Chem-Bio Informatics Journal
Online ISSN : 1347-0442
Print ISSN : 1347-6297
ISSN-L : 1347-0442
Volume 11
Displaying 1-5 of 5 articles from this issue
Original
  • Yuki Kuriya, Shigemitsu Tanaka, Genta Kobayashi, Taizo Hanai, Masahiro ...
    2011 Volume 11 Pages 1-23
    Published: 2011
    Released on J-STAGE: February 10, 2011
    JOURNAL FREE ACCESS
    Attempts were made to design and develop an analytical pipeline for identifying ways of increasing the production of a target metabolite that combine an optimized substrate-feeding schedule with a strategy for identifying and eliminating bottlenecks in metabolic pathways. As a case study, the proposed analytical pipeline was applied to acetone-butanol-ethanol fermentation by Clostridium bacteria, a process that has attracted considerable attention as a metabolic system capable of producing butanol, a possible biofuel.
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  • Shuhei Kimura, Koki Matsumura, Mariko Okada-Hatakeyama
    2011 Volume 11 Pages 24-40
    Published: 2011
    Released on J-STAGE: February 25, 2011
    JOURNAL FREE ACCESS
    The problem decomposition strategy is a very efficient technique for the inference of S-system models of genetic networks. This strategy defines the inference of a genetic network consisting of N genes as N subproblems, each of which is a 2(N+1)-dimensional function optimization problem. Genetic networks made up of dozens genes can be analyzed with this strategy, though the computational cost in doing so remains quite high. In this study, we attempt to infer S-system models more efficiently by further dividing each 2(N+1)-dimensional subproblem into one (N+2)-dimensional problem and one (N+1)-dimensional problem. The subproblems are divided using the genetic network inference method based on linear programming machines (LPMs). Next, we propose a new method for estimating the S-system parameters by alternately solving the two divided problems. According to our experimental results, the proposed approach requires less than one-third of the time required by the original problem decomposition approach. Finally, we apply our approach to actual expression data from the bacterial SOS DNA repair system.
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  • Tadahiro Suzuki, Yumiko Iwahashi
    2011 Volume 11 Pages 41-51
    Published: 2011
    Released on J-STAGE: May 25, 2011
    JOURNAL FREE ACCESS
    Deoxynivalenol (DON) is a secondary metabolite that is generated by Fusarium species, which seriously affects both humans and livestock. Protein synthesis inhibition and ribotoxic stress, caused by induction of the mitogen activated protein kinase (MAPK) cascade, are thought to be responsible for the majority of DON toxicity. However, as DNA damage has also been reported, it is necessary to clarify all sources of toxicity. In this study, we conducted a DON exposure test using the PTC1 yeast mutant with disrupted MAPK-related genes, and observed gene expression changes using DNA microarray analysis. Our results indicated changes in the expression of genes associated with protein synthesis inhibition, as well as with DNA damage. At the same time, genes related to the synthesis of folic acid, a coenzyme in DNA synthesis, were inhibited. To complement the dysfunction of these genes, the growth media was supplemented with folic acid. As a result, the recovery of growth was confirmed, although it was a consistent effect and it did not reflect differences in susceptibility to DON toxicity.
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  • Kenji Onodera, Takayasu Kawasaki, Shunsuke Kamijo
    2011 Volume 11 Pages 52-62
    Published: 2011
    Released on J-STAGE: June 13, 2011
    JOURNAL FREE ACCESS
    Bacterial RNA polymerase (RNAP) is the least popular target for antibiotics, and currently Rifampicin is only an approved drug for clinical use. However, RNAP is essential for bacterial growth and survival, and it can be a promising target for antimicrobial agents. Thus, we decided to search new antimicrobial agents for RNAP by virtual screening. When virtual screenings are performed, certain compounds repeatedly appears on hits covering a wide range of targets (frequently hitters). Also, the performance of hit generation is important factor in success of the virtual screening. Since we previously developed the optimized docking scores, we examined our scoring methods with rigorous removals of frequent hitters. We used two complex structures for RNAP, and also used two unrelated structures as negative controls to remove frequent hitters. Finally, we selected seven high-scored candidates from hits, and two of them showed the inhibition of Gram-positive bacteria by paper disk agar diffusion assay in vivo.
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  • Hikmet Cetin, Takeshi N Sasaki, Masaki Sasai
    2011 Volume 11 Pages 63-81
    Published: 2011
    Released on J-STAGE: December 05, 2011
    JOURNAL FREE ACCESS
    Prediction of three-dimensional structures of proteins from amino-acid sequences is an important problem of bioinformatics. We still do not have, however, a reliable computational method for de novo prediction, i.e., prediction of structures that do not have homologues of known structures. One way to improve techniques of de novo prediction is to assess quality of the predicted model structures to select candidate models from them before knowing the experimentally determined structure. In this paper, we develop a new method of model quality assessment for de novo prediction, the fragment-based consistency score (FCS) method. In the FCS method, fragments from library proteins are collected for each fragment of the model, and structural similarities between the model fragment and the library fragments are measured for assessing qualities of the model. Three structural indices are employed; secondary structure, local density and local contact order. The optimal performance was obtained when relatively correlated fragments are collected, and structural similarity is measured in fuzzy comparison and averaged by finite width matching. The FCS method can select partially correct models for hard targets of de novo prediction examined in CASP7 and CASP8. We also evaluated the abilities of the FCS method to select structural models of loops or coil regions in targets, and to distinguish proteins which are similar in sequence but have greatly different conformations from each other. The proposed FCS method helps to improve the de novo prediction scheme and is also useful to solve difficult structures in the template-based modeling.
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