Identification of Pharmacokinetic Markers for Guanxin Danshen Drop Pills in Rats by Combination of Pharmacokinetics, Systems Pharmacology, and Pharmacodynamic Assays

This paper reported a feasibility study strategy of identifying pharmacokinetic (PK) markers for a cardiovascular herbal medicine, Guanxin Danshen drop pill (GDDP). First, quantification analysis revealed the constituent composition in the preparation by high-performance liquid chromatography (HPLC). Subsequently, physiochemical property calculation predicted the solubility and intestinal permeability of the constituents in the preparation. Furthermore, HPLC–MS analysis ascertained the absorbable ingredients and their PK properties in rat plasma. The main effective substances from the ingredients absorbed into blood and their cardiovascular effects were also predicted by systems pharmacology study, and were further confirmed by in vivo protective effects on isoprenaline-induced myocardial injury in mice. Finally, the ingredients with high content, representative structure feature, favorable PK properties, high relevant degree to myocardial ischemia (MI) issues, and validated therapeutic effects were considered as the PK markers for the preparation. Ginsenosides Rg1, Rb1, and tanshinone (TS) IIA were identified originally as PK markers for representing PK properties of GDDP. In addition, integrated PK studies were carried out according to previous reports, viz. drug concentration sum method and the AUC weighting method, to understand the in vivo process of GDDP comprehensively. The present study maybe provide a reference approach to identify PK markers for cardiovascular herbal medicines

タンパク質複合体および相互作用の情報解析手法

京都大学0048新制・課程博士博士(情報学)甲第19109号情博第555号新制||情||98(附属図書館)32060京都大学大学院情報学研究科知能情報学専攻(主査)教授 阿久津 達也, 教授 山本 章博, 教授 鹿島 久嗣学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Proteome compression via protein domain compositions.

In this paper, we study domain compositions of proteins via compression of whole proteins in an organism for the sake of obtaining the entropy that the individual contains. We suppose that a protein is a multiset of domains. Since gene duplication and fusion have occurred through evolutionary processes, the same domains and the same compositions of domains appear in multiple proteins, which enables us to compress a proteome by using references to proteins for duplicated and fused proteins. Such a network with references to at most two proteins is modeled as a directed hypergraph. We propose a heuristic approach by combining the Edmonds algorithm and an integer linear programming, and apply our procedure to 14 proteomes of Dictyostelium discoideum, Escherichia coli, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Oryza sativa, Danio rerio, Xenopus laevis, Gallus gallus, Mus musculus, Pan troglodytes, and Homo sapiens. The compressed size using both of duplication and fusion was smaller than that using only duplication, which suggests the importance of fusion events in evolution of a proteome

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

[Background]Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods. [Results]We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes. [Conclusions]We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes

Characterization of Flax Water Retting of Different Durations in Laboratory Condition and Evaluation of Its Fiber Properties

Water retting is a traditional retting method that enables the low-cost production of bast fibers. This study investigated the retting of flax straws by distilled water for three different durations at room temperature in laboratory condition. The retting quality was evaluated in terms of the weight loss and degumming rate together with the fiber properties, which included color, linear density, and tensile properties. The degumming rate was defined as the percentage change in pectin content of phloem regions from the raw flax to water-retted flax. It was found that the dissolution of pectin and other contaminating materials during the beginning retting stage must have played an important role in pectin (content) and weight loss besides pectin degradation, and water retting gradually improved both the apparel properties, such as whiteness and fineness, and the mechanical properties of the fibers. Given the results, a water retting duration of six days should be sufficient to provide sound retting efficiency and reasonable fiber properties

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel

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Result on the average precision, recall, and F-measure with varying in the best case using features (F1–7).

<p>Result on the average precision, recall, and F-measure with varying in the best case using features (F1–7).</p

Result on the average precision, recall, and F-measure with varying in the best case using features (F1–7).

<p>Result on the average precision, recall, and F-measure with varying in the best case using features (F1–7).</p