A 3D nanoporous graphitic carbon (g-C) material is synthesized by using an adamantane (C(10)H(16)) flame, and utilized to support a Pt(50)-Ru(50) alloy catalyst. The physico-chemical properties of the Pt(50)-Ru(50)/3D nanoporous g-C electrode are examined by a range of spectroscopy techniques as well as Brunauer-Emmett-Teller surface area analysis. Cyclic voltammetry measurements are used for electrochemical characterization of the Pt(50)-Ru(50)/3D nanoporous g-C electrode. The electrochemical investigations show that the supported Pt(50)-Ru(50) has excellent activity and stability towards methanol electro-oxidation. Good CO tolerance is also shown, and considered to be due to the presence of Ru nanoparticles. It is proposed that Ru is able to promote the oxidation of strongly adsorbed CO on Pt by supplying an oxygen source: Ru(OH)(ad). Moreover, the presence of 3D nanopores in the g-C support may also contribute to the observed higher current density by virtue of the easy transport of methanol and the oxidation products through these nanopores.

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Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.