Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Insights into bacterial genome composition through variable target GC content profiling

This study presents a new computational method for guanine (G) and cytosine (C), or GC, content profiling based on the idea of multiple resolution sampling (MRS). The benefit of our new approach over existing techniques follows from its ability to locate significant regions without prior knowledge of the sequence, nor the features being sought. The use of MRS has provided novel insights into bacterial genome composition. Key findings include those that are related to the core composition of bacterial genomes, to the identification of large genomic islands (in Enterobacterial genomes), and to the identification of surface protein determinants in human pathogenic organisms (e.g., Staphylococcus genomes). We observed that bacterial surface binding proteins maintain abnormal GC content, potentially pointing to a viral origin. This study has demonstrated that GC content holds a high informational worth and hints at many underlying evolutionary processes. For online Supplementary Material, see www.liebertonline.com.

Ordovician nautiloid fossils of Xainza region, Tibet

The Taerma bridge-Zhakang in the Xainza area is the second spot of Ordovician in northern Tibet based on the discovery of some reliable fossils. The strata contain a large amount of fossils of many taxa, which developed well enough to attain reliable support for era and distinctive boundary. It is the best spot for the fossil study of the Ordovician in the northern Tibet up to now and provides important clues to the classification and correlation of the Ordovician and to the paleogeography distribution as well as to the tectonic evolution of the northern Tibet. 29 species of Nautiloid fossils, which belong to 3 orders, 8 families and 15 genera, have been identified. Among them,3 genera and 9 species are new. Here only one new genus Eneoceras gen. nov. and six new species are described in detail as example. Other two new genera Taremaocera's and Variabioceras will be described in other papers.Genus Eneoceras gen.nov.Diagnosis:The new genus Eneoceras gen. nov. is characterized by the features as “Conch orthoconic, medium in size. The surface is decorated with annulus which array in a distance as the septa. Conch enlarging slowly. Compressed laterally. Circular in cross section. Siphuncle small, situated central from ventral in position. Spetal neck subcyrtochoanitic. Slightly expanded in connecting rings. Siphuncle appears as a string of beads with thin parietal deposits in it. Medium in spetal density. Thin epithecal and hypothecal deposits developed in cameras

MoO3 nanoparticles dispersed uniformly in carbon matrix : a high capacity composite anode for Li-ion batteries

A MoO₃-carbon nanocomposite was synthesized from a mixture of MoO₃ and graphite by a controlled ball milling procedure. The as-prepared product consists of nanosized MoO₃ particles (2-180 nm) homogeneously distributed in carbon matrix. The nanocomposite acts as a high capacity anode material for lithium-ion batteries and exhibits good cyclic behavior. Its initial capacity exceeds the theoretical capacity of 745 mA h g^-1 in a mixture of MoO₃ and graphite (1:1 by weight), and the stable capacity of 700 mA h g^-1 (94% of the theoretical capacity) is still retained after 120 cycles. The electrode performance is linked with the unique nanoarchitecture of the composite and is compared with the performance of MoO₃-based anode materials reported in the literature previously (nanoparticles, ball milled powders, and carbon-coated nanobelts). The high value of capacity and good cyclic stability of MoO₃-carbon nanocomposite are attractive in respect to those of the reported MoO₃ electrodes.

Li-Metal symmetrical cell studies using ionic organic plastic crystal electrolyte

A low current density preconditioning process, which produces an improved lithium transport mechanism is created by the action of charge flow through a plastic crystal electrolyte (figure). A reduction in cell polarisation at high applied current density is demonstrated which approaches the rates required for these electrolytes to be used in practical devices.

Substrate-induced coagulation (SIC) of nano-disperse carbon black in non-aqueous media : a method of manufacturing highly conductive cathode materials for Li-ion batteries by self-assembly

Substrate-induced coagulation (SIC) is a coating process based on self-assembly for coating different surfaces with fine particulate materials. The particles are dispersed in a suitable solvent and the stability of the dispersion is adjusted by additives. When a surface, pre-treated with a flocculant e.g. a polyelectrolyte, is dipped into the dispersion, it induces coagulation resulting in the deposition of the particles on the surface. A non-aqueous SIC process for carbon coating is presented, which can be performed in polar, aprotic solvents such as N-Methyl-2- pyrrolidinone (NMP). Polyvinylalcohol (PVA) is used to condition the surface of substrates such as mica, copperfoil, silicon-wafers and lithiumcobalt oxide powder, a cathode material used for Li-ion batteries. The subsequent SIC carbon coating produces uniform layers on the substrates and causes the conductivity of lithiumcobalt oxide to increase drastically, while retaining a high percentage of active battery material.

Regulation of human pregnane X receptor and its target gene cytochrome P450 3A4 by Chinese herbal compounds and a molecular docking study

1. The pregnane X receptor (PXR) plays a critical role in the regulation of human cytochrome P450 3A4 (CYP3A4) gene. In this study, we investigated the effect of an array of compounds isolated from Chinese herbal medicines on the activity of PXR using a luciferase reporter gene assay in transiently transfected HepG2 and Huh7 cells and on the expression of PXR and CYP3A4 in LS174T cells. Furthermore, molecular docking was performed to investigate the binding modes of herbal compounds with PXR.

2. Praeruptorin A and C, salvianolic acid B, sodium danshensu, protocatechuic aldehyde, cryptotanshinone, emodin, morin, and tanshinone IIA significantly transactivated the CYP3A4 reporter gene construct in either HepG2 or Huh7 cells. The PXR mRNA expression in LS174T cells was significantly induced by physcion, protocatechuic aldehyde, salvianolic acid B, and sodium danshensu. However, epifriedelanol, morin, praeruptorin D, mulberroside A, tanshinone I, and tanshinone IIA significantly down-regulated the expression of PXR mRNA in LS174T cells.

3. All the herbal compounds tested can be readily docked into the ligand-binding cavity of PXR mainly through hydrogen bond and aromatic interactions with Ser247, Gln285, His407, and Arg401.

4. These findings suggest that herbal medicines can significantly regulate PXR and CYP3A4 and this has important implication in herb–drug interactions.

Triethyl and tributyl phosphite as flame-retarding additives in Li-ion batteries

This study examined the influence of triethyl and tributyl phosphite (TEP and TBP) additives on the electrochemical performance of lithium-ion cells. The cell performance of the TEP- and TBP-containing electrolytes was evaluated by cyclic voltammetry, thermogravimetric analysis, electrochemical impedance spectroscopy, Fourier transform infrared spectroscopy and scanning electron microscopy. The flammability of the electrolytes was also investigated by measuring the self-extinguishing time of the electrolytes. The results showed that the TEP and TBP additives suppressed the flammability of the electrolyte, with a significant improvement in cell performance observed for the TEP additive. In addition, TEP and TBP additives improved the thermal stability of the battery and its electrochemical cell performance. Overall, 5 wt% TEP and TBP can be used as a flame-retarding additive to improve the cell performance of Li-ion batteries due to the decrease in cell impedance and SEI formation.

Effect of flame-retarding additives on surface chemistry in Li-ion batteries

This study examined the properties of 1 wt.% vinylene carbonate, vinyl ethylene carbonate, and diphenyloctyl phosphate additive electrolytes as a promising way of beneficially improving the surface and cell resistance of Li-ion batteries. The additive electrolytes were dominant both in surface formation and internal resistance. In particular, electrochemical impedance spectroscopy, Fourier transform infrared spectroscopy and scanning electron microscopy confirmed that diphenyloctyl phosphate is an excellent additive to the electrolyte in the Li-ion batteries due to the improved co-intercalation of the solvent molecules.