The Problems of Civil Law in China and Japan

千葉大学大学院人文社会科学研究科研究プロジェクト報告書第171集『中日における民法現代化の課題』 小賀野晶一

A Negative Cooperativity Mechanism of Human CYP2E1 Inferred from Molecular Dynamics Simulations and Free Energy Calculations

Human cytochrome P450 2E1 (CYP2E1) participates in the metabolism of over 2% of all the oral drugs. A hallmark peculiar feature of this enzyme is that it exhibits a pronounced negative cooperativity in substrate binding. However the mechanism by which the negative cooperativity occurs is unclear. Here, we performed molecular dynamics simulations and free energy calculations on human CYP2E1 to examine the structural differences between the substrate-free and the enzymes with one and two aniline molecules bound. Our results indicate that although the effector substrate does not bind in the active site cavity, it still can directly interact with the active site residues of human CYP2E1. The interaction of the effector substrate with the active site leads to a reorientation of active site residues, which thereby weakens the interactions of the active substrate with this site. We also identify a conserved residue T303 that plays a crucial role in the negative cooperative binding on the short-range effects. This residue is a key factor in the positioning of substrates and in proton delivery to the active site. Additionally, a long-range effect of the effector substrate is identified in which F478 is proposed to play a key role. As located in the interface between the active and effector sites, this residue structurally links the active and effector sites and is found to play a significant role in affecting substrate access and ligand positioning within the active site. In the negative cooperative binding, this residue can decrease the interactions of the active substrate with the active site by π–π stacking which then lowers the hydroxylation activity for the active substrate. These findings are in agreement with previous experimental observations and thus provide detailed atomistic insight into the poorly understood mechanism of the negative cooperativity in human CYP2E1

Additional file 2: of Differential networking meta-analysis of gastric cancer across Asian and American racial groups

Table S1. DRN meta-analysis of gastric cancer, https://www.synapse.org/#!Synapse:syn10154163 (XLSX 98 kb

Additional file 1: of Differential networking meta-analysis of gastric cancer across Asian and American racial groups

Supplementary Figure and legends. Figure S1. Quality assessment of our data set (GSE54129). Figure S2. Expression level distribution of genes in three datasets. Figure S3. The differential networking meta-analysis framework. Figure S4. The GIF expression in different patient groups based on Neoplasm Histologic Grade. (DOCX 1497 kb

20/9/25, San Sebastian, Grand prix tourisme : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 104416Appartient à l’ensemble documentaire : Pho20RolImage de press

Thlaspi japonicum Boiss.

原著和名: タカネグンバイ科名: アブラナ科 = Cruciferae採集地: 北海道 夕張岳 (北海道 石狩 夕張岳)採集日: 1957/8/1採集者: 萩庭丈壽整理番号: JH900235国立科学博物館整理番号: TNS-VS-99413

Additional file 9: Figure S4. of A novel integrated gene coexpression analysis approach reveals a prognostic three-transcription-factor signature for glioma molecular subtypes

Comparison of 3-TF signature subtypes and morphologically defined glioma subtypes in Tiantan data. (TIF 2631 kb

Additional file 1: Figure S1. of A novel integrated gene coexpression analysis approach reveals a prognostic three-transcription-factor signature for glioma molecular subtypes

Cophenetic correlation coefficients of nonnegative matrix factorization (NMF) clustering method with 88 seed signatures. (TIF 6064 kb

Additional file 17: Figure S9. of A novel integrated gene coexpression analysis approach reveals a prognostic three-transcription-factor signature for glioma molecular subtypes

The p-value distribution of survival analysis of clustering results by three-random-gene signatures. (TIF 995 kb

Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data-3

<p><b>Copyright information:</b></p><p>Taken from "Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/194</p><p>BMC Bioinformatics 2007;8():194-194.</p><p>Published online 11 Jun 2007</p><p>PMCID:PMC1913542.</p><p></p>tions of PCC values for PPI pairs, GGI pairs and random pairs. The two arrows indicate the regions where expression correlations were strengthened by the PPI PCC calculation. (B) Scatter plot of PCC values of PPI pairs vs. those of corresponding GGI pairs. X-axis indicates the PCC values of PPI pairs, and y-axis indicates the PCC values of the corresponding GGI pairs. A linear regression resulted in a regression line and a regression formula, which were shown in green. Totals within each quadrants were marked with black numbers at the four corners