Biomedical subjects

Shigeru Muta

Publications and source records attributed to Shigeru Muta.

6 recordsLinked to original sources

PubMed2006

Construction of enzyme-substrate complexes between hen egg-white lysozyme and N-acetyl-D-glucosamine hexamer by systematic conformational search and molecular dynamics simulation.

We constructed the complexes between HEWL and (GlcNAc)6 oligomer in order to investigate the amino acid residues related to substrate binding in the productive and nonproductive complexes, and the relationship between the distortion of the GlcNAc residue D and the formation of the productive complexes. We obtained 49 HEWL-(GlcNAc)6 complexes by a systematic conformational search and classified the each one to the three binding modes; left side, center, or right side. Furthermore we performed the molecular dynamics simulation against 20 HEWL-(GlcNAc)6 complexes (8: chair model, 12 : half-chair model) selected from the 49 complexes to investigate the interaction between HEWL and (GlcNAc)6. As results, we confirmed that it is necessary for GlcNAc residue D to be half-chaired form to bind toward the right side to form productive complexes. We found newly that eight amino acid residues interact with the (GlcNAc)6 oligomer, as follows, Arg73, Gly102, Asn103 for GlcNAc residue A; Asn103 for GlcNAc residues B and C; Leu56, Ala107, Val109 for GlcNAc residue D; Ala110 for GlcNAc residue E; and Lys33 for GlcNAc residue F. We also indicated that GlcNAc residue F does not interact with Thr47 and rarely interacts with Phe34 and Asn37.

Acetylglucosamine↗

PubMed2006

Properties of a trifluoroleucine-resistant mutant of Saccharomyces cerevisiae.

We characterized a trifluoroleucine-resistant mutant of Saccharomyces cerevisiae, TFL20, that has a mutation in the LEU4 gene. We monitored the concentration of extracellular i-AmOH and intracellular amino acids, and compared the ratios of gene expression in TFL20 with the wild-type strain, K30. We found that the LEU1, LEU2, and BAT1 genes were up-regulated in TFL20 for metabolism, and that TFL20 simultaneously produced as much i-AmOH and leucine as K30 does.

2-Isopropylmalate Synthase↗

PubMed2005

Detection and normalization of biases present in spotted cDNA microarray data: a composite method addressing dye, intensity-dependent, spatially-dependent, and print-order biases.

Microarrays are often used to identify target genes that trigger specific diseases, to elucidate the mechanisms of drug effects, and to check SNPs. However, data from microarray experiments are well known to contain biases resulting from the experimental protocols. Therefore, in order to elucidate biological knowledge from the data, systematic biases arising from their protocols must be removed prior to any data analysis. To remove these biases, many normalization methods are used by researchers. However, not all biases are eliminated from the microarray data because not all types of errors from experimental protocols are known. In this paper, we report an effective way of removing various types of biases by treating each microarray dataset independently to detect biases present in the dataset. After the biases contained in each dataset were identified, a combination of normalization methods specifically made for each dataset was applied to remove biases one at a time.

Algorithms↗

PubMed2003

Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries.

Gene regulatory networks elucidated from strategic, genome-wide experimental data can aid in the discovery of novel gene function information and expression regulation events from observation of transcriptional regulation among genes of known and unknown biological function. To create a reliable and comprehensive data set for the elucidation of transcription regulation networks, we conducted systematic genome-wide disruption expression experiments of yeast on 118 genes with known involvement in transcription regulation. We report several novel regulatory relationships between known transcription factors and other genes with previously unknown biological function discovered with this expression library. Here we report the downstream regulatory subnetworks for UME6 and MET28. The elucidated network topology among these genes demonstrates MET28's role as a nodal point between genes involved in cell division and those involved in DNA repair mechanisms.

Algorithms↗

PubMed2003

Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades.

We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains, each with a single-gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent griseofulvin, whose exact molecular targets were previously unknown, we used Boolean and Bayesian network discovery techniques to determine the gene expression regulatory cascades affected directly by this drug. Using this method we identified CIK1 as an important affected target gene related to the functional phenotype induced by griseofulvin. Cellular functional analysis of griseofulvin showed similar tubulin-specific morphological effects on mitotic spindle formation to those of the drug, in agreement with the known function of CIK1p. Further, using the nonparametric, nonlinear Bayesian gene networks we were able to identify alternative ligand-dependant transcription factors and G protein homologues upstream of CIK1 that regulate CIK1 expression and might therefore serve as alternative molecular targets to induce the same molecular response as griseofulvin.

Bayes Theorem↗

PubMed2002

[Drug discovery based on microarray].

Gene regulatory networks developed from full genome expression libraries from gene perturbation variant cell lines can be used to quickly and efficiently identify the molecular mechanism of action of drugs or lead compound molecules. We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains each with a single gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent, we used Boolean network discovery techniques to determine the genes whose expression was most profoundly affected by this drug. Our system identified the gene as the most significantly suppressed target molecule due to exposure to the antifungal agent. This process for network based drug discovery can significantly decrease the time and resources necessary to make rational drug targeting decisions.

Antifungal Agents↗