Chemoinformatic analysis of GRAS (Generally Recognized as Safe) flavor chemicals and natural products

Potential role of Citrus bergamia flower essential oil against oral pathogens

BACKGROUND

Oral bacterial infections are difficult to treat due to emergence of resistance against antibiotic therapy. Essential oils are considered emerging alternate therapy against bacterial infections and biofilms. We investigated Citrus bergemia flower essential oil against oral pathogens.

METHODS

The essential oil was analsyed using Gas Chromatography(GC-MS), in silico investigations, antioxidant, antimicrobial, antibiofilm and antiquorum sensing assays.

RESULTS

Gas Chromatography analysis confirmed presence of 17 compounds including 1,6-Octadien-3-ol,3,7-dimethyl, 48.17%), l-limonene (22.03%) and p-menth-1-ol, 8-ol (7.31%) as major components. In silico analysis showed compliance of all tested major components with Lipinski's rule, Bioavailability and antimicrobial activity using PASS (prediction of activity spectrum of substances). Molecular docking with transcriptional regulators 3QP5, 5OE3, 4B2O and 3Q3D revealed strong interaction of all tested compounds except 1,6-Octadien-3-ol,3,7-dimethyl. All tested compounds presented significant inhibition of DPPH (2,2-diphenyl-1-picrylhydrazyl) (IC50 0.65 mg/mL), H2O2 (hydrogen peroxide) (63.5%) and high FRAP (ferrous reducing antioxidant power) value (239.01 µg). In antimicrobial screening a significant activity (MIC 0.125 mg/mL) against Bacillus paramycoides and Bacillus chungangensis was observed. Likewise a strong antibiofilm (52.1 - 69.5%) and anti-QS (quorum sensing) (4-16 mm) activity was recorded in a dose dependent manner.

CONCLUSION

It was therefore concluded that C. bergemia essential oil posess strong antioxidant, antimicrobial and antibiofilm activities against tested oral pathogens.

In silico co-crystal design: Assessment of the latest advances

Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active pharmaceutical ingredient and can lead to improvements in physicochemical properties of clinical relevance. At the same time, machine learning is finding its way into all areas of drug discovery and delivers impressive results. In this review, we attempt to provide an overview of machine learning, deep learning and network-based recommendation approaches applied to pharmaceutical co-crystallization. We also present crystal structure prediction as an alternative to machine learning approaches.

Imputation of sensory properties using deep learning

Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network. The imputation model achieved a substantially higher accuracy of prediction, with improvements in R² between 0.26 and 0.45 over the next best method for each sensory property. We also demonstrate that robust uncertainty estimates generated by the imputation model enable the most accurate predictions to be identified and that imputation also more accurately predicts activity cliffs, where small changes in compound structure result in large changes in sensory properties. In combination, these results demonstrate that the use of imputation, based on data from less expensive, early experiments, enables better selection of compounds for more costly studies, saving experimental time and resources.

Antimicrobial and antioxidant activities of hydrocarbon and oxygenated monoterpenes against some foodborne pathogens through in vitro and in silico studies

The present work describes the antimicrobial action of 25 monoterpenes (six hydrocarbons, five ketones, two aldehydes, six alcohols and six acetate analogues) against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus and antifungal activity against Aspergillus flavus. The antibacterial activity was evaluated by broth microdilution technique as a minimum inhibitory concentration (MIC) and the antifungal activity was performed by mycelia radial growth technique as the effective concentration causing 50% inhibition of the mycelial growth (EC₅₀). The results showed that thymol and α-terpineol were the most potent against E. coli (MIC = 45 and 55 mg/L, respectively) and S. aureus (MIC = 135 and 225 mg/L, respectively). The results also showed that thymol displayed the maximum antifungal action against A. flavus with EC₅₀ 20 mg/L. Furthermore, the antioxidant activity was determined using N,N-dimethyl-1,4-phenylenediamine (DMPD) and the results showed that geraniol were the most potent compound (IC₅₀ = 19 mg/L). Molecular docking studies indicated that the compounds displayed different binding interactions with the amino acid residues at the catalytic sites of N5-carboxyaminoimidazole synthetase and oxysterol binding protein Osh4 enzymes. Non-covalent interactions including van der Waals, hydrogen bonding as well as hydrophobic were observed between the compounds and the enzymes. A significant relationship was found between the docking score and the biological activity of the tested monoterpenes compared to the ceftriaxone and carbendazim as standard bactericide and fungicide, respectively. In silico ADMET properties were also performed and displayed potential for the development of promising antimicrobial agents. For these reasons, these compounds may be considered as potential ecofriendly alternatives in food preservation to delay or prevent the microbial infection and prolong the shelf life of food products.

Potential of hydrocarbon and oxygenated monoterpenes against Culex pipiens larvae: Toxicity, biochemical, pharmacophore modeling and molecular docking studies

Culex pipiens is a main vector for Bancroftian filariasis, Rift Valley Fever and diseases caused by other viruses, leaving several peoples with disabilities. In recent years, plant derived compounds have received much attention as potential alternatives to synthetic chemicals due to their low toxicity to mammals and environmental persistence. Twenty-one monoterpenes from different chemical groups (hydrocarbons and oxygenated products) were evaluated against Culex pipiens larvae. In addition, in vivo biochemical studies including effects on acetylcholine esterase (AChE), acid and alkaline phosphatases (ACP and ALP), total adenosine triphosphatase (ATPase) and gamma-aminobutyric acid transaminase (GABA-T) were investigated. Furthermore, in silico studies including pharmacophore elucidation, ADMET analysis and molecular docking of these compounds were performed. Among all tested monoterpenes, hydrocarbons [p-cymene, (R)-(+)-limonene and (+)-α-pinene], acetates (cinnamyl acetate, citronellyl acetate, eugenyl acetate and terpinyl acetate), alcohols [(±)-β-citronellol and terpineol], aldehydes [citral and (1R)-(-)-myrtenal] and ketone [(R)-(+)-pulegone] exhibited the highest larval toxicity with LC₅₀ = 14.88, 27.97, 26.13, 2.62, 3.81, 2.74, 21.65, 1.64, 21.70, 21.76, 1.68 and 1.90 mg/L after 48 h of exposure, respectively. The compounds proved a significant inhibition of all tested enzymes except total ATPase. The biochemical and molecular docking studies proved that AChE and GABA-T were the main targets for the tested monoterpenes.

Acaricidal activity, biochemical effects and molecular docking of some monoterpenes against two-spotted spider mite (Tetranychus urticae Koch)

Six natural monoterpenes (1,8-cineole, (-)-citronellal, limonene, α-pinene, pulegone and 4-terpineol) showed high acaricidal activity by fumigant and contact actions against adult females of the two-spotted spider mite, Tetranychus urticae Koch. The monoterpenes exhibited varying degrees of acaricidal potency using contact toxicity test after 24 and 48 h of treatment, where the LC₅₀ values were <160 and 45 mg/L, respectively. In fumigation test, of these six monoterpenes, pulegone exhibited the highest toxicity (LC₅₀ = 3.81 mg/L air), while (-)-citronellal had the lowest fumigant toxicity (LC₅₀ = 15.20 mg/L air). All compounds had high inhibitory effect on acetylcholinesterase (AChE) and gama amino butyric acid transaminase (GABA-T) activities. Pulegone was the most AChE inhibitor (IC₅₀ = 8.79 mg/L), while 4-terpineol revealed the lowest inhibitory effect (IC₅₀ = 32.82 mg/L). However, limonene caused the highest inhibition of GABA-T (IC₅₀ = 11.37 mg/L). The molecular docking studies revealed that the compounds displayed different binding interactions with the amino acid residues at the catalytic sites of AChE and GABA-T enzymes. Noncovalent interactions especially van der Waals, hydrogen bonding as well as hydrophobic was found between the compounds and the enzymes. A significant relationship was found between the docking score and the biological activity of monoterpenes compared to the standard acaricide pyridaben. In silico ADMET properties were also performed and displayed potential for the development of good acaricidal candidates.

Chemical space of naturally occurring compounds

The 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.

Analysis of a large food chemical database: chemical space, diversity, and complexity

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FoodDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of "Food Informatics". Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections.

Analysis of a large food chemical database: chemical space, diversity, and complexity

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FooDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of "Food Informatics". Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections. An additional future direction of this work is to use the list of 3,228 polyphenolic compounds identified in this work to enhance the on-going polyphenol-protein interactome studies.

Scaffold Diversity of Fungal Metabolites

Many drug discovery projects rely on commercial compounds to discover active leads. However, current commercial libraries, with mostly synthetic compounds, access a small fraction of the possible chemical diversity. Natural products, in contrast, possess a vast structural diversity and have proven to be an outstanding source of new drugs. Several chemoinformatic analyses of natural products have demonstrated their diversity and structural complexity. However, to our knowledge, the scaffold content and structural diversity of fungal secondary metabolites have never been studied. Herein, the scaffold diversity of 223 fungal metabolites was measured and compared to the diversity of approved drugs and commercial libraries for HTS containing natural, synthetic, and semi-synthetic compounds. In addition, the global diversity of the fungal isolates was assessed and compared to other reference data sets using Consensus Diversity Plots, a chemoinformatic tool recently developed. It was concluded that fungal secondary metabolites are cyclic systems with few ramifications and more diverse than the commercial libraries with natural products and semi-synthetic compounds. The fungal metabolites data set was one of the most structurally diverse, containing a large proportion of different and unique scaffolds not found in the other compound data sets including ChEMBL. Therefore, fungal metabolites offer a rich source of molecules suited for identifying diverse candidates for drug discovery.

Database fingerprint (DFP): an approach to represent molecular databases

BACKGROUND

Molecular fingerprints are widely used in several areas of chemoinformatics including diversity analysis and similarity searching. The fingerprint-based analysis of chemical libraries, in particular of large collections, usually requires the molecular representation of each compound in the library that may lead to issues of storage space and redundant calculations. In fact, information redundancy is inherent to the data, resulting on binary digit positions in the fingerprint without significant information.

RESULTS

Herein is proposed a general approach to represent an entire compound library with a single binary fingerprint. The development of the database fingerprint (DFP) is illustrated first using a short fingerprint (MACCS keys) for 10 data sets of general interest in chemistry. The application of the DFP is further shown with PubChem fingerprints for the data sets used in the primary example but with a larger number of compounds, up to 25,000 molecules. The performance of DFP were studied through differential Shannon entropy, k-mean clustering, and DFP/Tanimoto similarity.

CONCLUSIONS

The DFP is designed to capture key information of the compound collection and can be used to compare and assess the diversity of molecular libraries. This Preliminary Communication shows the potential of the novel fingerprint to conduct inter-library relationships. A major future goal is to apply the DFP for virtual screening and developing DFP for other data sets based on several different type of fingerprints.Graphical AbstractDatabase fingerprint captures the key information of molecular databases to perform chemical space characterization and virtual screening.

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

Consensus Diversity Plots: a global diversity analysis of chemical libraries

Background

Measuring the structural diversity of compound databases is relevant in drug discovery and many other areas of chemistry. Since molecular diversity depends on molecular representation, comprehensive chemoinformatic analysis of the diversity of libraries uses multiple criteria. For instance, the diversity of the molecular libraries is typically evaluated employing molecular scaffolds, structural fingerprints, and physicochemical properties. However, the assessment with each criterion is analyzed independently and it is not straightforward to provide an evaluation of the “global diversity”.

Results

Herein the Consensus Diversity Plot (CDP) is proposed as a novel method to represent in low dimensions the diversity of chemical libraries considering simultaneously multiple molecular representations. We illustrate the application of CDPs to classify eight compound data sets and two subsets with different sizes and compositions using molecular scaffolds, structural fingerprints, and physicochemical properties.

Conclusions

CDPs are general data mining tools that represent in two-dimensions the global diversity of compound data sets using multiple metrics. These plots can be constructed using single or combined measures of diversity. An online version of the CDPs is freely available at: https://consensusdiversityplots-difacquim-unam.shinyapps.io/RscriptsCDPlots/.Graphical Abstract

Chemoinformatic expedition of the chemical space of fungal products

AIM

Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project.

RESULTS

Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties.

CONCLUSION

Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.

Molecular Modeling and Chemoinformatics to Advance the Development of Modulators of Epigenetic Targets: A Focus on DNA Methyltransferases

In light of the emerging field of Epi-informatics, ie, computational methods applied to epigenetic research, molecular docking, and dynamics, pharmacophore and activity landscape modeling and QSAR play a key role in the development of modulators of DNA methyltransferases (DNMTs), one of the major epigenetic target families. The increased chemical information available for modulators of DNMTs has opened up the avenue to explore the epigenetic relevant chemical space (ERCS). Herein, we discuss recent progress on the identification and development of inhibitors of DNMTs as potential epi-drugs and epi-probes that have been driven by molecular modeling and chemoinformatics methods. We also survey advances on the elucidation of their structure-activity relationships and exploration of ERCS. Finally, it is illustrated how computational approaches can be applied to identify modulators of DNMTs in food chemicals.

A chemical space odyssey of inhibitors of histone deacetylases and bromodomains

The interest in epigenetic drug and probe discovery is growing as reflected in the large amount of structure-epigenetic activity information available.

Epigenetic relevant chemical space: a chemoinformatic characterization of inhibitors of DNA methyltransferases

The first comprehensive exploration of the epigenetic relevant chemical space is reported in this work with a special emphasis on inhibitors of DNA methyltransferases.

Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.

Expanding the fragrance chemical space for virtual screening

The properties of fragrance molecules in the public databases SuperScent and Flavornet were analyzed to define a “fragrance-like” (FL) property range (Heavy Atom Count ≤ 21, only C, H, O, S, (O + S) ≤ 3, Hydrogen Bond Donor ≤ 1) and the corresponding chemical space including FL molecules from PubChem (NIH repository of molecules), ChEMBL (bioactive molecules), ZINC (drug-like molecules), and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). The FL subsets of these databases were classified by MQN (Molecular Quantum Numbers, a set of 42 integer value descriptors of molecular structure) and formatted for fast MQN-similarity searching and interactive exploration of color-coded principal component maps in form of the FL-mapplet and FL-browser applications freely available at http://www.gdb.unibe.ch. MQN-similarity is shown to efficiently recover 15 different fragrance molecule families from the different FL subsets, demonstrating the relevance of the MQN-based tool to explore the fragrance chemical space.

Integrating virtual and biochemical screening for protein tyrosine phosphatase inhibitor discovery

Protein tyrosine phosphatases (PTPs) represent an important class of enzymes that mediate signal transduction and control diverse aspects of cell behavior. The importance of their activity is exemplified by their significant contribution to disease etiology with over half of all human PTP genes implicated in at least one disease. Small molecule inhibitors targeting individual PTPs are important biological tools, and are needed to fully characterize the function of these enzymes. Moreover, potent and selective PTP inhibitors hold the promise to transform the treatment of many diseases. While numerous methods exist to develop PTP-directed small molecules, we have found that complimentary use of both virtual (in silico) and biochemical (in vitro) screening approaches expedite compound identification and drug development. Here, we summarize methods pertinent to our work and others. Focusing on specific challenges and successes we have experienced, we discuss the considerable caution that must be taken to avoid enrichment of inhibitors that function by non-selective oxidation. We also discuss the utility of using "open" PTP structures to identify active-site directed compounds, a rather unconventional choice for virtual screening. When integrated closely, virtual and biochemical screening can be used in a productive workflow to identify small molecules targeting PTPs.

Systematic mining of generally recognized as safe (GRAS) flavor chemicals for bioactive compounds

Bioactive food compounds can be both therapeutically and nutritionally relevant. Screening strategies are widely employed to identify bioactive compounds from edible plants. Flavor additives contained in the so-called FEMA GRAS (generally recognized as safe) list of approved flavoring ingredients is an additional source of potentially bioactive compounds. This work used the principles of molecular similarity to identify compounds with potential mood-modulating properties. The ability of certain GRAS molecules to inhibit histone deacetylase-1 (HDAC1), proposed as an important player in mood modulation, was assayed. Two GRAS chemicals were identified as HDAC1 inhibitors in the micromolar range, results similar to what was observed for the structurally related mood prescription drug valproic acid. Additional studies on bioavailability, toxicity at higher concentrations, and off-target effects are warranted. The methodology described in this work could be employed to identify potentially bioactive flavor chemicals present in the FEMA GRAS list.

Shifting from the single to the multitarget paradigm in drug discovery

Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and high-throughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target-one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only 'key' compounds that fit with a single-target 'lock', but also 'master key' compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects).

Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases

Inhibitors of DNA methyltransferase (DNMT) are attractive compounds not only as potential therapeutic agents for the treatment of cancer and other diseases, but also as research tools to investigate the role of DNMTs in epigenetic events. Recent advances in high-throughput screening (HTS) for epigenetic targets and the availability of the first crystallographic structure of human DNMT1 encourage the integration of research strategies to uncover and optimize the activity of DNMT inhibitors. Herein, we present a binding model of a novel small-molecule DNMT1 inhibitor obtained by HTS, recently released in a public database. The docking model is in agreement with key interactions previously identified for established inhibitors using extensive computational studies including molecular dynamics and structure-based pharmacophore modeling. Based on the chemical structure of the novel inhibitor, a sequential computational screening of five chemical databases was performed to identify candidate compounds for testing. Similarity searching followed by molecular docking of chemical databases such as approved drugs, natural products, a DNMT-focused library, and a general screening collection, identified at least 108 molecules with promising DNMT inhibitory activity. The chemical structures of all hit compounds are disclosed to encourage the research community working on epigenetics to test experimentally the enzymatic and demethylating activity in vivo. Five candidate hits are drugs approved for other indications and represent potential starting points of a drug repurposing strategy.