ExPASy (http://www.expasy.org) has worldwide reputation as one of the main bioinformatics resources for proteomics. It has now evolved, becoming an extensible and integrative portal accessing many scientific resources, databases and software tools in different areas of life sciences. Scientists can henceforth access seamlessly a wide range of resources in many different domains, such as proteomics, genomics, phylogeny/evolution, systems biology, population genetics, transcriptomics, etc. The individual resources (databases, web-based and downloadable software tools) are hosted in a ‘decentralized’ way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions. Specifically, a single web portal provides a common entry point to a wide range of resources developed and operated by different SIB groups and external institutions. The portal features a search function across ‘selected’ resources. Additionally, the availability and usage of resources are monitored. The portal is aimed for both expert users and people who are not familiar with a specific domain in life sciences. The new web interface provides, in particular, visual guidance for newcomers to ExPASy.

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community.

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.

Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein–small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.ch.

The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of a test set of 251 complexes were reproduced within 2 Å RMSD to the crystal structure. Up to 70% were reproduced when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility.

The ability of pollutants to affect human health is a major concern, justified by the wide demonstration that reproductive functions are altered by endocrine disrupting chemicals. The definition of endocrine disruption is today extended to broader endocrine regulations, and includes activation of metabolic sensors, such as the peroxisome proliferator-activated receptors (PPARs). Toxicology approaches have demonstrated that phthalate plasticizers can directly influence PPAR activity. What is now missing is a detailed molecular understanding of the fundamental basis of endocrine disrupting chemical interference with PPAR signaling. We thus performed structural and functional analyses that demonstrate how monoethyl-hexyl-phthalate (MEHP) directly activates PPAR␥ and promotes adipogenesis, albeit to a lower extent than the full agonist rosiglitazone. Importantly, we demonstrate that MEHP induces a selective activation of different PPAR␥ target genes. Chromatin immunoprecipitation and fluorescence microscopy in living cells reveal that this selective activity correlates with the recruitment of a specific subset of PPAR␥ coregulators that includes Med1 and PGC-1␣, but not p300 and SRC-1. These results highlight some key mechanisms in metabolic disruption but are also instrumental in the context of selective PPAR modulation, a promising field for new therapeutic development based on PPAR modulation.

The binding of chemokines to glycosaminoglycans is thought to play a crucial role in chemokine functions. It has recently been shown that stromal cell-derived factor-1␣ (SDF-1␣), a CXC chemokine with potent anti-human immunodeficiency virus activity, binds to heparan sulfate through a typical consensus sequence for heparin recognition (BBXB, where B is a basic residue KHLK, amino acids 24 -27). Calculation of the accessible surface, together with the electrostatic potential of the SDF-1␣ dimer, revealed that other amino acids (Arg-41 and Lys-43) are found in the same surface area and contribute to the creation of a positively charged crevice, located at the dimer interface. GRID calculations confirmed that this binding site will be the most energetically favored area for the interaction with sulfate groups. Site-directed mutagenesis and surface plasmon resonance-based binding assays were used to investigate the structural basis for SDF-1␣ binding to heparin. Among the residues clustered in this basic surface area, Lys-24 and Lys-27 have dominant roles and are essential for interaction with heparin. Amino acids Arg-41 and Lys-43 participate in the binding but are not strictly required for the interaction to take place. Direct binding assays and competition analysis with monoclonal antibodies also permitted us to show that the N-terminal residue (Lys-1), an amino acid critical for receptor activation, is involved in complex formation. Binding studies with selectively desulfated heparin, heparin oligosaccharides, and heparitinase-resistant heparan sulfate fragments showed that a minimum size of 12-14 monosaccharide units is required for efficient binding and that 2-O-and N-sulfate groups have a dominant role in the interaction. Finally, the heparin-binding site was identified on the crystal structure of SDF-1␣, and a docking study was undertaken. During the energy minimization process, heparin lost its perfect ribbon shape and fitted the protein surface perfectly. In the model, Lys-1, Lys-24, Lys-27, and Arg-41 were found to have the major role in binding a polysaccharide fragment consisting of 13 monosaccharide units.Chemokines are small structurally related chemo-attractant cytokines, characterized by conserved cysteine residues. Almost 40 chemokines have been identified to date which, based on the position of the first N-terminal cysteines, fall into four sub-families. Two of them have been well characterized, the CC group, which includes regulated on activation, normal T-cell expressed, and secreted, monocyte chemoattractant protein-1, and MIP-1 1 (macrophage inflammatory peptides-1), and the CXC group, the prototype of which is interleukin-8. The C chemokine (lymphotactine) and the CX 3 C chemokine (fractalkine) sub-families have been identified more recently (1-4). These proteins signal through G-protein-coupled seven transmembrane domain receptors (5) and are primarily involved in immunosurveillance, activation, and recruitment of specific cell populations during disease (1, 6 -8). Most, if not all, chemokin...

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