Deciphering a Pharmacophore Network: A Case Study Using BCR-ABL Data.

This paper introduces a general method that can be used to create groups of pharmacophores to support their further in-depth analysis. A BCR-ABL molecular dataset was used to calculate graph edit distances between pharmacophores and led to their organization into a novel pharmacophore network. The application of a graph layout algorithm allowed us to discriminate between the pharmacophores associated with active compounds and those associated with inactive compounds. A clustering approach was used to refine the partitioning by grouping the pharmacophores based on their structures, activities, and binding modes. Analysis of a newly spatialized pharmacophore network provided us with critical insight into structure-activity relationships, most notably those that revealed distinctions between activity classes and chemical families. As shown, this method permits us to identify families of structurally homogeneous pharmacophores.

Disproportionality analysis in VigiBase as a drug repositioning method for the discovery of potentially useful drugs in Alzheimer's disease.

Drug repositioning aims to propose new indications for marketed drugs. Although several methods exist, the utility of pharmacovigilance databases for this purpose is unclear. We conducted a disproportionality analysis in the World Health Organization pharmacovigilance database VigiBase to identify potential anticholinesterase drug candidates for repositioning in Alzheimer's disease (AD). METHODS: Disproportionality analysis is a validated method for detecting significant associations between drugs and adverse events (AEs) in pharmacovigilance databases. We applied this approach in VigiBase to establish the safety profile displayed by the anticholinesterase drugs used in AD and searched the database for drugs with similar safety profiles. The detected drugs with potential activity against acetylcholinesterase and butyrylcholinesterases (BuChEs) were then evaluated to confirm their anticholinesterase potential. RESULTS: We identified 22 drugs with safety profiles similar to AD medicines. Among these drugs, 4 (clozapine, aripiprazole, sertraline and S-duloxetine) showed a human BuChE inhibition rate of over 70% at 10-5  M. Their human BuChE half maximal inhibitory concentration values were compatible with clinical anticholinesterase action in humans at their normal doses. The most active human BuChE inhibitor in our study was S-duloxetine, with a half maximal inhibitory concentration of 1.2 µM. Combined with its ability to inhibit serotonin (5-HT) reuptake, the use of this drug could represent a novel multitarget directed ligand therapeutic strategy for AD. CONCLUSION: We identified 4 drugs with repositioning potential in AD using drug safety profiles derived from a pharmacovigilance database. This method could be useful for future drug repositioning efforts.

Cryptic Pockets Repository through Pocket Dynamics Tracking and Metadynamics on Essential Dynamics Space: Applications to Mcl-1.

Detection of cryptic pockets (hidden protein pockets) is a hot topic in structure-based drug discovery, especially for drugging the yet undruggable proteome. The experimental detection of cryptic pockets is still considered an expensive endeavor. Thus, computational methods, such as atomistic simulations, are used instead. These simulation methods can provide a perspective on protein dynamics that overpasses the experimental X-ray structures' static and average view. Nonetheless, unbiased molecular dynamics (MD) simulations fall short to detect transient and cryptic pockets requiring the crossing of high-energy barriers. Enhanced sampling methods, such as Metadynamics, provide a solution to overcome the time-scale problem faced by unbiased MD simulations. However, these methods are still limited by the availability of collective variable space to capture the intricate parameters, leading to the opening of cryptic pockets. Unfortunately, the design of such collective variables requires a priori knowledge of the binding site, information that is by definition lacking for cryptic pockets. In this work, we evaluated the use of the Metadynamics biasing scheme on essential coordinates space as a general method for cryptic pocket detection. This approach was applied to an antiapoptotic protein: Mcl-1 as a test model. In addition to providing a broader characterization of Mcl-1's conformational space, we show the effectiveness of this method in drawing the full repository of Mcl-1's known and novel cryptic pockets in an unsupervised manner.

Insights into Mcl-1 Conformational States and Allosteric Inhibition Mechanism from Molecular Dynamics Simulations, Enhanced Sampling, and Pocket Crosstalk Analysis.

In this study, we explored the structural dynamics of Mcl-1, an anti-apoptotic protein. On the basis of structural ensembles, the essential dynamics was extracted and showed two major axes of variability: a breathing motion at the binding interface and a correlated motion through the internal loops. A free energy surface characterizing the breathing motion at the binding interface was generated and suggested an equilibrium between a closed conformation and a "ready to bind" conformation as the predominant states of Mcl-1 in solution. Moreover, the analysis of the dynamics along the internal loops revealed a hidden communication network of transient and cryptic pockets controlling the allosteric inhibition of Mcl-1. A detailed model joining the pocket crosstalk and salt bridge networks along the internal loops was proposed and allowed us to shed light on the key interactions governing Mcl-1's allosteric inhibition.

A Computational Selection of Metabolite Biomarkers Using Emerging Pattern Mining: A Case Study in Human Hepatocellular Carcinoma.

The biomarker development in metabolomics aims at discriminating diseased from normal subjects and at creating a predictive model that can be used to diagnose new subjects. From a case study on human hepatocellular carcinoma (HCC), we studied for the first time the potential usefulness of the emerging patterns (EPs) that come from the data mining domain. When applied to a metabolomics data set labeled with two classes (e.g., HCC patients vs healthy subjects), EP mining can capture differentiating combinations of metabolites between the two classes. We observed that the so-called jumping emerging patterns (JEPs), which correspond to the combinations of metabolites that occur in only one of the two classes, achieved better performance than individual biomarkers. Particularly, the implementation of the JEPs in a rules-based diagnostic tool drastically reduced the false positive rate, i.e., the rate of healthy subjects predicted as HCC patients.

Toward Understanding Mcl-1 Promiscuous and Specific Binding Mode.

Mcl-1, which is an anti-apoptotic member of the Bcl-2 protein family, is overexpressed in various cancers and promotes the aberrant survival of tumor cells. To inhibit Mcl-1, and initiate apoptosis, an interaction between BH3-only proteins and Mcl-1 anti-apoptotic protein is necessary. These protein-protein interactions exhibit some selectivity: Mcl-1 binds specifically to Noxa, whereas Bim and Puma bind strongly to all anti-apoptotic proteins. Even if the three-dimensional (3D) structures of several Mcl-1/BH3-only complexes have been solved, the BH3-only binding specificity to Mcl-1 is still not completely understood. In this study, molecular dynamics simulations were used to elucidate the molecular basis of the interactions with Mcl-1. Our results corroborate the importance of four conserved hydrophobic residues and a conserved aspartic acid on BH3-only as a common binding pattern. Furthermore, our results highlight the contribution of the fifth hydrophobic residue in the C-terminal part and a negatively charged patch in the N-terminal of BH3-only peptides as important for their fixation to Mcl-1. We hypothesize that this negatively charged patch will be an Mcl-1 specific binding pattern.

Conformation and Dynamics of Human Urotensin II and Urotensin Related Peptide in Aqueous Solution.

Conformation and dynamics of the vasoconstrictive peptides human urotensin II (UII) and urotensin related peptide (URP) have been investigated by both unrestrained and enhanced-sampling molecular-dynamics (MD) simulations and NMR spectroscopy. These peptides are natural ligands of the G-protein coupled urotensin II receptor (UTR) and have been linked to mammalian pathophysiology. UII and URP cannot be characterized by a single structure but exist as an equilibrium of two main classes of ring conformations, open and folded, with rapidly interchanging subtypes. The open states are characterized by turns of various types centered at K8Y9 or F6W7 predominantly with no or only sparsely populated transannular hydrogen bonds. The folded conformations show multiple turns stabilized by highly populated transannular hydrogen bonds comprising centers F6W7K8 or W7K8Y9. Some of these conformations have not been characterized previously. The equilibrium populations that are experimentally difficult to access were estimated by replica-exchange MD simulations and validated by comparison of experimental NMR data with chemical shifts calculated with density-functional theory. UII exhibits approximately 72% open:28% folded conformations in aqueous solution. URP shows very similar ring conformations as UII but differs in an open:folded equilibrium shifted further toward open conformations (86:14) possibly arising from the absence of folded N-terminal tail-ring interaction. The results suggest that the different biological effects of UII and URP are not caused by differences in ring conformations but rather by different interactions with UTR.

Acute toxicities of pharmaceuticals toward green algae. mode of action, biopharmaceutical drug disposition classification system and quantile regression models.

The acute toxicities of 36 pharmaceuticals towards green algae were estimated from a set of quantile regression models representing the first global quantitative structure-activity relationships. The selection of these pharmaceuticals was based on their predicted environmental concentrations. An agreement between the estimated values and the observed acute toxicity values was found for several families of pharmaceuticals, in particular, for antidepressants. A recent classification (BDDCS) of drugs based on ADME properties (Absorption, Distribution, Metabolism and Excretion) was clearly correlated with the acute ecotoxicities towards algae. Over-estimation of toxicity from our QSAR models was observed for classes 2, 3 and 4 whereas our model results were in agreement for the class 1 pharmaceuticals. Clarithromycin, a class 3 antibiotic characterized by weak metabolism and high solubility, was the most toxic to algae (molecular stability and presence in surface water).

A Three-Site Mechanism for Agonist/Antagonist Selective Binding to Vasopressin Receptors.

Molecular-dynamics simulations with metadynamics enhanced sampling reveal three distinct binding sites for arginine vasopressin (AVP) within its V2 -receptor (V2 R). Two of these, the vestibule and intermediate sites, block (antagonize) the receptor, and the third is the orthosteric activation (agonist) site. The contacts found for the orthosteric site satisfy all the requirements deduced from mutagenesis experiments. Metadynamics simulations for V2 R and its V1a R-analog give an excellent correlation with experimental binding free energies by assuming that the most stable binding site in the simulations corresponds to the experimental binding free energy in each case. The resulting three-site mechanism separates agonists from antagonists and explains subtype selectivity.

Discovering structural alerts for mutagenicity using stable emerging molecular patterns.

This study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability. The growth rate is a well-established measurement of contrast between classes. Moreover, the extracted patterns correspond to formal concepts; the most robust patterns, named the stable emerging patterns (SEPs), can then be identified thanks to their stability, a new notion originating from the domain of formal concept analysis. All of these elements are explained in the paper from the point of view of computation. The method was applied to a molecular data set on mutagenicity. The experimental results demonstrate its efficiency: it automatically outputs a manageable number of structural patterns that are strongly related to mutagenicity. Moreover, a part of the resulting structures corresponds to already known structural alerts. Finally, an in-depth chemical analysis relying on these structures demonstrates how the method can initiate promising processes of chemical knowledge discovery.

Dual histamine H3R/serotonin 5-HT4R ligands with antiamnesic properties: pharmacophore-based virtual screening and polypharmacology.

In recent years, preclinical and clinical studies have generated considerable interest in the development of histamine H3 receptor (H3R) antagonists as novel treatment for degenerative disorders associated with impaired cholinergic function. To identify novel scaffolds for H3R antagonism, a common feature-based pharmacophore model was developed and used to screen the 17,194 compounds of the CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie) chemical library. Out of 268 virtual hits which have been gathered in 34 clusters, we were particularly interested in tricyclic derivatives also exhibiting a potent 5HT4R affinity. Benzo[h][1,6]naphthyridine derivatives showed the highest H3R affinity, and compound 17 (H3R Ki = 41.6 nM; 5-HT4R Ki = 208 nM) completely reversed the amnesiant effect of scopolamine at 3 mg/kg in a spatial working memory experiment. For the first time we demonstrated the feasibility to combine H3R and 5-HT4R activities in a single molecule, raising the exciting possibility that dual H3R antagonist/5HT4R agonist have potential for the treatment of neurodegenerative diseases such as Alzheimer's disease.

Assessment of the genotoxic and carcinogenic potentials of 3-aminothiophene derivatives using in vitro and in silico methodologies.

Thiophene derivatives, a class of compounds widely used in products such as pharmaceuticals, agrochemicals or dyestuffs, represent chemicals of concern. Indeed, the thiophene ring is often considered as a structural moiety that may be involved in toxic effects in humans. We primarily focus on the genotoxic/mutagenic and carcinogenic potentials of the methyl 3-amino-4-methylthiophene-2-carboxylate (1), a precursor of the articaine local anesthetic (4) which falls within the scope of the European REACH (Registration, Evaluation, Authorisation and restriction of CHemicals) legislation. To discern some structure-toxicity relationships, we also studied two related compounds, namely the 3-amino 4-methylthiophene (2) and the 2-acetyl 4-chlorothiophene (3). Techniques employed to assess mutagenic and DNA-damaging effects involved the Salmonella mutagenicity assay (or Ames test) and the single-cell gel electrophoresis assay (or Comet assay). In the range of tested doses, none of these derivatives led to a positive response in the Ames tests and DNA damage was only observed in the Comet assay after high concentration exposure of 2. The study of their carcinogenic potential using the in vitro SHE (Syrian Hamster Embryo) cell transformation assay (CTA) highlighted the activity of compound 2. A combination of experimental data with in silico predictions of the reactivity of thiophene derivatives towards cytochrome P450 (CYP450), enabled us to hypothesize possible pathways leading to these toxicological profiles.

Protecting honey bees: identification of a new varroacide by in silico, in vitro, and in vivo studies.

Varroa destructor is the main concern related to the gradual decline of honeybees. Nowadays, among the various acaricides used in the control of V. destructor, most presents increasing resistance. An interesting alternative could be the identification of existent molecules as new acaricides with no effect on honeybee health. We have previously constructed the first 3D model of AChE for honeybee. By analyzing data concerning amino acid mutations implicated in the resistance associated to pesticides, it appears that pirimicarb should be a good candidate for varroacide. To check this hypothesis, we characterized the AChE gene of V. destructor. In the same way, we proposed a 3D model for the AChE of V. destructor. Starting from the definition of these two 3D models of AChE in honeybee and varroa, a comparison between the gorges of the active site highlighted some major differences and particularly different shapes. Following this result, docking studies have shown that pirimicarb adopts two distinct positions with the strongest intermolecular interactions with VdAChE. This result was confirmed with in vitro and in vivo data for which a clear inhibition of VdAChE by pirimicarb at 10 µM (contrary to HbAChE) and a 100% mortality of varroa (dose corresponding to the LD50 (contact) for honeybee divided by a factor 100) were observed. These results demonstrate that primicarb could be a new varroacide candidate and reinforce the high relationships between in silico, in vitro, and in vivo data for the design of new selective pesticides.

Navigating pharmacophore space to identify activity discontinuities: a case study with BCR-ABL.

The exploration of chemical space is a fundamental aspect of chemoinformatics, particularly when one explores a large compound data set to relate chemical structures with molecular properties. In this study, we extend our previous work on chemical space visualization at the pharmacophoric level. Instead of using conventional binary classification of affinity (active vs inactive), we introduce a refined approach that categorizes compounds into four distinct classes based on their activity levels: super active, very active, active, and inactive. This classification enriches the color scheme applied to pharmacophore space, where the color representation of a pharmacophore hypothesis is driven by the associated compounds. Using the BCR-ABL tyrosine kinase as a case study, we identified intriguing regions corresponding to pharmacophore activity discontinuities, providing valuable insights for structure-activity relationships analysis.

A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

New Pharmacophore Fingerprints and Weight-matrix Learning for Virtual Screening. Application to Bcr-Abl Data.

In this work, we propose to analyze the potential of a new type of pharmacophoric descriptors coupled to a novel feature transformation technique, called Weight-Matrix Learning (WML, based on a feed-forward neural network). The application concerns virtual screening on a tyrosine kinase named BCR-ABL. First, the compounds were described using three different families of descriptors: our new pharmacophoric descriptors, and two circular fingerprints, ECFP4 and FCFP4. Afterwards, each of these original molecular representations were transformed using either an unsupervised WML method or a supervised one. Finally, using these transformed representations, K-Means clustering algorithm was applied to automatically partition the molecules. Combining our pharmacophoric descriptors with supervised Weight-Matrix Learning (SWMLR ) leads to clearly superior results in terms of several quality measures.

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm.

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS-based fingerprints and 12 well-known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state-of-the-art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.

Towards a partial order graph for interactive pharmacophore exploration: extraction of pharmacophores activity delta.

This paper presents a novel approach called Pharmacophore Activity Delta for extracting outstanding pharmacophores from a chemogenomic dataset, with a specific focus on a kinase target known as BCR-ABL. The method involves constructing a Hasse diagram, referred to as the pharmacophore network, by utilizing the subgraph partial order as an initial step, leading to the identification of pharmacophores for further evaluation. A pharmacophore is classified as a 'Pharmacophore Activity Delta' if its capability to effectively discriminate between active vs inactive molecules significantly deviates (by at least δ standard deviations) from the mean capability of its related pharmacophores. Among the 1479 molecules associated to BCR-ABL binding data, 130 Pharmacophore Activity Delta were identified. The pharmacophore network reveals distinct regions associated with active and inactive molecules. The study includes a discussion on representative key areas linked to different pharmacophores, emphasizing structure-activity relationships.

Structural characterizations of oligopyridyl foldamers, α-helix mimetics.

Protein-protein interactions are central to many biological processes, from intracellular communication to cytoskeleton assembly, and therefore represent an important class of targets for new therapeutics. The most common secondary structure in natural proteins is an α-helix. Small molecules seem to be attractive candidates for stabilizing or disrupting protein-protein interactions based on α-helices. In our study, we assessed the ability of oligopyridyl scaffolds to mimic the α-helical twist. The theoretical as well as experimental studies (X-ray diffraction and NMR) on conformations of bipyridines in the function of substituent and pyridine nitrogen positions were carried out. Furthermore, the experimental techniques showed that the conformations observed in bipyridines are maintained within a longer oligopyridyl scaffold (quaterpyridines). The alignment of the synthesized quaterpyridine with two methyl substituents showed that it is an α-helix foldamer; their methyl groups overlap very well with side chain positions, i and i + 3, of an ideal α-helix.

Interpretation of honeybees contact toxicity associated to acetylcholinesterase inhibitors.

The widespread use of different pesticides generates adverse effects on non target organisms like honeybees. Organophosphorous and carbamates kill honeybees through the inactivation of acetylcholinesterase (AChE), thereby interfering with nerve signaling and function. For this class of pesticides, it is fundamental to understand the relationship between their structures and the contact toxicity for honeybees. A Quantitative Structure-Activity Relationship (QSAR) study was carried out on 45 derivatives by a genetic algorithm approach starting from more than 2500 descriptors. In parallel, a new 3D model of AChE associated to honeybees was defined. Physicochemical properties of the receptor and docking studies of the derivatives allow understanding the meaningful of three descriptors and the implication of several amino acids in the overall toxicity of the pesticides.