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Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their

We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/- 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligands binding poses through the explicit reconstruction of the ligand-protein conformation spaces. Machine Learning predictions are also performed to predict selected compound properties. Computations were performed on GPU-accelerated supercomputers and high performance cloud infrastructures to exponentially reduce time-to-solution and were systematically coupled to Nuclear Magnetic Resonance experiments to drive synthesis and in vitro characterization of compounds. Such study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows to address the protein conformational multiplicity problem. The proposed innovative scaffolds open routes for further optimization of Mpro inhibitors towards low nM affinities.

Endocrine disrupting chemicals (EDCs) are compounds able to penetrate the body and to interfere with the functions of the endocrine system. EDCs are considered as a public health threat since human exposure to these compounds have been associated with increased risk of several diseases. It has been shown that EDCs can act through direct binding to nuclear receptors (NR) which leads to either inhibition or overactivation of the hormonal activity. Early detection of potential EDCs becomes an imperative as it is a guarantee of safety for several fields including pharmaceutical, food industry and agriculture. Several health and environmental authorities have been investigating suspicious compounds through experimental testing. However, this remains a challenging task due to the considerable number of compounds to be evaluated. In silico methods can be used in complement to prioritize compounds for experimental testing.In this work, we propose a pipeline combining structure-based (SB) and ligand-based (LB) models to predict potential EDCs based on their ability to bind six nuclear receptors: AR, ERα, ERβ, GR, PPARγ and TRα. The pipeline output enables to categorize query compounds into “high”, “intermediate”, “medium” and “low” risk of being NRs binding compounds and thus, accordingly to the direct mechanism, potential EDCs. To build the pipeline, data was collected from the EPA Comptox dashboard gathering structurally diverse compounds experimentally tested in multiple endpoints against several protein receptors. The dashboard was filtered to only keep the compounds tested in binding assays against each studied NR leading to six individual datasets. Each one of these datasets was then employed to build docking, SB and LB pharmacophores models. Each model was optimized, and their combinations have been assessed to select the protocol associated with the best performances for each receptor. The best performances among the six studied NRs were obtained with the ERβ data set for which the combination of docking and pharmacophore models reached high sensitivity, specificity and accuracy values (0.8, 0.6 and 0.65 respectively)

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (M

Motivation The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. Availabilityand implementation All data are available online at http://datasetmachat.drugdesign.fr. Supplementary information Supplementary data are available at Bioinformatics online.

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted within a selective process to achieve sufficient phase-space sampling. Accurate statistical properties can be obtained through reweighting. Within this highly parallel setup, the Tinker-HP package can be powered by an arbitrary large number of GPUs on supercomputers, reducing exploration time from years to days. This approach is used to tackle the urgent modeling problem of the SARS-CoV-2 Main Protease (Mpro) producing more than 38 μs of all-atom simulations of its apo (ligand-free) dimer using the high-resolution AMOEBA PFF. The first 15.14 μs simulation (physiological pH) is compared to available non-PFF long-timescale simulation data. A detailed clustering analysis exhibits striking differences between FFs, with AMOEBA showing a richer conformational space. Focusing on key structural markers related to the oxyanion hole stability, we observe an asymmetry between protomers. One of them appears less structured resembling the experimentally inactive monomer for which a 6 μs simulation was performed as a basis for comparison. Results highlight the plasticity of the Mpro active site. The C-terminal end of its less structured protomer is shown to oscillate between several states, being able to interact with the other protomer, potentially modulating its activity. Active and distal site volumes are found to be larger in the most active protomer within our AMOEBA simulations compared to non-PFFs as additional cryptic pockets are uncovered. A second 17 μs AMOEBA simulation is performed with protonated His172 residues mimicking lower pH. Data show the protonation impact on the destructuring of the oxyanion loop. We finally analyze the solvation patterns around key histidine residues. The confined AMOEBA polarizable water molecules are able to explore a wide range of dipole moments, going beyond bulk values, leading to a water molecule count consistent with experimental data. Results suggest that the use of PFFs could be critical in drug discovery to accurately model the complexity of the molecular interactions structuring Mpro., We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

The estrogen receptors α (ERα) are transcription factors involved in several physiological processes belonging to the nuclear receptors (NRs) protein family. Besides the endogenous ligands, several other chemicals are able to bind to those receptors. Among them are endocrine disrupting chemicals (EDCs) that can trigger toxicological pathways. Many studies have focused on predicting EDCs based on their ability to bind NRs, mainly, estrogen receptors (ER), thyroid hormones receptors (TR), androgen receptors (AR), glucocorticoid receptors (GR), and peroxisome proliferator-activated receptors gamma (PPARγ). In this work, we suggest a pipeline designed for the prediction of ERα binding activity. The flagged compounds can be further explored using experimental techniques to assess their potential to be EDCs. The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0.81 and 0.54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.

Background Despite the improvement of relapse-free survival mediated by anti-angiogenic drugs like sunitinib (Sutent®), or by combinations of anti-angiogenic drugs with immunotherapy, metastatic clear cell Renal Cell Carcinoma (mccRCC) remain incurable. Hence, new relevant treatments are urgently needed. The VEGFs coreceptors, Neuropilins 1, 2 (NRP1, 2) are expressed on several tumor cells including ccRCC. We analyzed the role of the VEGFs/NRPs signaling in ccRCC aggressiveness and evaluated the relevance to target this pathway. Methods We correlated the NRP1, 2 levels to patients’ survival using online available data base. Human and mouse ccRCC cells were knocked-out for the NRP1 and NRP2 genes by a CRISPR/Cas9 method. The number of metabolically active cells was evaluated by XTT assays. Migration ability was determined by wound closure experiments and invasion ability by using Boyden chamber coated with collagen. Production of VEGFA and VEGFC was evaluated by ELISA. Experimental ccRCC were generated in immuno-competent/deficient mice. The effects of a competitive inhibitor of NRP1, 2, NRPa-308, was tested in vitro and in vivo with the above-mentioned tests and on experimental ccRCC. NRPa-308 docking was performed on both NRPs. Results Knock-out of the NRP1 and NRP2 genes inhibited cell metabolism and migration and stimulated the expression of VEGFA or VEGFC, respectively. NRPa-308 presented a higher affinity for NRP2 than for NRP1. It decreased cell metabolism and migration/invasion more efficiently than sunitinib and the commercially available NRP inhibitor EG00229. NRPa-308 presented a robust inhibition of experimental ccRCC growth in immunocompetent and immunodeficient mice. Such inhibition was associated with decreased expression of several pro-tumoral factors. Analysis of the TCGA database showed that the NRP2 pathway, more than the NRP1 pathway correlates with tumor aggressiveness only in metastatic patients. Conclusions Our study strongly suggests that inhibiting NRPs is a relevant treatment for mccRCC patients in therapeutic impasses and NRPa-308 represents a relevant hit.

Prior to any docking study, the virtual compounds database that will be screened must be carefully selected and prepared. This compound collection, often referred to as the virtual library, can encompass up to millions of compounds. Already prepared virtual libraries can be used, but users can also generate their own. In this chapter, we will present the different categories of virtual screening libraries together with guidelines for their preparation prior to docking. We will present the different categories of compound collections and we will distinguish actual and virtual compounds databases. We will then describe the key steps for virtual library preparation by detailing the database cleaning and discussing the use of ADME/Tox filters.

[#17491] article suite à une conférence orale: 13th EG Euroworkshop on 3D object retrieval, 3DOR 2020, Graz, Austria, september 4-5, 2020; International audience; Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the protein and species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost. (C) 2020 The Author(s). Published by Elsevier Ltd.

Neuropilin-1 (NRP-1) is an extra-cellular receptor for the main Vascular Endothelial Growth Factor over-expressed in tumour tissues, VEGF-A165. Consequently, NRP-1 is involved in angiogenesis and in tumour growth, and its over-expression is related to a clinical poor prognosis. NRP-1 appears as a major target in oncology, which remains poorly exploited. Herein, we report a new series of 18 small-sized fully organic VEGF-A165/NRP-1 antagonists (NRPas). These compounds share an original scaffold, including two linkers (sulphonamide and amide) and three aromatic cores. Among them, 2a (renamed NRPa-308) emerges as a promising “hit”. In vitro, 2a exerts not only potent anti-angiogenic activity, but also significant effects on cell viability of large panel of human solid and haematological cancer cell lines. Importantly, 2a is less cytotoxic on healthy tissues than the marketed anti-angiogenic drug sunitinib. Lastly, in a mouse xenograft model (human MDA-MB-231 breast cancer cells), 2a improves the median survival and reduces the tumour growth, but does not exert visible acute toxicity. Altogether, these results highlight its huge potential for a further “hit-to-lead” optimization, leading to new anti-cancer drugs.

The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

International audience; The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.

This track aimed at retrieving protein evolutionary classification based on their surfaces meshes only. Given that proteins are dynamic, non-rigid objects and that evolution tends to conserve patterns related to their activity and function, this track offers a challenging issue using biologically relevant molecules. We evaluated the performance of 5 different algorithms and analyzed their ability, over a dataset of 5,298 objects, to retrieve various conformations of identical proteins and various conformations of ortholog proteins (proteins from different organisms and showing the same activity). All methods were able to retrieve a member of the same class as the query in at least 94% of the cases when considering the first match, but show more divergent when more matches were considered. Last, similarity metrics trained on databases dedicated to proteins improved the results., Eurographics Workshop on 3D Object Retrieval, SHREC Session 1, 25, 31, Florent Langenfeld, Apostolos Axenopoulos, Halim Benhabiles, Petros Daras, Andrea Giachetti, Xusi Han, Karim Hammoudi, Daisuke Kihara, Tuan M. Lai, Haiguang Liu, Mahmoud Melkemi, Stelios K. Mylonas, Genki Terashi, Yufan Wang, Feryal Windal, and Matthieu Montes

Screening Explorer is a web-based application that allows for an intuitive evaluation of the results of screening experiments using complementary metrics in the field. The usual evaluation of screening results implies the separate generation and apprehension of the ROC, predictiveness, and enrichment curves and their global metrics. Similarly, partial metrics need to be calculated repeatedly for different fractions of a data set and there exists no handy tool that allows reading partial metrics simultaneously on different charts. For a deeper understanding of the results of screening experiments, we rendered their analysis straightforward by linking these metrics interactively in an interactive usable web-based application. We also implemented simple consensus scoring methods based on scores normalization, standardization (z-scores), and compounds ranking to evaluate the enrichments that can be expected through methods combination. Two demonstration data sets allow the users to easily apprehend the functions of this tool that can be applied to the analysis of virtual and experimental screening results. Screening Explorer is freely accessible at http://stats.drugdesign.fr .

International audience; In this study, a surface acoustic wave (SAW) biosensor has been investigated to quantify the affinity and to estimate the binding kinetic between tumor necrosis factor α (TNF-α) and its inhibitor: SPD-304. To the best of our knowledge this is first ever report on real time and label free monitoring of TNF-α/ SPD-304 affinity. Effects of the SPD-304 solvent and DMSO cosolvent on TNFα/SPD-304 interaction have been investigated. Gravimetric results indicate that the limit of detection (LOD) of the developed biosensor was of order of 10 nM and that DMSO cosolvent influences the kinetic of interaction, the saturation value, and thus the value of the dissociation constant (Kd) of the TNF-α / SPD-304 system.

Nuclear receptors (NRs) are transcription factors that regulate gene expression in various physiological processes through their interactions with small hydrophobic molecules. They constitute an important class of targets for drugs and endocrine disruptors and are widely studied for both health and environment concerns. Since the integration of negative data can be critical for accurate modeling of ligand activity profiles, we manually collected and annotated NRs interaction data (positive and negative) through a sharp review of the corresponding literature. 15 116 positive and negative interactions data are provided for 28 NRs together with 593 PDB structures in the freely available Nuclear Receptors Database Including Negative Data ( http://nr-dbind.drugdesign.fr ). The NR-DBIND contains the most extensive information about interaction data on NRs, which should bring valuable information to chemists, biologists, pharmacologists and toxicologists.

International audience; In this work, a serie of cyclocoumarol derivatives was designed, synthesized, characterized and studied for their potentialities as selective inhibitors of COX-2. All target compounds have been screened for their anti-inflammatory activity by the assay of PGE2 production. Among them, compound 5d exhibited the most potent inhibitory activity with a PGE2 inhibition compared to NS-398 (79% and 88% respectively) and showed non-inhibitory activity towards the COX-1 enzyme. Docking studies revealed the capacity of this compound to occupy the selective COX-2 cavity establishing additional hydrogen bonds between the oxygen of the methoxy group and the His90 and Arg513 of the binding site of the enzyme.

We are currently developing a bio-shape similarity system for supplying high-throughput protein shape similarity applications within massive datasets. The proposed system is powered by a global-to-local shape similarity system which exploits shape elevation and local convexity attributes. In the first step, a global similarity is computed between the shape descriptors associated to each protein input. The procedure outputs best N similarities chosen by the user, within a query-to-cluster approach. The second stage is a patch-based local similarity computation method which is designed to find the best similar target from the cluster for supplying query-to-target protein retrieval applications. The local patch-based similarity comparison benefits of a multi-CPU implementation, offering thus fast query search capabilities within massive datasets. Experimental results on the SHREC 2017 BioShape dataset composed of 5484 models, illustrate the effectiveness of the proposed system.

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

TNFα is a homotrimeric pro-inflammatory cytokine, whose direct targeting by protein biotherapies has been an undeniable success for the treatment of chronic inflammatory diseases. Despite many efforts, no orally active drug targeting TNFα has been identified so far. In the present work, we identified through combined in silico/in vitro/in vivo approaches a TNFα direct inhibitor, compound 1, displaying nanomolar and micromolar range bindings to TNFα. Compound 1 inhibits the binding of TNFα with both its receptors TNFRI and TNFRII. Compound 1 inhibits the TNFα induced apoptosis on L929 cells and the TNFα induced NF-κB activation in HEK cells. In vivo, oral administration of compound 1 displays a significant protection in a murine TNFα-dependent hepatic shock model. This work illustrates the ability of low-cost combined in silico/in vitro/in vivo screening approaches to identify orally available small-molecules targeting challenging protein-protein interactions such as homotrimeric TNFα.

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

International audience; Inflammation is a complex phenomenon necessary in human defense mechanisms but also involved in the development of some human diseases. The discovery of cyclooxygenase-2 (COX-2) improved the pharmacology of nonsteroidal anti-inflammatory drugs (NSAID) giving a clear mechanism for prostaglandin regulation in vivo and providing a new target for the development of COX-2-selective drugs without gastrointestinal side-effects. Keeping in view the importance of this pharmacological class, several literature reports have underlined the impact of these antiinflammatory compounds in therapeutics. The present review considers the most recently published literature concerning COX-2 inhibitors until 2016. Through a wide chemical classification, the last developments concerning this therapeutic family by highlighting structure-activity relationships insights and mechanisms are presented. A summary of the principal adverse effects observed and an overview of the new potential therapeutic indications for COX-2 inhibitors are also reported.

Efforts aimed at restoring robust immune responses limiting human immunodeficiency virus (HIV)-1 replication therapeutically are warranted. We report that vaccination with dendritic cells generated ex vivo and loaded with HIV lipopeptides in patients (n = 19) on antiretroviral therapy was well tolerated and immunogenic. Vaccination increased: (i) the breadth of the immune response from 1 (1-3) to 4 (2-5) peptide-pool responses/patient (p = 0.009); (ii) the frequency of functional T cells (producing at least two cytokines among IFN-γ, TNF-α, and IL-2) from 0.026 to 0.32% (p = 0.002) and from 0.26 to 0.35% (p = 0.005) for CD4(+) and CD8(+) T cells, respectively; and (iii) the breadth of cytokines secreted by PBMCs upon antigen exposure, including IL-2, IFN-γ, IL-21, IL-17, and IL-13. Fifty percent of patients experienced a maximum of viral load (VL) 1 log10 lower than the other half following antiretroviral treatment interruption. An inverse correlation was found between the maximum of VL and the frequency of polyfunctional CD4(+) T cells (p = 0.007), production of IL-2 (p = 0.006), IFN-γ (p = 0.01), IL-21 (p = 0.006), and IL-13 (p = 0.001). These results suggest an association between vaccine responses and a better control of viral replication. These findings will help in the development of strategies for a functional cure for HIV infection.

Neuropilin-1/-2 (+33 NRPs), VEGF-A 165 co-receptors , are over-expressed during cancer progression. Thus, NRPs targeted drug development is challenged using a multistep in silico/in vitro screening procedure. The first fully non-peptidic VEGF-A 165 /NRPs protein–protein interaction antagonist (IC 50 = 34 μM) without effect on pro-angiogenic kinases has been identified (compound-1). This hit showed breast cancer cells anti-proliferative activity (IC 50 = 0.60 μM). Compound-1 treated NOG-xenografted mice significantly exerted tumor growth inhibition, which is correlated with Ki-67 low expression and apoptosis. Furthermore, CD31 + /CD34 + vessels are reduced in accordance with HUVEC-tube formation inhibition (IC 50 = 0.20 μM). Taking together, compound-1 is the first fully organic inhibitor targeting NRPs.

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A(3), the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These "binding site properties-based" guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available.

Interleukin-6 (IL-6) overproduction has been involved in the pathogenesis of several chronic inflammatory diseases and the administration of an anti-IL-6 receptor monoclonal antibody has been proven clinically efficient to treat them. However, the drawbacks of monoclonal antibodies have led our group to develop an innovative anti-IL-6 strategy using a peptide-based active immunization. This approach has previously shown its efficacy in a mouse model of systemic sclerosis. Here the safety, immunogenicity and efficacy of this strategy was assessed in non human primates. No unscheduled death and clinical signs of toxicity was observed during the study. Furthermore, the cynomolgus monkeys immunized against the IL-6 peptide produced high levels of anti-IL-6 antibodies as well as neutralizing antibodies compared to control groups. They also showed an important decrease of the cumulative inflammatory score following a delayed-type hypersensitivity reaction induced by the Tetanus vaccine compared to control groups (minus 57,9%, P = 0.014). These findings are highly significant because the immunizing IL-6 peptide used in this study is identical in humans and in monkeys and this novel anti-IL-6 strategy could thus represent a promising alternative to monoclonal antibodies.

In the past few years, several genomewide association studies (GWASs) have been conducted to identify host genetic variants involved in control of human immunodeficiency virus type 1 (HIV-1) load and in progression to AIDS [1–10]. Overall, these GWASs emphasized the major role of the HLA chromosome 6 region, particularly the HCP5/HLA-B*57 rs2395029 signal [1–3, 5, 8], and the CXCR6 gene region [7]. These GWASs focused on viral load and disease progression, but genetic correlates of the HIV-1 acquisition phenotype have met limited success: 2 recent GWASs conducted in Malawi reported no significant determinants of HIV infection [11, 12]. Looking for new host factors correlated to HIV susceptibility is critical because the only validated association to date is the 32 base-pair deletion in the CCR5 gene: only 1%–2% of Europeans are homozygous for this mutation and exhibit a near-complete protection against infection by HIV-1 R5 strains [13, 14]. To identify additional genetic factors that might contribute to HIV-1 acquisition, we performed a meta-analysis using GWAS genotypic data from 2 European AIDS progression cohorts, comparing each group of HIV-1–infected patients with uninfected controls of the same ancestry [8, 15]. Next, we replicated the association for the single-nucleotide polymorphism (SNP) showing the smallest P value in the European meta-analysis on 2 independent US cohorts of European ancestry.

Background: Seven genome-wide association studies (GWAS) have been published in AIDS, and only associations in the HLA region on chromosome 6 and CXCR6 have passed genome-wide significance. Methods: We reanalyzed the data from 3 previously published GWAS, targeting specifically low-frequency SNPs (minor allele frequency

a Background. The compilation of previous genomewide association studies of AIDS shows a major polymor- phism in the HCP5 gene associated with both control of the viral load and long-term nonprogression (LTNP) to AIDS. Methods. To look for genetic variants that affect LTNP without necessary control of the viral load, we reanalyzed the genomewide data of the unique LTNP Genomics of Resistance to Immunodeficiency Virus (GRIV) cohort by excluding "elite controller" patients, who were controlling the viral load at very low levels (!100 copies/mL). Results. The rs2234358 polymorphism in the CXCR6 gene was the strongest signal ( ; odds ratio, 7 P p 2.5 10 1.85) obtained for the genomewide association study comparing the 186 GRIV LTNPs who were not elite controllers with 697 uninfected control subjects. This association was replicated in 3 additional independent European studies, reaching genomewide significance of . This association with LTNP is independent of the 10 P p 9.7 10 combined CCR2-CCR5 locus and the HCP5 polymorphisms. Conclusions. The statistical significance, the replication, and the magnitude of the association demonstrate that CXCR6 is likely involved in the molecular etiology of AIDS and, in particular, in LTNP, emphasizing the power of extreme-phenotype cohorts. CXCR6 is a chemokine receptor that is known as a minor coreceptor in human immunodeficiency virus type 1 infection but could participate in disease progression through its role as a mediator of inflammation.

Cytochrome P4507B1 7alpha-hydroxylates dehydroepiandrosterone (DHEA), epiandrosterone (EpiA) and 5alpha-androstane-3beta,17beta-diol (Adiol). 11beta-Hydroxysteroid dehydrogenase type 1 (11beta-HSD1) interconverts 7alpha- and 7beta-forms. Whether the interconversion proceeds through oxido-reductive steps or epimerase activity was investigated. Experiments using [(3)H]-labelled 7beta-hydroxy-DHEA, 7beta-hydroxy-EpiA and 7beta-hydroxy-Adiol showed the (3)H-label to accumulate in the 7-oxo-DHEA trap but not in 7-oxo-EpiA or 7-oxo-Adiol traps. Computed models of 7-oxygenated steroids docked in the active site of 11beta-HSD1 either in a flipped or turned form relative to cortisone and cortisol. 7-Oxo-steroid reduction in 7alpha- or 7beta-hydroxylated derivatives resulted from either turned or flipped forms. 11beta-HSD1 incubation in H(2)(18)O medium with each 7-hydroxysteroid did not incorporate (18)O in 7-hydroxylated derivatives of EpiA and Adiol independently of the cofactor used. Thus oxido-reductive steps apply for the interconversion of 7alpha- and 7beta-hydroxy-DHEA through 7-oxo-DHEA. Epimerization may proceed on the 7-hydroxylated derivatives of EpiA and Adiol through a mechanism involving the cofactor and Ser(170). The physiopathological importance of this epimerization process is related to 7beta-hydroxy-EpiA production and its effects in triggering the resolution of inflammation.

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

In the present work, we describe and discuss two interactive visual­ ization techniques recently added to the Udock software. First, we propose to display a spherical projection of the protein's molecular surface properties. The resulting 2D map allows to get in one glance a global view of the protein. Second, we propose to let the user choose a specific level of detail when visualizing the protein's 3D surface. We describe a simple smoothing algorithm that achieves this goal in real time. These techniques are designed to enhance the usability of molecular visualization and interactive simulation software.

Background In the present work, we aim to transfer to the field of virtual screening the predictiveness curve, a metric that has been advocated in clinical epidemiology. The literature describes the use of predictiveness curves to evaluate the performances of biological markers to formulate diagnoses, prognoses and assess disease risks, assess the fit of risk models, and estimate the clinical utility of a model when applied to a population. Similarly, we use logistic regression models to calculate activity probabilities related to the scores that the compounds obtained in virtual screening experiments. The predictiveness curve can provide an intuitive and graphical tool to compare the predictive power of virtual screening methods. Results Similarly to ROC curves, predictiveness curves are functions of the distribution of the scores and provide a common scale for the evaluation of virtual screening methods. Contrarily to ROC curves, the dispersion of the scores is well described by predictiveness curves. This property allows the quantification of the predictive performance of virtual screening methods on a fraction of a given molecular dataset and makes the predictiveness curve an efficient tool to address the early recognition problem. To this last end, we introduce the use of the total gain and partial total gain to quantify recognition and early recognition of active compounds attributed to the variations of the scores obtained with virtual screening methods. Additionally to its usefulness in the evaluation of virtual screening methods, predictiveness curves can be used to define optimal score thresholds for the selection of compounds to be tested experimentally in a drug discovery program. We illustrate the use of predictiveness curves as a complement to ROC on the results of a virtual screening of the Directory of Useful Decoys datasets using three different methods (Surflex-dock, ICM, Autodock Vina). Conclusion The predictiveness curves cover different aspects of the predictive power of the scores, allowing a detailed evaluation of the performance of virtual screening methods. We believe predictiveness curves efficiently complete the set of tools available for the analysis of virtual screening results. Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0100-8) contains supplementary material, which is available to authorized users.

Background Many genome-wide association studies have been performed on progression towards the acquired immune deficiency syndrome (AIDS) and they mainly identified associations within the HLA loci. In this study, we demonstrate that the integration of biological information, namely gene expression data, can enhance the sensitivity of genetic studies to unravel new genetic associations relevant to AIDS. Methods We collated the biological information compiled from three databases of expression quantitative trait loci (eQTLs) involved in cells of the immune system. We derived a list of single nucleotide polymorphisms (SNPs) that are functional in that they correlate with differential expression of genes in at least two of the databases. We tested the association of those SNPs with AIDS progression in two cohorts, GRIV and ACS. Tests on permuted phenotypes of the GRIV and ACS cohorts or on randomised sets of equivalent SNPs allowed us to assess the statistical robustness of this method and to estimate the true positive rate. Results Eight genes were identified with high confidence (p = 0.001, rate of true positives 75%). Some of those genes had previously been linked with HIV infection. Notably, ENTPD4 belongs to the same family as CD39, whose expression has already been associated with AIDS progression; while DNAJB12 is part of the HSP90 pathway, which is involved in the control of HIV latency. Our study also drew our attention to lesser-known functions such as mitochondrial ribosomal proteins and a zinc finger protein, ZFP57, which could be central to the effectiveness of HIV infection. Interestingly, for six out of those eight genes, down-regulation is associated with non-progression, which makes them appealing targets to develop drugs against HIV.

A protocol was devised in which FRED, DOCK, and Surflex were combined in a multistep virtual ligand screening (VLS) procedure to screen the pocket of four different proteins. One goal was to evaluate the impact of chaining "freely available packages to academic users" on docking/scoring accuracy and CPU time consumption. A bank of 65 660 compounds including 49 known actives was generated. Our procedure is successful because docking/scoring parameters are tuned according to the nature of the binding pocket and because a shape-based filtering tool is applied prior to flexible docking. The obtained enrichment factors are in line with those reported in recent studies. We suggest that consensus docking/scoring could be valuable to some drug discovery projects. The present protocol could process the entire bank for one receptor in less than a week on one processor, suggesting that VLS experiments could be performed even without large computer resources.

The evaluation of virtual ligand screening methods is of major importance to ensure their reliability. Taking into account the agonist/antagonist pharmacological profile should improve the quality of the benchmarking data sets since ligand binding can induce conformational changes in the nuclear receptor structure and such changes may vary according to the agonist/antagonist ligand profile. We indeed found that splitting the agonist and antagonist ligands into two separate data sets for a given nuclear receptor target significantly enhances the quality of the evaluation. The pharmacological profile of the ligand bound in the binding site of the target structure was also found to be an additional critical parameter. We also illustrate that active compound data sets for a given pharmacological activity can be used as a set of experimentally validated decoy ligands for another pharmacological activity to ensure a reliable and challenging evaluation of virtual screening methods.

Neuropilins (NRPs) are VEGF-A 165 co-receptors over-expressed in tumor cells, and considered as targets in angiogenic-related pathologies. We previously identified compound 1 , the first non-peptidic antagonist of the VEGF-A 165 /NRP binding, which exhibits in vivo anti-angiogenic and anti-tumor activities. We report here the synthesis and biological evaluations of new antagonists structurally-related to compound 1 . Among these molecules, 4a , 4c and 4d show cytotoxic effects on HUVEC and MDA-MB-31 cells, and antagonize VEGF-A 165 /NRP-1 binding. This study confirmed our key structure–activity relationships hypothesis and paved the way to compound 1 ‘hit to lead’ optimization.

Past genome-wide association studies (GWAS) involving individuals with AIDS have mainly identified associations in the HLA region. Using the latest software, we imputed 7 million single-nucleotide polymorphisms (SNPs)/indels of the 1000 Genomes Project from the GWAS-determined genotypes of individuals in the Genomics of Resistance to Immunodeficiency Virus AIDS nonprogression cohort and compared them with those of control cohorts. The strongest signals were in MICA, the gene encoding major histocompatibility class I polypeptide-related sequence A (P = 3.31 × 10(-12)), with a particular exonic deletion (P = 1.59 × 10(-8)) in full linkage disequilibrium with the reference HCP5 rs2395029 SNP. Haplotype analysis also revealed an additive effect between HLA-C, HLA-B, and MICA variants. These data suggest a role for MICA in progression and elite control of human immunodeficiency virus type 1 infection.

Nuclear receptors (NRs) constitute an important class of drug targets. We created the most exhaustive NR-focused benchmarking database to date, the NRLiSt BDB (NRs ligands and structures benchmarking database). The 9905 compounds and 339 structures of the NRLiSt BDB are ready for structure-based and ligand-based virtual screening. In the present study, we detail the protocol used to generate the NRLiSt BDB and its features. We also give some examples of the errors that we found in ChEMBL that convinced us to manually review all original papers. Since extensive and manually curated experimental data about NR ligands and structures are provided in the NRLiSt BDB, it should become a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understanding of NR's function and modulation, and to support the discovery of new drugs targeting NRs. NRLiSt BDB is freely available online at http://nrlist.drugdesign.fr .

International audience; INTRODUCTION: Interleukin-6 (IL-6) is a pleiotropic cytokine for which preliminary data have suggested that it might contribute to systemic sclerosis (SSc). Our aims were to investigate, firstly, IL-6 expression in patients with SSc and, secondly, the efficacy of both passive and active immunization against IL-6 to reduce skin fibrosis in complementary mouse models of SSc. METHODS: Human serum levels and skin expression of IL-6 were determined by enzyme-linked immunosorbent assay and immunohistochemistry, respectively. We first evaluated the antifibrotic properties of the monoclonal anti-IL-6R antibody, MR16-1, in the bleomycin-induced dermal fibrosis mouse model, reflecting early and inflammatory stages of SSc. Then, we assessed the efficacy of MR16-1 in tight skin-1 (Tsk-1) mice, an inflammation-independent model of skin fibrosis. Additionally, we have developed an innovative strategy using an anti-IL-6 peptide-based active immunization. Infiltrating leukocytes, T cells, and B cells were quantified, and IL-6 levels were measured in the serum and lesional skin of mice after passive or active immunization. RESULTS: Serum and skin levels of IL-6 were significantly increased in patients with early SSc. Treatment with MR16-1 led in the bleomycin mouse model to a 25% (P = 0.02) and 30% (P = 0.007) reduction of dermal thickness and hydroxyproline content, respectively. MR16-1 demonstrated no efficacy in Tsk-1 mice. Thereafter, mice were immunized against a small peptide derived from murine IL-6 and this strategy led in the bleomycin model to a 20% (P = 0.02) and 25% (P = 0.005) decrease of dermal thickness and hydroxyproline content, respectively. Passive and active immunization led to decreased T-cell infiltration in the lesional skin of mice challenged with bleomycin. Upon bleomycin injections, serum and skin IL-6 levels were increased after treatment with MR16-1 and were significantly reduced after anti-IL-6 active immunization. CONCLUSIONS: Our results support the relevance of targeting IL-6 in patients with early SSc since IL-6 is overexpressed in early stages of the disease. Targeting IL-6 by both passive and active immunization strategies prevented the development of bleomycin-induced dermal fibrosis in mice. Our results highlight the therapeutic potential of active immunization against IL-6, which is a seductive alternative to passive immunization.