck enzymes, respectively) tend to be more conserved and preferentially retain homeologs after WGD. Moreover, the simultaneous retention of homeologs encoding enzymes which catalyze consecutive steps in a pathway is highly favored and easily achieved, and enzyme-enzyme interactions contribute to the retention of one-third of WGD enzymes.Conclusions
Our analyses indicate that the hub and bottleneck enzymes of metabolic network obtain great benefits from WGD, and this event grants clear evolutionary advantages in adaptation to different environments. 相似文献3.
Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery. 相似文献
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Berkowitz O Wirtz M Wolf A Kuhlmann J Hell R 《The Journal of biological chemistry》2002,277(34):30629-30634
Real time biomolecular interaction analysis based on surface plasmon resonance has been proven useful for studying protein-protein interaction but has not been extended so far to investigate enzyme-enzyme interactions, especially as pertaining to regulation of metabolic activity. We have applied BIAcore technology to study the regulation of enzyme-enzyme interaction during mitochondrial cysteine biosynthesis in Arabidopsis thaliana. The association of the two enzyme subunits in the hetero-oligomeric cysteine synthase complex was investigated with respect to the reaction intermediate and putative effector O-acetylserine. We have determined an equilibrium dissociation constant of the cysteine synthase complex (K(D) = 25 +/- 4 x 10(-9) m), based on a reliable A + B <--> AB model of interaction. Analysis of dissociation kinetics in the presence of O-acetylserine revealed a half-maximal dissociation rate at 77 +/- 4 microm O-acetylserine and strong positive cooperativity for complex dissociation. The equilibrium of interaction was determined using an enzyme activity-based approach and yielded a K(m) value of 58 +/- 7 microm O-acetylserine. Both effector concentrations are in the range of intracellular O-acetylserine fluctuations and support a functional model that integrates effector-driven cysteine synthase complex dissociation as a regulatory switch for the biosynthetic pathway. The results show that BIAcore technology can be applied to obtain quantitative kinetic data of a hetero-oligomeric protein complex with enzymatic and regulatory function. 相似文献
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INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation
Marzia Di Filippo Dario Pescini Bruno Giovanni Galuzzi Marcella Bonanomi Daniela Gaglio Eleonora Mangano Clarissa Consolandi Lilia Alberghina Marco Vanoni Chiara Damiani 《PLoS computational biology》2022,18(2)
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Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology
Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. 相似文献
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Background
The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.Results
Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.Conclusion
Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network. 相似文献11.
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Sidney A. Bernhard 《Cell biochemistry and biophysics》1988,12(1):119-132
A new model for the organization and flow of metabolites through a metabolic pathway is presented. The model is based on four
major findings. (1) The intracellular concentrations of enzyme sites exceed the concentrations of intermediary metabolites
that bind specifically to these sites. (2) The concentration of the excessive enzyme sites in the cell is sufficiently high
so that nearly all the cellular intermediary metabolites are enzyme-bound. (3) Enzyme conformations are perturbed by the interactions
with substrates and products; the conformations of enzyme-substrate and enzyme-product complexes are different. (4) Two enzymes,
catalyzing reactions that are sequential in a metabolic pathway, transfer the common metabolite back and forth via an enzyme-enzyme
complex without the intervention of the solvent environment. The model proposes that the enzyme-enzyme recognition is ligand-induced.
Conversion of E2S and E2P results in the loss of recognition of E2 by E1 and the concomitant recognition of E2 by E3. This model substantially alters existent views of the bioenergetics and the kinetics of intracellular metabolism. The rates
of direct transfer of metabolite from enzyme to enzyme are comparable to the rates of interconversion between substrate and
product within an individual enzyme. Consequently, intermediary metabolites are nearly equipartitioned among their high-affinity
enzyme sites within a metabolic pathway. Metabolic flux involves the direct transfer of metabolite from enzyme to enzyme via
a set of low and nearly equal energy barriers. 相似文献
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It has long been believed that cells organize their cytoplasm so as to efficiently channel metabolites between sequential enzymes. This metabolic channeling has the potential to yield higher metabolic fluxes as well as better regulatory control over metabolism. One mechanism for achieving such channeling is to ensure that sequential enzymes in a pathway are physically close to each other in the cell. We present evidence that indirect protein interactions between related enzymes represent a global mechanism for achieving metabolic channeling; the intuition being that protein interactions between enzymes and non-enzymatic mediator proteins are a powerful means of physically associating enzymes in a modular fashion. By analyzing the metabolic and protein-protein interactions networks of Escherichia coli, yeast and humans, we are able to show that all three species have many more indirect protein interactions linking enzymes that share metabolites than would be expected by chance. Moreover, these interactions are distributed non-randomly in the metabolic network. Our analyses in yeast and E. coli show that reactions possessing such interactions also show higher flux than do those lacking them. On the basis of these observations, we suggest that an important role of protein interactions with mediator proteins is to contribute to the spatial organization of the cell. This hypothesis is supported by the fact that these mediator proteins are also enriched with annotations related to signal transduction, a system where scaffolding proteins are known to limit cross-talk by controlling spatial localization. 相似文献
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Hong Zhang Jinan Chen Yongqiang Wang Lin Peng Yi Lu Amy E. Keating Taijiao Jiang 《Journal of molecular biology》2009,392(1):228-2757
Mapping protein-protein interactions at a domain or motif level can provide structural annotation of the interactome. The α-helical coiled coil is among the most common protein-interaction motifs, and proteins predicted to contain coiled coils participate in diverse biological processes. Here, we introduce a combined computational/experimental screening strategy that we used to uncover coiled-coil interactions among proteins involved in vesicular trafficking in Saccharomyces cerevisiae. A number of coiled-coil complexes have already been identified and reported to play important roles in this important biological process. We identify additional examples of coiled coils that can form physical associations. The computational strategy used to prioritize coiled-coil candidates for testing dramatically improved the efficiency of discovery in a large experimental screen. As assessed by comprehensive yeast two-hybrid assays, computational prefiltering retained 90% of positive interacting pairs and eliminated > 60% of negatives from a set of interaction candidates. The coiled-coil-mediated interaction network elucidated using the combined computational/experimental approach comprises 80 coiled-coil associations between 58 protein pairs, among which 21 protein interactions have not been previously reported in interaction databases and 26 interactions were previously known at the protein level but have now been localized to the coiled-coil motif. The coiled-coil-mediated interactions were specific rather than promiscuous, and many interactions could be recapitulated in a green fluorescent protein complementation assay. Our method provides an efficient route to discovering new coiled-coil interactions and uncovers a number of associations that may have functional significance for vesicular trafficking. 相似文献
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Background
Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression. 相似文献20.