A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network.

BACKGROUND: The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. RESULTS: Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. CONCLUSION: We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

Role of CD8 in aberrant function of cytotoxic T lymphocytes.

Using H-2Kd-restricted photoprobe-specific cytotoxic T lymphocyte (CTL) clones, which permit assessment of T cell receptor (TCR)-ligand interactions by TCR photoaffinity labeling, we observed that the efficiency of antigen recognition by CTL was critically dependent on the half-life of TCR-ligand complexes. We show here that antigen recognition by CTL is essentially determined by the frequency of serial TCR engagement, except for very rapid dissociations, which resulted in aberrant TCR signaling and antagonism. Thus agonists that were efficiently recognized exhibited rapid TCR-ligand complex dissociation, and hence a high frequency of serial TCR engagement, whereas the opposite was true for weak agonists. Surprisingly, these differences were largely accounted for by the coreceptor CD8. While it was known that CD8 substantially decreases TCR-ligand complex dissociation, we observed in this study that this effect varied considerably among ligand variants, indicating that epitope modifications can alter the CD8 contribution to TCR-ligand binding, and hence the efficiency of antigen recognition by CTL.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

EADock : design of a new molecular docking algorithm and some of its applications

3 Summary 3. 1 English The pharmaceutical industry has been facing several challenges during the last years, and the optimization of their drug discovery pipeline is believed to be the only viable solution. High-throughput techniques do participate actively to this optimization, especially when complemented by computational approaches aiming at rationalizing the enormous amount of information that they can produce. In siiico techniques, such as virtual screening or rational drug design, are now routinely used to guide drug discovery. Both heavily rely on the prediction of the molecular interaction (docking) occurring between drug-like molecules and a therapeutically relevant target. Several softwares are available to this end, but despite the very promising picture drawn in most benchmarks, they still hold several hidden weaknesses. As pointed out in several recent reviews, the docking problem is far from being solved, and there is now a need for methods able to identify binding modes with a high accuracy, which is essential to reliably compute the binding free energy of the ligand. This quantity is directly linked to its affinity and can be related to its biological activity. Accurate docking algorithms are thus critical for both the discovery and the rational optimization of new drugs. In this thesis, a new docking software aiming at this goal is presented, EADock. It uses a hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with .the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 R around the center of mass of the ligand position in the crystal structure, and conversely to other benchmarks, our algorithms was fed with optimized ligand positions up to 10 A root mean square deviation 2MSD) from the crystal structure. This validation illustrates the efficiency of our sampling heuristic, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best-ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures in this benchmark could be explained by the presence of crystal contacts in the experimental structure. EADock has been used to understand molecular interactions involved in the regulation of the Na,K ATPase, and in the activation of the nuclear hormone peroxisome proliferatoractivated receptors a (PPARa). It also helped to understand the action of common pollutants (phthalates) on PPARy, and the impact of biotransformations of the anticancer drug Imatinib (Gleevec®) on its binding mode to the Bcr-Abl tyrosine kinase. Finally, a fragment-based rational drug design approach using EADock was developed, and led to the successful design of new peptidic ligands for the a5ß1 integrin, and for the human PPARa. In both cases, the designed peptides presented activities comparable to that of well-established ligands such as the anticancer drug Cilengitide and Wy14,643, respectively. 3.2 French Les récentes difficultés de l'industrie pharmaceutique ne semblent pouvoir se résoudre que par l'optimisation de leur processus de développement de médicaments. Cette dernière implique de plus en plus. de techniques dites "haut-débit", particulièrement efficaces lorsqu'elles sont couplées aux outils informatiques permettant de gérer la masse de données produite. Désormais, les approches in silico telles que le criblage virtuel ou la conception rationnelle de nouvelles molécules sont utilisées couramment. Toutes deux reposent sur la capacité à prédire les détails de l'interaction moléculaire entre une molécule ressemblant à un principe actif (PA) et une protéine cible ayant un intérêt thérapeutique. Les comparatifs de logiciels s'attaquant à cette prédiction sont flatteurs, mais plusieurs problèmes subsistent. La littérature récente tend à remettre en cause leur fiabilité, affirmant l'émergence .d'un besoin pour des approches plus précises du mode d'interaction. Cette précision est essentielle au calcul de l'énergie libre de liaison, qui est directement liée à l'affinité du PA potentiel pour la protéine cible, et indirectement liée à son activité biologique. Une prédiction précise est d'une importance toute particulière pour la découverte et l'optimisation de nouvelles molécules actives. Cette thèse présente un nouveau logiciel, EADock, mettant en avant une telle précision. Cet algorithme évolutionnaire hybride utilise deux pressions de sélections, combinées à une gestion de la diversité sophistiquée. EADock repose sur CHARMM pour les calculs d'énergie et la gestion des coordonnées atomiques. Sa validation a été effectuée sur 37 complexes protéine-ligand cristallisés, incluant 11 protéines différentes. L'espace de recherche a été étendu à une sphère de 151 de rayon autour du centre de masse du ligand cristallisé, et contrairement aux comparatifs habituels, l'algorithme est parti de solutions optimisées présentant un RMSD jusqu'à 10 R par rapport à la structure cristalline. Cette validation a permis de mettre en évidence l'efficacité de notre heuristique de recherche car des modes d'interactions présentant un RMSD inférieur à 2 R par rapport à la structure cristalline ont été classés premier pour 68% des complexes. Lorsque les cinq meilleures solutions sont prises en compte, le taux de succès grimpe à 78%, et 92% lorsque la totalité de la dernière génération est prise en compte. La plupart des erreurs de prédiction sont imputables à la présence de contacts cristallins. Depuis, EADock a été utilisé pour comprendre les mécanismes moléculaires impliqués dans la régulation de la Na,K ATPase et dans l'activation du peroxisome proliferatoractivated receptor a (PPARa). Il a également permis de décrire l'interaction de polluants couramment rencontrés sur PPARy, ainsi que l'influence de la métabolisation de l'Imatinib (PA anticancéreux) sur la fixation à la kinase Bcr-Abl. Une approche basée sur la prédiction des interactions de fragments moléculaires avec protéine cible est également proposée. Elle a permis la découverte de nouveaux ligands peptidiques de PPARa et de l'intégrine a5ß1. Dans les deux cas, l'activité de ces nouveaux peptides est comparable à celles de ligands bien établis, comme le Wy14,643 pour le premier, et le Cilengitide (PA anticancéreux) pour la seconde.

Design and analysis of ChIP-Seq experiments for nuclear factor I DNA-binding proteins

SUMMARY : Eukaryotic DNA interacts with the nuclear proteins using non-covalent ionic interactions. Proteins can recognize specific nucleotide sequences based on the sterical interactions with the DNA and these specific protein-DNA interactions are the basis for many nuclear processes, e.g. gene transcription, chromosomal replication, and recombination. New technology termed ChIP-Seq has been recently developed for the analysis of protein-DNA interactions on a whole genome scale and it is based on immunoprecipitation of chromatin and high-throughput DNA sequencing procedure. ChIP-Seq is a novel technique with a great potential to replace older techniques for mapping of protein-DNA interactions. In this thesis, we bring some new insights into the ChIP-Seq data analysis. First, we point out to some common and so far unknown artifacts of the method. Sequence tag distribution in the genome does not follow uniform distribution and we have found extreme hot-spots of tag accumulation over specific loci in the human and mouse genomes. These artifactual sequence tags accumulations will create false peaks in every ChIP-Seq dataset and we propose different filtering methods to reduce the number of false positives. Next, we propose random sampling as a powerful analytical tool in the ChIP-Seq data analysis that could be used to infer biological knowledge from the massive ChIP-Seq datasets. We created unbiased random sampling algorithm and we used this methodology to reveal some of the important biological properties of Nuclear Factor I DNA binding proteins. Finally, by analyzing the ChIP-Seq data in detail, we revealed that Nuclear Factor I transcription factors mainly act as activators of transcription, and that they are associated with specific chromatin modifications that are markers of open chromatin. We speculate that NFI factors only interact with the DNA wrapped around the nucleosome. We also found multiple loci that indicate possible chromatin barrier activity of NFI proteins, which could suggest the use of NFI binding sequences as chromatin insulators in biotechnology applications. RESUME : L'ADN des eucaryotes interagit avec les protéines nucléaires par des interactions noncovalentes ioniques. Les protéines peuvent reconnaître les séquences nucléotidiques spécifiques basées sur l'interaction stérique avec l'ADN, et des interactions spécifiques contrôlent de nombreux processus nucléaire, p.ex. transcription du gène, la réplication chromosomique, et la recombinaison. Une nouvelle technologie appelée ChIP-Seq a été récemment développée pour l'analyse des interactions protéine-ADN à l'échelle du génome entier et cette approche est basée sur l'immuno-précipitation de la chromatine et sur la procédure de séquençage de l'ADN à haut débit. La nouvelle approche ChIP-Seq a donc un fort potentiel pour remplacer les anciennes techniques de cartographie des interactions protéine-ADN. Dans cette thèse, nous apportons de nouvelles perspectives dans l'analyse des données ChIP-Seq. Tout d'abord, nous avons identifié des artefacts très communs associés à cette méthode qui étaient jusqu'à présent insoupçonnés. La distribution des séquences dans le génome ne suit pas une distribution uniforme et nous avons constaté des positions extrêmes d'accumulation de séquence à des régions spécifiques, des génomes humains et de la souris. Ces accumulations des séquences artéfactuelles créera de faux pics dans toutes les données ChIP-Seq, et nous proposons différentes méthodes de filtrage pour réduire le nombre de faux positifs. Ensuite, nous proposons un nouvel échantillonnage aléatoire comme un outil puissant d'analyse des données ChIP-Seq, ce qui pourraient augmenter l'acquisition de connaissances biologiques à partir des données ChIP-Seq. Nous avons créé un algorithme d'échantillonnage aléatoire et nous avons utilisé cette méthode pour révéler certaines des propriétés biologiques importantes de protéines liant à l'ADN nommés Facteur Nucléaire I (NFI). Enfin, en analysant en détail les données de ChIP-Seq pour la famille de facteurs de transcription nommés Facteur Nucléaire I, nous avons révélé que ces protéines agissent principalement comme des activateurs de transcription, et qu'elles sont associées à des modifications de la chromatine spécifiques qui sont des marqueurs de la chromatine ouverte. Nous pensons que lés facteurs NFI interagir uniquement avec l'ADN enroulé autour du nucléosome. Nous avons également constaté plusieurs régions génomiques qui indiquent une éventuelle activité de barrière chromatinienne des protéines NFI, ce qui pourrait suggérer l'utilisation de séquences de liaison NFI comme séquences isolatrices dans des applications de la biotechnologie.

MIMAS 3.0 is a Multiomics Information Management and Annotation System.

BACKGROUND: DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types. DESCRIPTION: MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure. CONCLUSION: We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via http://multiomics.sourceforge.net/.

Selective absence of CD8+ TCRalpha beta+ intestinal epithelial cells in transgenic mice expressing beta2-microglobulin-associated ligands exclusively on thymic cortical epithelium.

Whereas interactions between the TCRalpha beta and self MHC:peptide complexes are clearly required for positive selection of mature CD4(+) and CD8(+) T cells during intrathymic development, the role of self or foreign ligands in maintaining the peripheral T cell repertoire is still controversial. In this report we have utilized keratin 14-beta2-microglobulin (K14-beta2m)-transgenic mice expressing beta2m-associated ligands exclusively on thymic cortical epithelial cells to address the possible influence of TCR:ligand interactions in peripheral CD8(+) T cell homeostasis. Our data indicate that CD8(+) T cells in peripheral lymphoid tissues are present in normal numbers in the absence of self MHC class I:peptide ligands. Surprisingly, however, steady state homeostasis of CD8(+) T cells in the intestinal epithelium is severely affected by the absence of beta2m-associated ligands. Indeed TCRalpha beta(+) IEL subsets expressing CD8alpha beta or CD8alpha alpha are both dramatically reduced in K14-beta2m mice, suggesting that the development, survival or expansion of CD8(+) IEL depends upon interaction of the TCR with MHC class I:peptide or other beta2m-associated ligands elsewhere than on thymic cortical epithelium. Collectively, our data reveal an unexpected difference in the regulation of CD8(+) T cell homeostasis by beta2m-associated ligands in the intestine as compared to peripheral lymphoid organs.

Inhibitory receptor-mediated regulation of natural killer cells.

Natural killer (NK) cells are capable of directly recognizing pathogens, pathogen-infected cells, and transformed cells. NK cells recognize target cells using approximately 100 germ-line encoded receptors, which display activating or inhibitory function. NK cell activation usually requires the engagement of more than one receptor, and these may contribute distinct signaling inputs that are required for the firm adhesion of NK cells to target cells, polarization, and the release of cytotoxic granules, as well as the production of cytokines. In this article we discuss receptor-mediated mechanisms that counteract NK cell activation. The distinct intracellular inhibitory signaling pathways and how they can dominantly interfere with NK cell activation signaling events are discussed first. In addition, mechanisms by which inhibitory receptors modulate cellular activation at the level of receptor-ligand interactions are described. Receptor-mediated inhibition of NK cell function serves three main purposes: ensuring tolerance of NK cells to normal cells, enabling NK cell responses to aberrant host cells that have lost an inhibitory ligand, and, finally, allowing the recognition of certain pathogens that do not express inhibitory ligands.

The efficiency of antigen recognition by CD8+ CTL clones is determined by the frequency of serial TCR engagement.

Using H-2Kd-restricted CTL clones, which are specific for a photoreactive derivative of the Plasmodium berghei circumsporozoite peptide PbCS(252-260) (SYIPSAEKI) and permit assessment of TCR-ligand interactions by TCR photoaffinity labeling, we have previously identified several peptide derivative variants for which TCR-ligand binding and the efficiency of Ag recognition deviated by fivefold or more. Here we report that the functional CTL response (cytotoxicity and IFN-gamma production) correlated with the rate of TCR-ligand complex dissociation, but not the avidity of TCR-ligand binding. While peptide antagonists exhibited very rapid TCR-ligand complex dissociation, slightly slower dissociation was observed for strong agonists. Conversely and surprisingly, weak agonists typically displayed slower dissociation than the wild-type agonists. Acceleration of TCR-ligand complex dissociation by blocking CD8 participation in TCR-ligand binding increased the efficiency of Ag recognition in cases where dissociation was slow. In addition, permanent TCR engagement by TCR-ligand photocross-linking completely abolished sustained intracellular calcium mobilization, which is required for T cell activation. These results indicate that the functional CTL response depends on the frequency of serial TCR engagement, which, in turn, is determined by the rate of TCR-ligand complex dissociation.

Genetic Variations and Diseases in UniProtKB/Swiss-Prot: The Ins and Outs of Expert Manual Curation.

During the last few years, next-generation sequencing (NGS) technologies have accelerated the detection of genetic variants resulting in the rapid discovery of new disease-associated genes. However, the wealth of variation data made available by NGS alone is not sufficient to understand the mechanisms underlying disease pathogenesis and manifestation. Multidisciplinary approaches combining sequence and clinical data with prior biological knowledge are needed to unravel the role of genetic variants in human health and disease. In this context, it is crucial that these data are linked, organized, and made readily available through reliable online resources. The Swiss-Prot section of the Universal Protein Knowledgebase (UniProtKB/Swiss-Prot) provides the scientific community with a collection of information on protein functions, interactions, biological pathways, as well as human genetic diseases and variants, all manually reviewed by experts. In this article, we present an overview of the information content of UniProtKB/Swiss-Prot to show how this knowledgebase can support researchers in the elucidation of the mechanisms leading from a molecular defect to a disease phenotype.

Proof of concept for microarray-based detection of DNA-binding oncogenes in cell extracts.

The function of DNA-binding proteins is controlled not just by their abundance, but mainly at the level of their activity in terms of their interactions with DNA and protein targets. Moreover, the affinity of such transcription factors to their target sequences is often controlled by co-factors and/or modifications that are not easily assessed from biological samples. Here, we describe a scalable method for monitoring protein-DNA interactions on a microarray surface. This approach was designed to determine the DNA-binding activity of proteins in crude cell extracts, complementing conventional expression profiling arrays. Enzymatic labeling of DNA enables direct normalization of the protein binding to the microarray, allowing the estimation of relative binding affinities. Using DNA sequences covering a range of affinities, we show that the new microarray-based method yields binding strength estimates similar to low-throughput gel mobility-shift assays. The microarray is also of high sensitivity, as it allows the detection of a rare DNA-binding protein from breast cancer cells, the human tumor suppressor AP-2. This approach thus mediates precise and robust assessment of the activity of DNA-binding proteins and takes present DNA-binding assays to a high throughput level.

Probing the interactions of NK cell receptors with ligand expressed in trans and cis.

Certain receptors on natural killer (NK) cells, which are specific for MHC class I (MHC-I) molecules, do not only interact with ligand expressed on opposing cell membranes (in trans) but also interact with those on the same cell membrane (in cis). Cis interactions have been demonstrated for only a small number of cell surface receptors. However, this has not been tested systematically, raising the possibility that additional receptors may be able to bind ligand expressed in cis. Here we describe a number of approaches to evaluate trans and cis binding of the Ly49A NK cell receptor to its H-2D(d) ligand. These procedures should facilitate the investigation of cis/trans interactions of other receptor-ligand pairs and simplify the analysis of NK cell receptor variants.

Interactions of tumor necrosis factor (TNF) and TNF receptor family members in the mouse and human.

Ligands of the tumor necrosis factor superfamily (TNFSF) (4-1BBL, APRIL, BAFF, CD27L, CD30L, CD40L, EDA1, EDA2, FasL, GITRL, LIGHT, lymphotoxin alpha, lymphotoxin alphabeta, OX40L, RANKL, TL1A, TNF, TWEAK, and TRAIL) bind members of the TNF receptor superfamily (TNFRSF). A comprehensive survey of ligand-receptor interactions was performed using a flow cytometry-based assay. All ligands engaged between one and five receptors, whereas most receptors only bound one to three ligands. The receptors DR6, RELT, TROY, NGFR, and mouse TNFRH3 did not interact with any of the known TNFSF ligands, suggesting that they either bind other types of ligands, function in a ligand-independent manner, or bind ligands that remain to be identified. The study revealed that ligand-receptor pairs are either cross-reactive between human and mouse (e.g. Tweak/Fn14, RANK/RANKL), strictly species-specific (GITR/GITRL), or partially species-specific (e.g. OX40/OX40L, CD40/CD40L). Interestingly, the receptor binding patterns of lymphotoxin alpha and alphabeta are redundant in the human but not in the mouse system. Ligand oligomerization allowed detection of weak interactions, such as that of human TNF with mouse TNFR2. In addition, mouse APRIL exists as two different splice variants differing by a single amino acid. Although human APRIL does not interact with BAFF-R, the shorter variant of mouse APRIL exhibits weak but detectable binding to mouse BAFF-R.

Cholesterol and sphingomyelin drive ligand-independent T-cell antigen receptor nanoclustering.

The T-cell antigen receptor (TCR) exists in monomeric and nanoclustered forms independently of antigen binding. Although the clustering is involved in the regulation of T-cell sensitivity, it is unknown how the TCR nanoclusters form. We show that cholesterol is required for TCR nanoclustering in T cells and that this clustering enhances the avidity but not the affinity of the TCR-antigen interaction. Investigating the mechanism of the nanoclustering, we found that radioactive photocholesterol specifically binds to the TCRβ chain in vivo. In order to reduce the complexity of cellular membranes, we used a synthetic biology approach and reconstituted the TCR in liposomes of defined lipid composition. Both cholesterol and sphingomyelin were required for the formation of TCR dimers in phosphatidylcholine-containing large unilamellar vesicles. Further, the TCR was localized in the liquid disordered phase in giant unilamellar vesicles. We propose a model in which cholesterol and sphingomyelin binding to the TCRβ chain causes TCR dimerization. The lipid-induced TCR nanoclustering enhances the avidity to antigen and thus might be involved in enhanced sensitivity of memory compared with naive T cells. Our work contributes to the understanding of the function of specific nonannular lipid-membrane protein interactions.

SwissDock, a protein-small molecule docking web service based on EADock DSS.

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.