Graphene-Wrapped Sulfur Particles as a Rechargeable Lithium-Sulfur-Battery Cathode Material with High Capacity and Cycling Stability

We report the synthesis of a graphene-sulfur composite material by wrapping polyethyleneglycol (PEG) coated submicron sulfur particles with mildly oxidized graphene oxide sheets decorated by carbon black nanoparticles. The PEG and graphene coating layers are important to accommodating volume expansion of the coated sulfur particles during discharge, trapping soluble polysulfide intermediates and rendering the sulfur particles electrically conducting. The resulting graphene-sulfur composite showed high and stable specific capacities up to ~600mAh/g over more than 100 cycles, representing a promising cathode material for rechargeable lithium batteries with high energy density.Comment: published in Nano Letter

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed