AMMOS: A Software Platform to Assist in silico Screening

Three software packages based on the common platform of AMMOS (Automated Molecular Mechanics Optimization tool for in silico Screening) for assisting virtual ligand screening purposes have been recently developed. DG-AMMOS allows generation of 3D conformations of small molecules using distance geometry and molecular mechanics optimization. AMMOS_SmallMol is a package for structural refinement of compound collections that can be used prior to docking experiments. AMMOS_ProtLig is a package for energy minimization of protein-ligand complexes. It performs an automatic procedure for molecular mechanics minimization at different levels of flexibility - from rigid to fully flexible structures of both the ligand and the receptor. The packages have been tested on small molecules with a high structural diversity and proteins binding sites of completely different geometries and physicochemical properties. The platform is developed as an open source software and can be used in a broad range of in silico drug design studies

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

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

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

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

Molecular recognition in the protein C anticoagulant pathway.

The protein C (PC) anticoagulant system provides specific and efficient control of blood coagulation. The system comprises circulating or membrane-bound protein components that take part in complicated multimolecular protein complexes being assembled on specific cellular phospholipid membranes. Each of the participating proteins is composed of multiple domains, many of which are known at the level of their three-dimensional structures. The key component of the PC system, the vitamin K-dependent PC, circulates in blood as zymogen to an anticoagulant serine protease. Activation is achieved on the surface of endothelial cells by thrombin bound to the membrane protein thrombomodulin. The endothelial PC receptor binds the Gla domain of PC and stimulates the activation. Activated PC (APC) modulates the activity of blood coagulation by specific proteolytic cleavages of a limited number of peptide bonds in factor (F)VIIIa and FVa, cofactors in the activation of FX and prothrombin, respectively. These reactions occur on the surface of negatively charged phospholipid membranes and are stimulated by the vitamin K-dependent protein S. Regulation of FVIIIa activity by APC is stimulated not only by protein S but also by FV, which, like thrombin, is a Janus-faced protein with both pro- and anticoagulant potential. However, whereas the properties of thrombin are modulated by protein–protein interactions, the specificity of FV function is governed by proteolysis by pro- or anti-coagulant enzymes. The molecular recognition of the PC system is beginning to be unravelled and provides insights into a fascinating and intricate molecular scenario

Structural model of human alpha1-microglobulin : proposed scheme for the interaction with the Gla domain of anticoagulant protein C

Alpha1-microglobulin (alpha1m) is a small glycoprotein with immunomodulatory properties. It is a member of the lipocalin family, a group of proteins that exhibit a well-conserved three-dimensional structure despite low sequence identity and that are known to bind small hydrophobic ligands. The types of ligands carried by alpha1m are still unknown, but it is known that this protein has yellow-brown chromophores attached to three lysines at position 92, 118 and 130. Alpha1m has one unpaired cysteine residue (Cys 34) that can form a disulphide bond with other proteins that also possess an exposed free unpaired cysteine. For instance, alpha1m interacts with the protein C (PC) Gla domain containing the Arg9Cys or Ser12Cys substitution. In order to gain insights about the alpha1m molecule and analyze the intriguing alpha1m-Gla domain interaction, it was decided to use bioinformatics. The three-dimensional structures of alpha1m and PC Gla domain were predicted. Alpha1m Cys 34 is solvent exposed and located near the entrance of the ligand-binding pocket. The chromophore-carrying lysines are found buried into the pocket, and the area around the entrance of this cavity displays about 10 positively charged residues. This electropositive region in alpha1m complements the essentially electronegative Gla domain and may play a role during intermolecular interactions. In addition, a few hydrophobic residues surround alpha1m Cys 34 and could be of importance during its interaction with macromolecular ligands