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.

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.

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.

Ruthenium-Catalyzed Cycloaddition for Introducing Chemical Diversity in Second-Generation β-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 β-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 β-lactamase KPC-2 and promising activity against a hyperpermeable strain of Escherichia coli producing KPC-2.

Pharmacogenetics of human sulfotransferases and impact of amino acid exchange on Phase II drug metabolism

Sulfotransferases (SULTs) are Phase II drug-metabolizing enzymes (DMEs) catalyzing the sulfation of a variety of endogenous compounds, natural products, and drugs. Various drugs, such as nonsteroidal anti-inflammatory drugs (NSAIDS) can inhibit SULTs, affecting drug-drug interactions. Several polymorphisms have been identified for SULTs that might be crucial for interindividual variability in drug response and toxicity or for increased disease risk. Here, we review current knowledge on non-synonymous single nucleotide polymorphisms (nsSNPs) of human SULTs, focusing on the coded SULT allozymes and molecular mechanisms explaining their variable activity, which is essential for personalized medicine. We discuss the structural and dynamic bases of key amino acid (AA) variants implicated in the impacts on drug metabolism in the case of SULT1A1, as revealed by molecular modeling approaches.

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.

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.

Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations

Sulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3′-phosphoadenosine 5′-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein–ligand interactions studies where major rearrangements are expected.

New machine learning and physics-based scoring functions for drug discovery

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions

Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

Analysis of protein missense alterations by combining sequence- and structure-based methods

BACKGROUND

Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play.

METHODS

Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB.

RESULTS

We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions.

CONCLUSION

Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.

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.

Comparative structural and evolutionary analyses predict functional sites in the artemisinin resistance malaria protein K13

Numerous mutations in the Plasmodium falciparum Kelch13 (K13) protein confer resistance to artemisinin derivatives, the current front-line antimalarial drugs. K13 is an essential protein that contains BTB and Kelch-repeat propeller (KREP) domains usually found in E3 ubiquitin ligase complexes that target substrate protein(s) for ubiquitin-dependent degradation. K13 is thought to bind substrate proteins, but its functional/interaction sites and the structural alterations associated with artemisinin resistance mutations remain unknown. Here, we screened for the most evolutionarily conserved sites in the protein structure of K13 as indicators of structural and/or functional constraints. We inferred structure-dependent substitution rates at each amino acid site of the highly conserved K13 protein during the evolution of Apicomplexa parasites. We found two solvent-exposed patches of extraordinarily conserved sites likely involved in protein-protein interactions, one in BTB and the other one in KREP. The conserved patch in K13 KREP overlaps with a shallow pocket that displays a differential electrostatic surface potential, relative to neighboring sites, and that is rich in serine and arginine residues. Comparative structural and evolutionary analyses revealed that these properties were also found in the functionally-validated shallow pocket of other KREPs including that of the cancer-related KEAP1 protein. Finally, molecular dynamics simulations carried out on PfK13 R539T and C580Y artemisinin resistance mutant structures revealed some local structural destabilization of KREP but not in its shallow pocket. These findings open new avenues of research on one of the most enigmatic malaria proteins with the utmost clinical importance.

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.

Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

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.

Insights into molecular mechanisms of drug metabolism dysfunction of human CYP2C9*30

Cytochrome P450 2C9 (CYP2C9) metabolizes about 15% of clinically administrated drugs. The allelic variant CYP2C9*30 (A477T) is associated to diminished response to the antihypertensive effects of the prodrug losartan and affected metabolism of other drugs. Here, we investigated molecular mechanisms involved in the functional consequences of this amino-acid substitution. Molecular dynamics (MD) simulations performed for the active species of the enzyme (heme in the Compound I state), in the apo or substrate-bound state, and binding energy analyses gave insights into altered protein structure and dynamics involved in the defective drug metabolism of human CYP2C9.30. Our data revealed an increased rigidity of the key Substrate Recognition Sites SRS1 and SRS5 and shifting of the β turn 4 of SRS6 toward the helix F in CYP2C9.30. Channel and binding substrate dynamics analyses showed altered substrate channel access and active site accommodation. These conformational and dynamic changes are believed to be involved in the governing mechanism of the reduced catalytic activity. An ensemble of representative conformations of the WT and A477T mutant properly accommodating drug substrates were identified, those structures can be used for prediction of new CYP2C9 and CYP2C9.30 substrates and drug-drug interactions.

Blockade of the malignant phenotype by β-subunit selective noncovalent inhibition of immuno- and constitutive proteasomes

A structure-based virtual screening of over 400,000 small molecules against the constitutive proteasome activity followed by in vitro assays led to the discovery of a family of proteasome inhibitors with a sulfonyl piperazine scaffold. Some members of this family of small non-peptidic inhibitors were found to act selectively on the β2 trypsin-like catalytic site with a preference for the immunoproteasome β2i over the constitutive proteasome β2c, while some act on the β5 site and post-acid site β1 of both, the immunoproteasome and the constitutive proteasome. Anti-proliferative and anti-invasive effects on tumor cells were investigated and observed for two compounds. We report novel chemical inhibitors able to interfere with the three types of active centers of both, the immuno- and constitutive proteasomes. Identifying and analyzing a novel scaffold with decorations able to shift the binders’ active site selectivity is essential to design a future generation of proteasome inhibitors able to distinguish the immunoproteasome from the constitutive proteasome.

In silico model of the human ClC-Kb chloride channel: pore mapping, biostructural pathology and drug screening

The human ClC-Kb channel plays a key role in exporting chloride ions from the cytosol and is known to be involved in Bartter syndrome type 3 when its permeation capacity is decreased. The ClC-Kb channel has been recently proposed as a potential therapeutic target to treat hypertension. In order to gain new insights into the sequence-structure-function relationships of this channel, to investigate possible impacts of amino-acid substitutions, and to design novel inhibitors, we first built a structural model of the human ClC-Kb channel using comparative modeling strategies. We combined in silico and in vitro techniques to analyze amino acids involved in the chloride ion pathway as well as to rationalize the possible role of several clinically observed mutations leading to the Bartter syndrome type 3. Virtual screening and drug repositioning computations were then carried out. We identified six novel molecules, including 2 approved drugs, diflusinal and loperamide, with Kd values in the low micromolar range, that block the human ClC-Kb channel and that could be used as starting point to design novel chemical probes for this potential therapeutic target.

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

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.

Pharmacogenomics of the cytochrome P450 2C family: impacts of amino acid variations on drug metabolism

Pharmacogenomics investigates DNA and RNA variations in the human genome related to drug responses. Cytochrome P450 (CYP) is a supergene family of drug-metabolizing enzymes responsible for the metabolism of approximately 90% of human drugs. Among the major CYP isoforms, the CYP2C subfamily is of clinical significance because it metabolizes approximately 20% of clinically administrated drugs and represents several variant alleles leading to adverse drug reactions or altering drug efficacy. Here, we review recent progress on understanding the interindividual variability of the CYP2C members and the functional and clinical impact on drug metabolism. We summarize current advances in the molecular modeling of CYP2C polymorphisms and discuss the structural bases and molecular mechanisms of amino acid variants of CYP2C members that affect drug metabolism.

Stabilization of protein-protein interaction complexes through small molecules

Most of the small molecules that have been identified thus far to modulate protein-protein interactions (PPIs) are inhibitors. Another promising way to interfere with PPI-associated biological processes is to promote PPI stabilization. Even though PPI stabilizers are still scarce, stabilization of PPIs by small molecules is gaining momentum and offers new pharmacological options. Therefore, we have performed a literature survey of PPI stabilization using small molecules. From this, we propose a classification of PPI stabilizers based on their binding mode and the architecture of the complex to facilitate the structure-based design of stabilizers.

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.

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.

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/.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Advances in molecular modeling of human cytochrome P450 polymorphism

Cytochrome P450 (CYP) is a supergene family of metabolizing enzymes involved in the phase I metabolism of drugs and endogenous compounds. CYP oxidation often leads to inactive drug metabolites or to highly toxic or carcinogenic metabolites involved in adverse drug reactions (ADR). During the last decade, the impact of CYP polymorphism in various drug responses and ADR has been demonstrated. Of the drugs involved in ADR, 56% are metabolized by polymorphic phase I metabolizing enzymes, 86% among them being CYP. Here, we review the major CYP polymorphic forms, their impact for drug response and current advances in molecular modeling of CYP polymorphism. We focus on recent studies exploring CYP polymorphism performed by the use of sequence-based and/or protein-structure-based computational approaches. The importance of understanding the molecular mechanisms related to CYP polymorphism and drug response at the atomic level is outlined.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.

Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction

Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.

Computational analysis of protein-protein interfaces involving an alpha helix: insights for terphenyl-like molecules binding

Background

Protein-Protein Interactions (PPIs) are key for many cellular processes. The characterization of PPI interfaces and the prediction of putative ligand binding sites and hot spot residues are essential to design efficient small-molecule modulators of PPI. Terphenyl and its derivatives are small organic molecules known to mimic one face of protein-binding alpha-helical peptides. In this work we focus on several PPIs mediated by alpha-helical peptides.

Method

We performed computational sequence- and structure-based analyses in order to evaluate several key physicochemical and surface properties of proteins known to interact with alpha-helical peptides and/or terphenyl and its derivatives.

Results

Sequence-based analysis revealed low sequence identity between some of the analyzed proteins binding alpha-helical peptides. Structure-based analysis was performed to calculate the volume, the fractal dimension roughness and the hydrophobicity of the binding regions. Besides the overall hydrophobic character of the binding pockets, some specificities were detected. We showed that the hydrophobicity is not uniformly distributed in different alpha-helix binding pockets that can help to identify key hydrophobic hot spots.

Conclusions

The presence of hydrophobic cavities at the protein surface with a more complex shape than the entire protein surface seems to be an important property related to the ability of proteins to bind alpha-helical peptides and low molecular weight mimetics. Characterization of similarities and specificities of PPI binding sites can be helpful for further development of small molecules targeting alpha-helix binding proteins.

Post-docking optimization and analysis of protein-ligand interactions of estrogen receptor alpha using AMMOS software

Understanding protein-ligand interactions is a critical step in rational drug design/virtual ligand screening. In this work we applied the AMMOS_ProtLig software for post-docking optimization of estrogen receptor alpha complexes generated after virtual ligand screening protocol. Using MOE software we identified the ligand-receptor interactions in the optimized complexes at different levels of protein flexibility and compared them to the experimentally observed interactions. We analyzed in details the binding sites of three X-ray complexes of the same receptor and identified the key residues for the protein-ligand interactions. The complexes were further processed with AMMOS_ProtLig and the interactions in the predicted poses were compared to those observed in the X-ray structures. The effect of employing different levels of flexibility was analyzed. The results confirmed the AMMOS_ProtLig applicability as a helpful postdocking optimization tool for virtual ligand screening of estrogen receptors.

In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity

Aqueous solubility is one of the most important ADMET properties to assess and to optimize during the drug discovery process. At present, accurate prediction of solubility remains very challenging and there is an important need of independent benchmarking of the existing in silico models such as to suggest solutions for their improvement. In this study, we developed a new protocol for improved solubility prediction by combining several existing models available in commercial or free software packages. We first performed an evaluation of ten in silico models for aqueous solubility prediction on several data sets in order to assess the reliability of the methods, and we proposed a new diverse data set of 150 molecules as relevant test set, SolDiv150. We developed a random forest protocol to evaluate the performance of different fingerprints for aqueous solubility prediction based on molecular structure similarity. Our protocol, called a "multimodel protocol", allows selecting the most accurate model for a compound of interest among the employed models or software packages, achieving r(2) of 0.84 when applied to SolDiv150. We also found that all models assessed here performed better on druglike molecules than on real drugs, thus additional improvement is needed in this direction. Overall, our approach enlarges the applicability domain as demonstrated by the more accurate results for solubility prediction obtained using our protocol in comparison to using individual models.

Targeting the proangiogenic VEGF-VEGFR protein-protein interface with drug-like compounds by in silico and in vitro screening

Protein-protein interactions play a central role in medicine, and their modulation with small organic compounds remains an enormous challenge. Because it has been noted that the macromolecular complexes modulated to date have a relatively pronounced binding cavity at the interface, we decided to perform screening experiments over the vascular endothelial growth factor receptor (VEGFR), a validated target for antiangiogenic treatments with a very flat interface. We focused the study on the VEGFR-1 D2 domain, and 20 active compounds were identified. These small compounds contained a (3-carboxy-2-ureido)thiophen unit and had IC(50) values in the low micromolar range. The most potent compound inhibited the VEGF-induced VEGFR-1 transduction pathways. Our findings suggest that our best hit may be a promising scaffold to probe this macromolecular complex and for the development of treatments of VEGFR-1-dependent diseases.

AMMOS software: method and application

Recent advances in computational sciences enabled extensive use of in silico methods in projects at the interface between chemistry and biology. Among them virtual ligand screening, a modern set of approaches, facilitates hit identification and lead optimization in drug discovery programs. Most of these approaches require the preparation of the libraries containing small organic molecules to be screened or a refinement of the virtual screening results. Here we present an overview of the open source AMMOS software, which is a platform performing an automatic procedure that allows for a structural generation and optimization of drug-like molecules in compound collections, as well as a structural refinement of protein-ligand complexes to assist in silico screening exercises.

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

Tyrosine kinase syk non-enzymatic inhibitors and potential anti-allergic drug-like compounds discovered by virtual and in vitro screening

In the past decade, the spleen tyrosine kinase (Syk) has shown a high potential for the discovery of new treatments for inflammatory and autoimmune disorders. Pharmacological inhibitors of Syk catalytic site bearing therapeutic potential have been developed, with however limited specificity towards Syk. To address this topic, we opted for the design of drug-like compounds that could impede the interaction of Syk with its cellular partners while maintaining an active kinase protein. To achieve this challenging task, we used the powerful potential of intracellular antibodies for the modulation of cellular functions in vivo, combined to structure-based in silico screening. In our previous studies, we reported the anti-allergic properties of the intracellular antibody G4G11. With the aim of finding functional mimics of G4G11, we developed an Antibody Displacement Assay and we isolated the drug-like compound C-13, with promising in vivo anti-allergic activity. The likely binding cavity of this compound is located at the close vicinity of G4G11 epitope, far away from the catalytic site of Syk. Here we report the virtual screen of a collection of 500,000 molecules against this new cavity, which led to the isolation of 1000 compounds subsequently evaluated for their in vitro inhibitory effects using the Antibody Displacement Assay. Eighty five compounds were selected and evaluated for their ability to inhibit the liberation of allergic mediators from mast cells. Among them, 10 compounds inhibited degranulation with IC₅₀ values ≤ 10 µM. The most bioactive compounds combine biological activity, significant inhibition of antibody binding and strong affinity for Syk. Moreover, these molecules show a good potential for oral bioavailability and are not kinase catalytic site inhibitors. These bioactive compounds could be used as starting points for the development of new classes of non-enzymatic inhibitors of Syk and for drug discovery endeavour in the field of inflammation related disorders.

The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections

SUMMARY

The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness. This is a unique online tool allowing to prepare large compound libraries with in house or user-defined filtering parameters.

AVAILABILITY

The FAF-Drugs2 server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/FAF-Drugs/.

Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein-protein interactions

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.