Frog: a FRee Online druG 3D conformation generator

In silico screening methods based on the 3D structures of the ligands or of the proteins have become an essential tool to facilitate the drug discovery process. To achieve such process, the 3D structures of the small chemical compounds have to be generated. In addition, for ligand-based screening computations or hierarchical structure-based screening projects involving a rigid-body docking step, it is necessary to generate multi-conformer 3D models for each input ligand to increase the efficiency of the search. However, most academic or commercial compound collections are delivered in 1D SMILES (simplified molecular input line entry system) format or in 2D SDF (structure data file), highlighting the need for free 1D/2D to 3D structure generators. Frog is an on-line service aimed at generating 3D conformations for drug-like compounds starting from their 1D or 2D descriptions. Given the atomic constitution of the molecules and connectivity information, Frog can identify the different unambiguous isomers corresponding to each compound, and generate single or multiple low-to-medium energy 3D conformations, using an assembly process that does not presently consider ring flexibility. Tests show that Frog is able to generate bioactive conformations close to those observed in crystallographic complexes. Frog can be accessed at http://bioserv.rpbs.jussieu.fr/Frog.html

An integrated modelling framework from cells to organism based on a cohort of digital embryos

We conducted a quantitative comparison of developing sea urchin embryos based on the analysis of five digital specimens obtained by automatic processing of in toto 3D+ time image data. These measurements served the reconstruction of a prototypical cell lineage tree able to predict the spatiotemporal cellular organisation of a normal sea urchin blastula. The reconstruction was achieved by designing and tuning a multi-level probabilistic model that reproduced embryo-level dynamics from a small number of statistical parameters characterising cell proliferation, cell surface area and cell volume evolution along the cell lineage. Our resulting artificial prototype was embedded in 3D space by biomechanical agent-based modelling and simulation, which allowed a systematic exploration and optimisation of free parameters to fit the experimental data and test biological hypotheses. The spherical monolayered blastula and the spatial arrangement of its different cell types appeared tightly constrained by cell stiffness, cell-adhesion parameters and blastocoel turgor pressure

RPBS: a web resource for structural bioinformatics

RPBS (Ressource Parisienne en Bioinformatique Structurale) is a resource dedicated primarily to structural bioinformatics. It is the result of a joint effort by several teams to set up an interface that offers original and powerful methods in the field. As an illustration, we focus here on three such methods uniquely available at RPBS: AUTOMAT for sequence databank scanning, YAKUSA for structure databank scanning and WLOOP for homology loop modelling. The RPBS server can be accessed at and the specific services at

Long-term balancing selection on chromosomal variants associated with crypsis in a stick insect

How polymorphisms are maintained within populations over long periods of time remains debated, because genetic drift and various forms of selection are expected to reduce variation. Here, we study the genetic architecture and maintenance of phenotypic morphs that confer crypsis in Timema cristinae stick insects, combining phenotypic information and genotyping-by-sequencing data from 1360 samples across 21 populations. We find two highly divergent chromosomal variants that span megabases of sequence and are associated with color polymorphism. We show that these variants exhibit strongly reduced effective recombination, are geographically widespread, and probably diverged millions of generations ago. We detect heterokaryotype excess and signs of balancing selection acting on these variants through the species' history. A third chromosomal variant in the same genomic region likely evolved more recently from one of the two color variants and is associated with dorsal pattern polymorphism. Our results suggest that large-scale genetic variation associated with crypsis has been maintained for long periods of time by potentially complex processes of balancing selection

Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com

A novel IgE antibody targeting the prostate-specific antigen as a potential prostate cancer therapy

Prostate cancer (PCa) is the second leading cause of cancer deaths in men in the United States. The prostate-specific antigen (PSA), often found at high levels in the serum of PCa patients, has been used as a marker for PCa detection and as a target of immunotherapy. The murine IgG1 monoclonal antibody AR47.47, specific for human PSA, has been shown to enhance antigen presentation by human dendritic cells and induce both CD4 andCD8 T-cell activation when complexed with PSA. In this study, we explored the properties of a novel mouse/human chimeric anti-PSA IgE containing the variable regions of AR47.47 as a potential therapy for PCa. Our goal was to take advantage of the unique properties of IgE in order to trigger immune activation against PCa.Fil: Daniels-Wells, Tracy R. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América;Fil: Helguera, Gustavo Fernando. Universidad de Buenos Aires. Facultad de Farmacia y Bioquimica. Departamento de Tecnologia Farmaceutica; Argentina; University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América;Fil: Leuchter, Richard K. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América;Fil: Quintero, Rafael. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América;Fil: Kozman, Maggie. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América;Fil: Rodríguez, José A.. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América; University of California. The Molecular Biology Institute; Estados Unidos de América;Fil: Ortiz-Sánchez, E. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América; Biomedical Research in Cancer. Basic Research Division. National Institute of Cancerology; Mexico.;Fil: Martínez-Maza, Otonel. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América;Fil: Schultes, Brigit C.. Advanced Immune Therapeutics; Estados Unidos de América;Fil: Nicodemus Christopher. Advanced Immune Therapeutics; Estados Unidos de América;Fil: Penichet, Manuel. University of California. David Geffen School of Medicine. Department of Surgery. Division of Surgical Oncology; Estados Unidos de América; University of California. The Molecular Biology Institute; Estados Unidos de América

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques

Major prospects for exploring canine vector borne diseases and novel intervention methods using 'omic technologies

Canine vector-borne diseases (CVBDs) are of major socioeconomic importance worldwide. Although many studies have provided insights into CVBDs, there has been limited exploration of fundamental molecular aspects of most pathogens, their vectors, pathogen-host relationships and disease and drug resistance using advanced, 'omic technologies. The aim of the present article is to take a prospective view of the impact that next-generation, 'omics technologies could have, with an emphasis on describing the principles of transcriptomic/genomic sequencing as well as bioinformatic technologies and their implications in both fundamental and applied areas of CVBD research. Tackling key biological questions employing these technologies will provide a 'systems biology' context and could lead to radically new intervention and management strategies against CVBDs