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.

Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics.

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.

Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods.

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.

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.

Pegylated triarylmethanes: Synthesis, antimicrobial activity, anti-proliferative behavior and in silico studies.

We describe herein the synthesis, characterization and biological studies of novel PEGylated triarylmethanes. Non-symmetrical and symmetrical triarylmethanes series have been synthesized by Friedel-Crafts hydroxyalkylation or directly from bisacodyl respectively followed by a functionalization with PEG fragments in order to increase bioavailability and biological effectiveness. The antimicrobial activity was investigated against Gram-positive and Gram-negative foodborne pathogens and against Candida albicans, an opportunistic pathogenic yeast. The anti-biocidal activity was also studied using Staphylococcus aureus as a reference bacterium. Almost all PEGylated molecules displayed an antifungal activity comparable with fusidic acid with MIC values ranging from 6.25 to 50 µg/mL. Compounds also revealed a promising antibiofilm activity with biofilm eradication percentages values above 80% for the best molecules (compounds 4d and 7). Compounds 7 and 8b showed a modest antiproliferative activity against human colorectal cancer cell lines HT-29. Finally, in silico molecular docking studies revealed DHFR and DNA gyrase B as potential anti-bacterial targets and in silico predictions of ADME suggested adequate drug-likeness profiles for the synthetized triarylmethanes.

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.

Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions.

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives.

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.

Importance of the pharmacological profile of the bound ligand in enrichment on nuclear receptors: toward the use of experimentally validated decoy ligands.

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.

PLO3S: Protein LOcal Surficial Similarity Screening.

The study of protein molecular surfaces enables to better understand and predict protein interactions. Different methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the Wave Kernel Signature: Protein LOcal Surficial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to filter large protein surfaces datasets in protein structures virtual screening protocols.

Colorectal anticancer activity of a novel class of triazolic triarylmethane derivatives.

Triarylmethanes and triazoles constitute privileged structures extensively used in drug discovery programs. In this work, 12 novel triarylmethanes linked to a triazole ring were designed, synthesized, and chemically characterized aiming to target colorectal cancer. The synthetic strategy for triarylmethanes mono- and bi-substituted by a functionalized triazole ring involved a 1,3-dipolar cycloaddition. A preliminary screening in human colorectal cancer cells (HT-29 and HCT116) and murine primary fibroblasts (L929) allowed the selection of the best candidate 9b based on its high inhibition of cancer cell proliferation with an IC50 of 11 µM on HT-29 and 14 µM on HCT116 and its non-cytotoxic effects on murine fibroblasts (<100 µM). A deep mechanistic study on various pathways showed that compound 9b induces caspase-3 cleavage, and its inhibitory effect on PARP activity is correlated with the increase of DNA fragmentation in cancer cells. Moreover, 9b induced apoptosis promoted by the inhibition of anti-apoptotic cell survival signaling pathways demonstrated via the downregulation of phosphorylated Akt and ERK proteins. Finally, the predicted binding modes of compounds 8c and 9b to five potential biological targets (i.e., AKT, ERK-1 and ERK-2, PARP and caspase-3) was evaluated using molecular modeling, and the predictions of the SuperPred webserver identified ERK2 as the most remarkable target. Also predicted in silico, 9b displayed appropriate drug-likeness and good absorption, distribution, metabolism and excretion (ADME) profiles.

Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query.

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.

Antiproliferative activity of trans-avicennol from Zanthoxylum chiloperone var. angustifolium against human cancer stem cells.

Zanthoxylum chiloperone var. angustifolium root bark was studied with the aim of finding novel molecules able to overcome cancer stem cell chemoresistance. Purification of a methanol-soluble extract resulted in the isolation of a known pyranocoumarin, trans-avicennol (1). Compound 1 demonstrated antiproliferative activity on glioma-initiating cells, whereas it was inactive on human neural stem cells. trans-Avicennol (1) activated the MAPK/ERK pathway and was also evaluated for its ability to inhibit the enzyme indoleamine-2,3-dioxygenase.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns.

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 in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Novel Set of Diarylmethanes to Target Colorectal Cancer: Synthesis, In Vitro and In Silico Studies.

Distinctive structural, chemical, and physical properties make the diarylmethane scaffold an essential constituent of many active biomolecules nowadays used in pharmaceutical, agrochemical, and material sciences. In this work, 33 novel diarylmethane molecules aiming to target colorectal cancer were designed. Two series of functionalized olefinic and aryloxy diarylmethanes were synthesized and chemically characterized. The synthetic strategy of olefinic diarylmethanes involved a McMurry cross-coupling reaction as key step and the synthesis of aryloxy diarylmethanes included an O-arylation step. A preliminarily screening in human colorectal cancer cells (HT-29 and HCT116) and murine primary fibroblasts (L929) allowed the selection, for more detailed analyses, of the three best candidates (10a, 10b and 12a) based on their high inhibition of cancer cell proliferation and non-toxic effects on murine fibroblasts (<100 µM). The anticancer potential of these diarylmethane compounds was then assessed using apoptotic (phospho-p38) and anti-apoptotic (phospho-ERK, phospho-Akt) cell survival signaling pathways, by analyzing the DNA fragmentation capacity, and through the caspase-3 and PARP cleavage pro-apoptotic markers. Compound 12a (2-(1-(4-methoxyphenyl)-2-(4-(trifluoromethyl)phenyl) vinyl) pyridine, Z isomer) was found to be the most active molecule. The binding mode to five biological targets (i.e., AKT, ERK-1 and ERK-2, PARP, and caspase-3) was explored using molecular modeling, and AKT was identified as the most interesting target. Finally, compounds 10a, 10b and 12a were predicted to have appropriate drug-likeness and good Absorption, Distribution, Metabolism and Excretion (ADME) profiles.

Neuropilin 1 and Neuropilin 2 gene invalidation or pharmacological inhibition reveals their relevance for the treatment of metastatic renal cell carcinoma.

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.

High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling.

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.

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.

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds.

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.

Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile.

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.