Insights into the molecular basis and mechanism of heme-triggered TLR4 signalling: The role of heme-binding motifs in TLR4 and MD2.

Haemolytic disorders, such as sickle cell disease, are accompanied by the release of high amounts of labile heme into the intravascular compartment resulting in the induction of proinflammatory and prothrombotic complications in affected patients. In addition to the relevance of heme-regulated proteins from the complement and blood coagulation systems, activation of the TLR4 signalling pathway by heme was ascribed a crucial role in the progression of these pathological processes. Heme binding to the TLR4-MD2 complex has been proposed recently, however, essential mechanistic information of the processes at the molecular level, such as heme-binding kinetics, the heme-binding capacity and the respective heme-binding sites (HBMs) is still missing. We report the interaction of TLR4, MD2 and the TLR4-MD2 complex with heme and the consequences thereof by employing biochemical, spectroscopic, bioinformatic and physiologically relevant approaches. Heme binding occurs transiently through interaction with up to four HBMs in TLR4, two HBMs in MD2 and at least four HBMs in their complex. Functional studies highlight that mutations of individual HBMs in TLR4 preserve full receptor activation by heme, suggesting that heme interacts with TLR4 through different binding sites independently of MD2. Furthermore, we confirm and extend the major role of TLR4 for heme-mediated cytokine responses in human immune cells.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9.

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 µM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 µM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 µM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Role of Conformational Dynamics of Sulfotransferases SULT1A1 and SULT1A3 in Substrate Specificity.

Sulfotransferases (SULTs) are phase II metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3'-Phosphoadenosine 5'-Phosphosulfate (PAPS) to a wide variety of endogenous compounds, drugs and natural products. Although SULT1A1 and SULT1A3 share 93% identity, SULT1A1, the most abundant SULT isoform in humans, exhibits a broad substrate range with specificity for small phenolic compounds, while SULT1A3 displays a high affinity toward monoamine neurotransmitters like dopamine. To elucidate the factors determining the substrate specificity of the SULT1 isoenzymes, we studied the dynamic behavior and structural specificities of SULT1A1 and SULT1A3 by using molecular dynamics (MD) simulations and ensemble docking of common and specific substrates of the two isoforms. Our results demonstrated that while SULT1A1 exhibits a relatively rigid structure by showing lower conformational flexibility except for the lip (loop L1), the loop L2 and the cap (L3) of SULT1A3 are extremely flexible. We identified protein residues strongly involved in the recognition of different substrates for the two isoforms. Our analyses indicated that being more specific and highly flexible, the structure of SULT1A3 has particularities in the binding site, which are crucial for its substrate selectivity.

Anti-Factor B Antibodies and Acute Postinfectious GN in Children.

BACKGROUND: The pathophysiology of the leading cause of pediatric acute nephritis, acute postinfectious GN, including mechanisms of the pathognomonic transient complement activation, remains uncertain. It shares clinicopathologic features with C3 glomerulopathy, a complement-mediated glomerulopathy that, unlike acute postinfectious GN, has a poor prognosis. METHODS: This retrospective study investigated mechanisms of complement activation in 34 children with acute postinfectious GN and low C3 level at onset. We screened a panel of anticomplement protein autoantibodies, carried out related functional characterization, and compared results with those of 60 children from the National French Registry who had C3 glomerulopathy and persistent hypocomplementemia. RESULTS: All children with acute postinfectious GN had activation of the alternative pathway of the complement system. At onset, autoantibodies targeting factor B (a component of the alternative pathway C3 convertase) were found in a significantly higher proportion of children with the disorder versus children with hypocomplementemic C3 glomerulopathy (31 of 34 [91%] versus 4 of 28 [14%], respectively). In acute postinfectious GN, anti-factor B autoantibodies were transient and correlated with plasma C3 and soluble C5b-9 levels. We demonstrated that anti-factor B antibodies enhance alternative pathway convertase activity in vitro, confirming their pathogenic effect. We also identified crucial antibody binding sites on factor B, including one correlated to disease severity. CONCLUSIONS: These findings elucidate the pathophysiologic mechanisms underlying acute postinfectious GN by identifying anti-factor B autoantibodies as contributing factors in alternative complement pathway activation. At onset of a nephritic syndrome with low C3 level, screening for anti-factor B antibodies might help guide indications for kidney biopsy to avoid misdiagnosed chronic glomerulopathy, such as C3 glomerulopathy, and to help determine therapy.

Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases.

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

AMMOS2: a web server for protein-ligand-water complexes refinement via molecular mechanics.

AMMOS2 is an interactive web server for efficient computational refinement of protein-small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein-ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein-ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein-ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein-ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php.

A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments.

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery.

MOTIVATION: Identification of small molecules that could be interesting starting points for drug discovery or to investigate a biological system as in chemical biology endeavours is both time consuming and costly. In silico approaches that assist the design of quality compound collections or help to prioritize molecules before synthesis or purchase are therefore valuable. Here quality refers to the selection of molecules that pass one or several selected filters that can be tuned by the users according to the project and the stage of the project. These filters can involve prediction of physicochemical properties, search for toxicophores or other unwanted chemical groups. RESULTS: FAF-Drugs4 is a novel version of our online server dedicated to the preparation and annotation of compound collections. The tool is now faster and several parameters have been optimized. In addition, a new service referred to as FAF-QED, an implementation of the quantitative estimate of drug-likeness method, is now available. AVAILABILITY AND IMPLEMENTATION: The server is available at http://fafdrugs4.mti.univ-paris-diderot.fr. CONTACT: Bruno.Villoutreix@inserm.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iPPI-DB: an online database of modulators of protein-protein interactions.

In order to boost the identification of low-molecular-weight drugs on protein-protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein-protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.

Structural Dynamics of DPP-4 and Its Influence on the Projection of Bioactive Ligands.

Dipeptidyl peptidase-4 (DPP-4) is a target to treat type II diabetes mellitus. Therefore, it is important to understand the structural aspects of this enzyme and its interaction with drug candidates. This study involved molecular dynamics simulations, normal mode analysis, binding site detection and analysis of molecular interactions to understand the protein dynamics. We identified some DPP-4 functional motions contributing to the exposure of the binding sites and twist movements revealing how the two enzyme chains are interconnected in their bioactive form, which are defined as chains A (residues 40-767) and B (residues 40-767). By understanding the enzyme structure, its motions and the regions of its binding sites, it will be possible to contribute to the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

Insights into the interaction of high potency inhibitor IRC-083864 with phosphatase CDC25.

CDC25 phosphatases play a crucial role in cell cycle regulation. They have been found to be over-expressed in various human tumours and to be valuable targets for cancer treatment. Here, we report the first model of binding of the most potent CDC25 inhibitor to date, the bis-quinone IRC-083864, into CDC25B obtained by combining molecular modeling and NMR studies. Our study provides new insights into key interactions of the catalytic site inhibitor and CDC25B in the absence of any available experimental structure of CDC25 with a bound catalytic site inhibitor. The docking model reveals that IRC-083864 occupies both the active site and the inhibitor binding pocket of the CDC25B catalytic domain. NMR saturation transfer difference and WaterLOGSY data indicate the binding zones of the inhibitor and support the docking model. Probing interactions of analogues of the two quinone units of IRC-083864 with CDC25B demonstrate that IRC-083864 competes with each monomer. Proteins 2017; 85:593-601. © 2016 Wiley Periodicals, Inc.

FAF-Drugs3: a web server for compound property calculation and chemical library design.

Drug attrition late in preclinical or clinical development is a serious economic problem in the field of drug discovery. These problems can be linked, in part, to the quality of the compound collections used during the hit generation stage and to the selection of compounds undergoing optimization. Here, we present FAF-Drugs3, a web server that can be used for drug discovery and chemical biology projects to help in preparing compound libraries and to assist decision-making during the hit selection/lead optimization phase. Since it was first described in 2006, FAF-Drugs has been significantly modified. The tool now applies an enhanced structure curation procedure, can filter or analyze molecules with user-defined or eight predefined physicochemical filters as well as with several simple ADMET (absorption, distribution, metabolism, excretion and toxicity) rules. In addition, compounds can be filtered using an updated list of 154 hand-curated structural alerts while Pan Assay Interference compounds (PAINS) and other, generally unwanted groups are also investigated. FAF-Drugs3 offers access to user-friendly html result pages and the possibility to download all computed data. The server requires as input an SDF file of the compounds; it is open to all users and can be accessed without registration at http://fafdrugs3.mti.univ-paris-diderot.fr.

MTiOpenScreen: a web server for structure-based virtual screening.

Open screening endeavors play and will play a key role to facilitate the identification of new bioactive compounds in order to foster innovation and to improve the effectiveness of chemical biology and drug discovery processes. In this line, we developed the new web server MTiOpenScreen dedicated to small molecule docking and virtual screening. It includes two services, MTiAutoDock and MTiOpenScreen, allowing performing docking into a user-defined binding site or blind docking using AutoDock 4.2 and automated virtual screening with AutoDock Vina. MTiOpenScreen provides valuable starting collections for screening, two in-house prepared drug-like chemical libraries containing 150 000 PubChem compounds: the Diverse-lib containing diverse molecules and the iPPI-lib enriched in molecules likely to inhibit protein-protein interactions. In addition, MTiOpenScreen offers users the possibility to screen up to 5000 small molecules selected outside our two libraries. The predicted binding poses and energies of up to 1000 top ranked ligands can be downloaded. In this way, MTiOpenScreen enables researchers to apply virtual screening using different chemical libraries on traditional or more challenging protein targets such as protein-protein interactions. The MTiOpenScreen web server is free and open to all users at http://bioserv.rpbs.univ-paris-diderot.fr/services/MTiOpenScreen/.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

Binding of phenothiazines into allosteric hydrophobic pocket of human thioredoxin 1.

Thioredoxins are multifunctional oxidoreductase proteins implicated in the antioxidant cellular apparatus and oxidative stress. They are involved in several pathologies and are promising anticancer targets. Identification of noncatalytic binding sites is of great interest for designing new allosteric inhibitors of thioredoxin. In a recent work, we predicted normal mode motions of human thioredoxin 1 and identified two major putative hydrophobic binding sites. In this work we investigated noncovalent interactions of human thioredoxin 1 with three phenotiazinic drugs acting as prooxidant compounds by using molecular docking and circular dichroism spectrometry to probe ligand binding into the previously predicted allosteric hydrophobic pockets. Our in silico and CD spectrometry experiments suggested one preferred allosteric binding site involving helix 3 and adopting the best druggable conformation identified by NMA. The CD spectra showed binding of thioridazine into thioredoxin 1 and suggested partial helix unfolding, which most probably concerns helix 3. Taken together, these data support the strategy to design thioredoxin inhibitors targeting a druggable allosteric binding site.

Tampering with cell division by using small-molecule inhibitors of CDK-CKS protein interactions.

Cyclin-dependent kinases (CDKs) control many cellular processes and are considered important therapeutic targets. Large collections of inhibitors targeting CDK active sites have been discovered, but their use in chemical biology or drug development has been often hampered by their general lack of specificity. An alternative approach to develop more specific inhibitors is targeting protein interactions involving CDKs. CKS proteins interact with some CDKs and play important roles in cell division. We discovered two small-molecule inhibitors of CDK-CKS interactions. They bind to CDK2, do not inhibit its enzymatic activity, inhibit the proliferation of tumor cell lines, induce an increase in G1 and/or S-phase cell populations, and cause a decrease in CDK2, cyclin A, and p27(Kip1) levels. These molecules should help decipher the complex contributions of CDK-CKS complexes in the regulation of cell division, and they might present an interesting therapeutic potential.

Discovery of novel inhibitors of vascular endothelial growth factor-A-Neuropilin-1 interaction by structure-based virtual screening.

Neuropilin-1 (NRP-1), one of the most important co-receptors of vascular endothelial growth factor-A (VEGF-A), increases its angiogenic action in several chronic diseases including cancer by increasing the activity of associated tyrosine kinase receptors, VEGFR1 and VEGFR2. Binding of VEGF-A to NRP-1 plays a critical role in pathological angiogenesis and tumor progression. Today, targeting this interaction is a validated approach to fight against angiogenesis-dependent diseases. Only anti-NRP-1 antibodies, peptide and peptidomimetic drug-candidates or hits have been developed thus far. In order to identify potent orally active small organic molecules various experimental and in silico approaches can be used. Here we report, novel promising small drug-like molecules disrupting the binding of VEGF-A165 to NRP-1. We carried out structure-based virtual screening experiments using the ChemBridge compound collection on the VEGF-A165 binding pocket of NRP-1. After docking and two rounds of similarity search computations, we identified 4 compounds that inhibit the biotinylated VEGF-A165 binding to recombinant NRP-1 with Ki of about 10 µM. These compounds contain a common chlorobenzyloxy alkyloxy halogenobenzyl amine scaffold that can serve as a base for further development of new NRP-1 inhibitors.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction.

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

Ruthenium-Catalyzed Cycloaddition for Introducing Chemical Diversity in Second-Generation ß-Lactamase Inhibitors.

Ruthenium(II) alkyne azide cycloaddition (RuAAC) is an attractive reaction to access 1,5-triazole derivatives and is applicable to internal alkynes. Here, we explore RuAAC to introduce molecular diversity on the diazabicyclooctane (DBO) scaffold of ß-lactamase inhibitors. The methodology presented is fully regioselective and enabled synthesis of a series of 1,5-triazole DBOs and trisubstituted analogues. Molecular modelling and biological evaluation revealed that the DBO substituents provided putative stabilizing interactions in the active site of broad-spectrum ß-lactamase KPC-2 and promising activity against a hyperpermeable strain of Escherichia coli producing KPC-2.

Serum levels of leptin and adiponectin in patients with autoimmune Hashimoto's thyroiditis.

INTRODUCTION: Adipose tissue is immunologically and hormonally active, and these effects are mediated largely by adipocytokines. Thyroid hormones regulate metabolism and organ function, and Hashimoto's thyroiditis (HT) is the most common autoimmune disease affecting thyroid function. AIM: To evaluate the levels of the adipocytokines leptin and adiponectin in patients with autoimmune HT, and to perform a comparative intragroup analysis in patients with different stages of gland functional activity, and in a control group. MATERIALS AND METHODS: Ninety-five patients with HT and 21 healthy controls were enrolled in the study. Venous blood was taken without anticoagulants after at least 12 hours of fasting, and serum samples were frozen at -70°C until analysis. Serum levels of leptin and adiponectin were determined by an enzyme-linked immunosorbent assay (ELISA). RESULTS: Serum levels of leptin in HT patients were higher than those in the control group (4.5±5.2 ng/mL vs. 1.9±1.3 ng/mL). The hypothyroid patient's group showed significantly higher levels of leptin than those of the healthy controls (5.1±5.2 ng/mL vs. 1.9±1.3 ng/mL), (p=0.031). Leptin levels correlated positively with body mass index (r=0.533, p.