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  • 1
    ISSN: 0960-894X
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Chemistry and Pharmacology , Medicine
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Applied Surface Science 75 (1994), S. 151-156 
    ISSN: 0169-4332
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Physics
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2015-06-14
    Description: Motivation: Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized. Results: In this study, we developed a novel method for supervised de novo metabolic pathway reconstruction with an improved graph alignment-based approach in the reaction-filling framework. We proposed a novel chemical graph alignment algorithm, which we called PACHA (Pairwise Chemical Aligner), to detect the regioisomer-sensitive connectivities between the aligned substructures of two compounds. Unlike other existing graph alignment methods, PACHA can efficiently detect only one common subgraph between two compounds. Our results show that the proposed method outperforms previous descriptor-based methods or existing graph alignment-based methods in the enzymatic reaction-likeness prediction for isomer-enriched reactions. It is also useful for reaction annotation that assigns potential reaction characteristics such as EC (Enzyme Commission) numbers and PIERO (Enzymatic Reaction Ontology for Partial Information) terms to substrate–product pairs. Finally, we conducted a comprehensive enzymatic reaction-likeness prediction for all possible uncharacterized compound pairs, suggesting potential metabolic pathways for newly predicted substrate–product pairs. Contact: maskot@bio.titech.ac.jp
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 4
    Publication Date: 2002-07-15
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 5
    Publication Date: 2016-06-16
    Description: Motivation : Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation : Contact : maskot@bio.titech.ac.jp
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 6
    Publication Date: 2012-09-08
    Description: Motivation: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug–target interactions is crucial in the drug design process. Results: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug–target interaction networks. We propose a novel algorithm for extracting informative chemogenomic features by using L 1 regularized classifiers over the tensor product space of possible drug–target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug–target interactions and the extracted features are biologically meaningful. The extracted substructure–domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. Availability: Softwares are available at the supplemental website. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/l1binary/ .
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 7
    Publication Date: 2012-09-08
    Description: Motivation: Unexpected drug activities derived from off-targets are usually undesired and harmful; however, they can occasionally be beneficial for different therapeutic indications. There are many uncharacterized drugs whose target proteins (including the primary target and off-targets) remain unknown. The identification of all potential drug targets has become an important issue in drug repositioning to reuse known drugs for new therapeutic indications. Results: We defined pharmacological similarity for all possible drugs using the US Food and Drug Administration's (FDA's) adverse event reporting system (AERS) and developed a new method to predict unknown drug–target interactions on a large scale from the integration of pharmacological similarity of drugs and genomic sequence similarity of target proteins in the framework of a pharmacogenomic approach. The proposed method was applicable to a large number of drugs and it was useful especially for predicting unknown drug–target interactions that could not be expected from drug chemical structures. We made a comprehensive prediction for potential off-targets of 1874 drugs with known targets and potential target profiles of 2519 drugs without known targets, which suggests many potential drug–target interactions that were not predicted by previous chemogenomic or pharmacogenomic approaches. Availability: Softwares are available upon request. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/aers/ .
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 8
    Publication Date: 2012-09-08
    Description: Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles. Supplementary information: Datasets and all results are available at http://web.kuicr.kyoto-u.ac.jp/supp/smizutan/target-effect/ . Availability: Software is available at the above supplementary website. Contact: yamanishi@bioreg.kyushu-u.ac.jp , or goto@kuicr.kyoto-u.ac.jp
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 9
    Publication Date: 2019
    Description: 〈p〉Basophils have nonredundant roles in various immune responses that require Ca〈sup〉2+〈/sup〉 influx. Here, we examined the role of two Ca〈sup〉2+〈/sup〉 sensors, stromal interaction molecule 1 and 2 (STIM1 and STIM2), in basophil activation. We found that loss of STIM1, but not STIM2, impaired basophil IL-4 production after stimulation with immunoglobulin E (IgE)–containing immune complexes. In contrast, when basophils were stimulated with IL-3, loss of STIM2, but not STIM1, reduced basophil IL-4 production. This difference in STIM proteins was associated with distinct time courses of Ca〈sup〉2+〈/sup〉 influx and transcription of the 〈i〉Il4〈/i〉 gene that were elicited by each stimulus. Similarly, basophil-specific STIM1 expression was required for IgE-driven chronic allergic inflammation in vivo, whereas STIM2 was required for IL-4 production after combined IL-3 and IL-33 treatment in mice. These data indicate that STIM1 and STIM2 have differential roles in the production of IL-4, which are stimulus dependent. Furthermore, these results illustrate the vital role of STIM2 in basophils, which is often considered to be less important than STIM1.〈/p〉
    Print ISSN: 1945-0877
    Electronic ISSN: 1937-9145
    Topics: Biology , Medicine
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  • 10
    Publication Date: 2019
    Description: 〈p〉Zymosan is a glucan that is a component of the yeast cell wall. Here, we determined the mechanisms underlying the zymosan-induced accumulation of neutrophils in mice. Loss of the receptor CD300b reduced the number of neutrophils recruited to dorsal air pouches in response to zymosan, but not in response to lipopolysaccharide (LPS), a bacterial membrane component recognized by Toll-like receptor 4 (TLR4). An inhibitor of nitric oxide (NO) synthesis reduced the number of neutrophils in the zymosan-treated air pouches of wild-type mice to an amount comparable to that in 〈i〉CD300b〈sup〉–/–〈/sup〉〈/i〉 mice. Treatment with clodronate liposomes decreased the number of NO-producing, CD300b〈sup〉+〈/sup〉 inflammatory dendritic cells (DCs) in wild-type mice, thus decreasing NO production and neutrophil recruitment. Similarly, CD300b deficiency decreased the NO-dependent recruitment of neutrophils to zymosan-treated joint cavities, thus ameliorating subsequent arthritis. We identified phytosphingosine, a lipid component of zymosan, as a potential ligand of CD300b. Phytosphingosine stimulated NO production in inflammatory DCs and promoted neutrophil recruitment in a CD300b-dependent manner. Together, these results suggest that the phytosphingosine-CD300b interaction promotes zymosan-dependent neutrophil accumulation by inducing NO production by inflammatory DCs and that CD300b may contribute to antifungal immunity.〈/p〉
    Print ISSN: 1945-0877
    Electronic ISSN: 1937-9145
    Topics: Biology , Medicine
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