Chemical Diversity Analysis Research Articles

Differential Accumulation of Metabolites and Transcripts Related to Flavonoid, Styrylpyrone, and Galactolipid Biosynthesis in Equisetum Species and Tissue Types.

Three species of the genus Equisetum (E. arvense, E. hyemale, and E. telmateia) were selected for an analysis of chemical diversity in an ancient land plant lineage. Principal component analysis of metabolomics data obtained with above-ground shoot and below-ground rhizome extracts enabled a separation of all sample types, indicating species- and organ-specific patterns of metabolite accumulation. Follow-up efforts indicated that galactolipids, carotenoids, and flavonoid glycosides contributed positively to the separation of shoot samples, while stryrylpyrone glycosides and phenolic glycosides were the most prominent positive contributors to the separation of rhizome samples. Consistent with metabolite data, genes coding for enzymes of flavonoid and galactolipid biosynthesis were found to be expressed at elevated levels in shoot samples, whereas a putative styrylpyrone synthase gene was expressed preferentially in rhizomes. The current study builds a foundation for future endeavors to further interrogate the organ and tissue specificity of metabolism in the last living genus of a fern family that was prevalent in the forests of the late Paleozoic era.

Phylogeny-Aware Chemoinformatic Analysis of Chemical Diversity in Lamiaceae Enables Iridoid Pathway Assembly and Discovery of Aucubin Synthase.

Countless reports describe the isolation and structural characterization of natural products, yet this information remains disconnected and underutilized. Using a cheminformatics approach, we leverage the reported observations of iridoid glucosides with the known phylogeny of a large iridoid producing plant family (Lamiaceae) to generate a set of biosynthetic pathways that best explain the extant iridoid chemical diversity. We developed a pathway reconstruction algorithm that connects iridoid reports via reactions and prunes this solution space by considering phylogenetic relationships between genera. We formulate a model that emulates the evolution of iridoid glucosides to create a synthetic data set, used to select the parameters that would best reconstruct the pathways, and apply them to the iridoid data set to generate pathway hypotheses. These computationally generated pathways were then used as the basis by which to select and screen biosynthetic enzyme candidates. Our model was successfully applied to discover a cytochrome P450 enzyme from Callicarpa americana that catalyzes the oxidation of bartsioside to aucubin, predicted by our model despite neither molecule having been observed in the genus. We also demonstrate aucubin synthase activity in orthologues of Vitex agnus-castus, and the outgroup Paulownia tomentosa, further strengthening the hypothesis, enabled by our model, that the reaction was present in the ancestral biosynthetic pathway. This is the first systematic hypothesis on the epi-iridoid glucosides biosynthesis in 25 years and sets the stage for streamlined work on the iridoid pathway. This work highlights how curation and computational analysis of widely available structural data can facilitate hypothesis-based gene discovery.

Chemical diversity analysis and biological activities evaluation of different polyphenolic extracts of Marrubium deserti De Noé from Algeria

Ethnopharmacological relevanceMarrubium deserti De Noé (Lamiaceae) commonly known as desert horehound, is a small perennial herb distributed in arid climates. This plant has been used in Algeria for the treatment of cold, cough, fever and inflammation.Aim of the study: This study aimed to perform a phytochemical analysis and to evaluate the effect of diethyl ether (ExDiEth), n-butanol (ExBut) and hydroalcoholic (ExHyD) extracts of the aerial parts of M. deserti De Noé on inflammation, nociceptive response and fever in animal models. Materials and methodsThe total phenolic content and the different phenolic fraction (flavones-flavonols, free aglycones, anthocyanidins, C-glycosides and flavonic heterosides) were quantified in order to study the chemical diversity. The anti-inflammatory effect was evaluated using carrageenan-induced paw oedema test and the nociceptive response was determined by the chemical method using acetic acid-induced writhing response and formalin-induced paw licking tests and by the thermal method using the hot plate in mice. In addition, anti-fever was investigated using yeast-induced pyrexia in rats. ResultsQuantitative analysis revealed that among all extracts, ExBut had a higher phenolic content and the aerial parts of M. deserti De Noé collected from two different sites (Biskra and Tamanresset) had a significant difference in the contents of flavonic heterosids and flavones-flavonols. PCA and CA showed a strong intra-specific variability of M. deserti De Noé individuals. Our results showed that the ExDiEth, ExBut and ExHyD treatments prevented the increase of paw oedema by 56.25%, 66.65% and 45.56% inhibition percent, respectively. Moreover, treatments with these extracts significantly (p<0.05) decreased the number of writhing and increased the reaction time to thermal stimulation as well as demonstrated reduction in paw licking time both at early and late phases. Likewise, they also showed a significant reduction of yeast-induced pyrexia till 4 h of treatment. ConclusionResults from this study show that M. deserti De Noé aerial parts exhibited anti-inflammatory, analgesic with central and peripheral actions and antipyretic activities which could be associated with the presence of some phytochemicals as flavones, flavonols, flavonic heterosids and phenolic acids.

An Adjustable Protocol to Analyze Chemical Profiles of Non-sterile Rhizosphere Soil.

The analysis of chemical diversity in non-sterile rhizosphere soil has been a pressing methodological challenge for years. Rhizosphere-enriched chemicals (i.e., rhizochemicals) include root exudation chemicals, (microbial) breakdown products thereof, and de novo produced metabolites by rhizosphere-inhabiting microbes, all of which can play an important role in plant-soil interactions. The power and resolution of analytical methods and statistical analysis pipelines allow for better acquisition, separation and identification of rhizochemicals, thus providing unprecedented insight into the biochemistry underpinning plant-soil interactions. The current protocol describes a recently developed method to characterize rhizochemical profiles from plants, including crops, and is modular and customizable, allowing for application across a range of different plant-soil combinations. The protocol provides in-depth details about the experimental system for sample collection, data acquisition by liquid chromatography coupled to mass spectrometry, and analytical pipeline, which statistically selects for rhizochemicals by statistical comparison between metabolite profiles from plant-containing soil and plant-free soil. Moreover, the optional addition of chemical standards permits a semi-targeted approach, which improves the annotation of chemical signatures and identification of single rhizochemicals.

Chemical space of naturally occurring compounds

AbstractThe chemical space of naturally occurring compounds is vast and diverse. Other than biologics, naturally occurring small molecules include a large variety of compounds covering natural products from different sources such as plant, marine, and fungi, to name a few, and several food chemicals. The systematic exploration of the chemical space of naturally occurring compounds have significant implications in many areas of research including but not limited to drug discovery, nutrition, bio- and chemical diversity analysis. The exploration of the coverage and diversity of the chemical space of compound databases can be carried out in different ways. The approach will largely depend on the criteria to define the chemical space that is commonly selected based on the goals of the study. This chapter discusses major compound databases of natural products and cheminformatics strategies that have been used to characterize the chemical space of natural products. Recent exemplary studies of the chemical space of natural products from different sources and their relationships with other compounds are also discussed. We also present novel chemical descriptors and data mining approaches that are emerging to characterize the chemical space of naturally occurring compounds.

Platform for Unified Molecular Analysis: PUMA.

We introduce a free platform for chemoinformatic-based diversity analysis and visualization of chemical space of user supplied data sets. Platform for Unified Molecular Analysis (PUMA) integrates metrics used to characterize compound databases including visualization of chemical space, scaffold content, and analysis of chemical diversity. The user's input is a file with SMILES, database names, and compound IDs. PUMA computes molecular properties of pharmaceutical relevance, Murcko scaffolds, and diversity analysis. The user can interactively navigate through the graphs and export image files and the raw data of the diversity calculations. The platform links two public online resources: Consensus Diversity Plots for the assessment of global diversity and Activity Landscape Plotter to analyze structure-activity relationships. Herein, we describe the functionalities of PUMA and exemplify its use through the analysis of compound databases of general interest. PUMA is freely accessible at the authors web-site https://www.difacquim.com/d-tools/ .

Nanomolar anti-sickling compounds identified by ligand-based pharmacophore approach

Adenosine receptors are considered as potential targets for drug development against several diseases. The discovery of subtype 2B adenosine receptors role in erythrocyte sickling process proved its importance to neglected diseases such as sickle cell anemia, which affects approximately 29.000 people around the world, but whose treatment is restricted to just one FDA approved drug (hydroxyurea). In order to widen the therapeutic arsenal available to treat sickle cell anemia patients, it is imperative to identify new lead compounds that modify the sickling course and not just its symptoms. In order to accomplish this goal, ligand-based pharmacophore models that differentiate true ligands from decoys and enlighten the structure-activity relationship of known RA2B antagonists were employed screen the lead-like subset of the ZINC database. Following a chemical diversity analysis, 18 compounds were selected for biological evaluation. Among them, one molecule Z1139491704 (pEC50 = 7.77 ± 0.17) has shown better anti-sickling activity than MRS1754 (pEC50 = 7.63 ± 0.12), a commercial RA2B antagonist. Moreover, these compounds exhibited no cytotoxic effect at low micromolar range on mammalian cells. In conclusion, the sound development of validated ligand-based pharmacophore models proved essential to identify novel chemical scaffolds that might be useful to develop anti-sickling drugs.

Structure-based design of broadly protective group a streptococcal M protein-based vaccines

BackgroundA major obstacle to the development of broadly protective M protein-based group A streptococcal (GAS) vaccines is the variability within the N-terminal epitopes that evoke potent bactericidal antibodies. The concept of M type-specific protective immune responses has recently been challenged based on the observation that multivalent M protein vaccines elicited cross-reactive bactericidal antibodies against a number of non-vaccine M types of GAS. Additionally, a new “cluster-based” typing system of 175M proteins identified a limited number of clusters containing closely related M proteins. In the current study, we used the emm cluster typing system, in combination with computational structure-based peptide modeling, as a novel approach to the design of potentially broadly protective M protein-based vaccines. MethodsM protein sequences (AA 16–50) from the E4 cluster containing 17 emm types of GAS were analyzed using de novo 3-D structure prediction tools and the resulting structures subjected to chemical diversity analysis to identify sequences that were the most representative of the 3-D physicochemical properties of the M peptides in the cluster. Five peptides that spanned the range of physicochemical attributes of all 17 peptides were used to formulate synthetic and recombinant vaccines. Rabbit antisera were assayed for antibodies that cross-reacted with E4 peptides and whole bacteria by ELISA and for bactericidal activity against all E4GAS. ResultsThe synthetic vaccine rabbit antisera reacted with all 17 E4M peptides and demonstrated bactericidal activity against 15/17 E4GAS. A recombinant hybrid vaccine containing the same E4 peptides also elicited antibodies that cross-reacted with all E4M peptides. ConclusionsComprehensive studies using structure-based design may result in a broadly protective M peptide vaccine that will elicit cluster-specific and emm type-specific antibody responses against the majority of clinically relevant emm types of GAS.

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae.

BackgroundThe gene expression and secretion of fungal lignocellulolytic enzymes are tightly controlled at the transcription level using independent mechanisms to respond to distinct inducers from plant biomass. An advanced systems-level understanding of transcriptional regulatory networks is required to rationally engineer filamentous fungi for more efficient bioconversion of different types of biomass.ResultsIn this study we focused on ten chemically defined inducers to drive expression of cellulases, hemicellulases and accessory enzymes in the model filamentous fungus Aspergillus oryzae and shed light on the complex network of transcriptional activators required. The chemical diversity analysis of the inducers, based on 186 chemical descriptors calculated from the structure, resulted into three clusters, however, the global, metabolic and extracellular protein transcription of the A. oryzae genome were only partially explained by the chemical similarity of the enzyme inducers. Genes encoding enzymes that have attracted considerable interest such as cellobiose dehydrogenases and copper-dependent polysaccharide mono-oxygenases presented a substrate-specific induction. Several homology-model structures were derived using ab-initio multiple threading alignment in our effort to elucidate the interplay of transcription factors involved in regulating plant-deconstructing enzymes and metabolites. Systematic investigation of metabolite-protein interactions, using the 814 unique reactants involved in 2360 reactions in the genome scale metabolic network of A. oryzae, was performed through a two-step molecular docking against the binding pockets of the transcription factors AoXlnR and AoAmyR. A total of six metabolites viz., sulfite (H2SO3), sulfate (SLF), uroporphyrinogen III (UPGIII), ethanolamine phosphate (PETHM), D-glyceraldehyde 3-phosphate (T3P1) and taurine (TAUR) were found as strong binders, whereas the genes involved in the metabolic reactions that these metabolites appear were found to be significantly differentially expressed when comparing the inducers with glucose.ConclusionsBased on our observations, we believe that specific binding of sulfite to the regulator of the cellulase gene expression, AoXlnR, may be the molecular basis for the connection of sulfur metabolism and cellulase gene expression in filamentous fungi. Further characterization and manipulation of the regulatory network components identified in this study, will enable rational engineering of industrial strains for improved production of the sophisticated set of enzymes necessary to break-down chemically divergent plant biomass.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0224-5) contains supplementary material, which is available to authorized users.

Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches

Heparanase is a key enzyme involved in the dissemination of metastatic cancer cells. In this study a combination of in silico techniques and experimental methods was used to identify new potential inhibitors against this target. A 3D model of heparanase was built from sequence homology and applied to the virtual screening of a library composed of 27 known heparanase inhibitors and a commercial collection of drugs and drug-like compounds. The docking results from this campaign were combined with those obtained from a pharmacophore model recently published based in the same set of chemicals. Compounds were then ranked according to their theoretical binding affinity, and the top-rated commercial drugs were selected for further experimental evaluation.Biophysical methods (NMR and SPR) were applied to assess experimentally the interaction of the selected compounds with heparanase. The binding site was evaluated via competition experiments, using a known inhibitor of heparanase. Three of the selected drugs were found to bind to the active site of the protein and their KD values were determined. Among them, the antimalarial drug amodiaquine presented affinity towards the protein in the low-micromolar range, and was singled out for a SAR study based on its chemical scaffold. A subset of fourteen 4-arylaminoquinolines from a global set of 249 analogues of amodiaquine was selected based on the application of in silico models, a QSAR solubility prediction model and a chemical diversity analysis. Some of these compounds displayed binding affinities in the micromolar range.

Chemical diversity analysis on some selected medicinally important pteridophytes of Western Ghats, India

ObjectiveTo study the phyto-constituents present in some selected medicinally important pteridophytes from the Western Ghats, India. MethodsThe dried and powered leaves materials (50 g) were extracted successively with 250 mL of petroleum ether, ethyl acetate, methanol, chloroform, acetone, benzene and water by using Soxhlet extractor for 8 h at a temperature not exceeding the boiling point of the solvent. The aqueous extracts were filtered using Whattman filter paper (No.1) and then concentrated in vacuum at 40°C using Rotary evaporator. The residues obtained were stored in a freezer −70°C until further tests. Phytochemical screening of the extracts was carried out according to the standard methods. ResultsThe present study screened the phytochemical properties of ten plants with sixty six extracts and showed varied degree of phyto-constituents present. The presence or absence of the phyto-constituents depended upon the solvent medium used for extraction and the physiological property of individual taxa. ConclusionsFrom these results, it can be concluded that the ten plants extracts may be used as broad-spectrum antimicrobial, bioactive agent after extensive investigation.

Apports de la chémo-informatique dans la recherche et l’optimisation des molécules d’intérêt thérapeutique

Successful identification of new chemical entities with drug-like properties in pharmaceutical and academic research groups involves an early screen and the use of a large number of public and proprietary chemical libraries. Before applying high-throughput experimental screening approaches, virtual screening strategies have been put in place in order to sort and filter this massive amount of compounds and data available at these very early stages. Chemoinformatic tools have a crucial role in this selection process and enable therapeutic chemists to focus very early on promising candidates. Virtual screening has conventionally been based either on models of the target or the ligand (molecule), but today these models include biopharmaceutical filters addressing right from the start of the project the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the molecules. Above all, chemoinformatic tools help chemists understand better the chemical diversity they can work with, especially when comparing chemical libraries. This paper will focus on exemples of the day-to-day use of chemoinformatics in screening programs. A large part will be dedicated to new tools (chemographic and pharmacographic approaches) being developed for the representation and analysis of chemical diversity, but also for combining chemical and biological information to expedite research programs.

Novel Algorithms for the Identification of Biologically Informative Chemical Diversity Metrics.

Despite great advances in the efficiency of analytical and synthetic chemistry, time and available starting material still limit the number of unique compounds that can be practically synthesized and evaluated as prospective therapeutics. Chemical diversity analysis (the capacity to identify finite diverse subsets that reliably represent greater manifolds of drug-like chemicals) thus remains an important resource in drug discovery. Despite an unproven track record, chemical diversity has also been used to posit, from preliminary screen hits, new compounds with similar or better activity. Identifying diversity metrics that demonstrably encode bioactivity trends is thus of substantial potential value for intelligent assembly of targeted screens. This paper reports novel algorithms designed to simultaneously reflect chemical similarity or diversity trends and apparent bioactivity in compound collections. An extensive set of descriptors are evaluated within large NCI screening data sets according to bioactivity differentiation capacities, quantified as the ability to co-localize known active species into bioactive-rich K-means clusters. One method tested for descriptor selection orders features according to relative variance across a set of training compounds, and samples increasingly finer subset meshes for descriptors whose exclusion from the model induces drastic drops in relative bioactive colocalization. This yields metrics with reasonable bioactive enrichment (greater than 50% of all bioactive compounds collected into clusters or cells with significantly enriched active/inactive rates) for each of the four data sets examined herein. A second method replaces variance by an active/inactive divergence score, achieving comparable enrichment via a much more efficient search process. Combinations of the above metrics are tested in 2D rectilinear diversity models, achieving similarly successful colocalization statistics, with metrics derived from the active/inactive divergence score typically outperforming those selected from the variance criterion and computed from the DiverseSolutions software.

Recent Advances in the In Silico Modelling of UDP Glucuronosyltransferase Substrates

UDP glucurononosyltransferases (UGT) are a superfamily of enzymes that catalyse the conjugation of a range of structurally diverse drugs, environmental and endogenous chemicals with glucuronic acid. This process plays a significant role in the clearance and detoxification of many chemicals. Over the last decade the regulation and substrate profiles of UGT isoforms have been increasingly characterised. The resulting data has facilitated the prototyping of ligand based in silico models capable of predicting, and gaining insights into, binding affinity and the substrate- and regio- selectivity of glucuronidation by UGT isoforms. Pharmacophore modelling has produced particularly insightful models and quantitative structure-activity relationships based on machine learning algorithms result in accurate predictions. Simple structural chemical descriptors were found to capture much of the chemical information relevant to UGT metabolism. However, quantum chemical properties of molecules and the nucleophilic atoms in the molecule can enhance both the predictivity and chemical intuitiveness of structure-activity models. Chemical diversity analysis of known substrates has shown some bias towards chemicals with aromatic and aliphatic hydroxyl groups. Future progress in in silico development will depend on larger and more diverse high quality metabolic datasets. Furthermore, improved protein structure data on UGTs will enable the application of structural modelling techniques likely leading to greater insight into the binding and reactive processes of UGT catalysed glucuronidation.

The Importance of Local Chemical Structure for Chemical Metabolism by Human Uridine 5‘-Diphosphate−Glucuronosyltransferase

The uridine 5'-diphosphate- (UDP-)glucuronosyltransferase (UGT) family of enzymes catalyzes the conjugation of chemicals containing a suitable nucleophilic atom with glucuronic acid. Despite the importance of glucuronidation as an elimination and detoxification mechanism for drugs, environmental chemicals, and endogenous compounds, the structural features of substrates that confer isoform selectivity are poorly understood. The relationship between the local molecular structure of nucleophilic atoms of chemicals and the ability of UGT isoforms to glucuronidate the nucleophilic atoms was investigated here. The proximity of an aromatic ring to the nucleophilic atom was highly associated with a greater likelihood of glucuronidation by most UGT isoforms. Similarly, most UGT isoforms were found to have a statistically significant preference for oxygen over nitrogen as the nucleophilic atom. The converse was established only for UGT1A4. Naïve Bayes models were trained to predict the site of glucuronidation for eight UGT isoforms on the basis of the partial charge and Fukui function of the nucleophilic atom and whether an aromatic ring was attached to the nucleophilic atom. On average, the cross-validated sensitivity and specificity of the models were approximately 75-80%. For all but UGT2B7, the area under the receiver operating characteristics curve of the model was greater than 0.8, indicating strong predictive ability. A chemical diversity analysis of the currently available data indicates bias toward chemicals containing phenolic groups, and it is likely that the availability of chemical data sets with greater diversity will facilitate further insights into the structural features of substrates that confer enzyme selectivity.

DISSIM: a program for the analysis of chemical diversity

As interest in database searching and compound selection has grown, there has been a concomitant growth in interest in the quantification of chemical similarity. Described here is a computer program called DISSIM, which addresses the problem of selecting diverse subsets from larger collections of chemical compounds. It is a pragmatic solution combining a maximum dissimilarity search algorithm and a general multidimensional measure of chemical similarity based on the combination of different molecular descriptors. The problem of correlation between descriptors is addressed and appropriate schemes for weighting and normalisation are described. The specific application of these techniques to the comparative analysis of topological indices and their use in the area of chemical diversity analysis and compound selection are also described.

DISSIM: a program for the analysis of chemical diversity

As interest in database searching and compound selection has grown, there has been a concomitant growth in interest in the quantification of chemical similarity. Described here is a computer program called DISSIM, which addresses the problem of selecting diverse subsets from larger collections of chemical compounds. It is a pragmatic solution combining a maximum dissimilarity search algorithm and a general multidimensional measure of chemical similarity based on the combination of different molecular descriptors. The problem of correlation between descriptors is addressed and appropriate schemes for weighting and normalisation are described. The specific application of these techniques to the comparative analysis of topological indices and their use in the area of chemical diversity analysis and compound selection are also described.