Secondary Metabolic Profile as a Tool for Distinction and Characterization of Cultivars of Black Pepper (Piper nigrum L.) Cultivated in Pará State, Brazil.

This study evaluated the chemical compositions of the leaves and fruits of eight black pepper cultivars cultivated in Pará State (Amazon, Brazil). Hydrodistillation and gas chromatography-mass spectrometry were employed to extract and analyze the volatile compounds, respectively. Sesquiterpene hydrocarbons were predominant (58.5-90.9%) in the cultivars "Cingapura", "Equador", "Guajarina", "Iaçará", and "Kottanadan", and "Bragantina", "Clonada", and "Uthirankota" displayed oxygenated sesquiterpenoids (50.6-75.0%). The multivariate statistical analysis applied using volatile composition grouped the samples into four groups: γ-Elemene, curzerene, and δ-elemene ("Equador"/"Guajarina", I); δ-elemene ("Iaçará"/"Kottanadan"/"Cingapura", II); elemol ("Clonada"/"Uthirankota", III) and α-muurolol, bicyclogermacrene, and cubebol ("Bragantina", IV). The major compounds in all fruit samples were monoterpene hydrocarbons such as α-pinene, ß-pinene, and limonene. Among the cultivar leaves, phenolics content (44.75-140.53 mg GAE·g-1 FW), the enzymatic activity of phenylalanine-ammonia lyase (20.19-57.22 µU·mL-1), and carotenoids (0.21-2.31 µg·mL-1) displayed significant variations. Due to black pepper's susceptibility to Fusarium infection, a molecular docking analysis was carried out on Fusarium protein targets using each cultivar's volatile components. F. oxysporum endoglucanase was identified as the preferential protein target of the compounds. These results can be used to identify chemical markers related to the susceptibility degree of black pepper cultivars to plant diseases prevalent in Pará State.

Essential Oils as Antiviral Agents. Potential of Essential Oils to Treat SARS-CoV-2 Infection: An In-Silico Investigation.

Essential oils have shown promise as antiviral agents against several pathogenic viruses. In this work we hypothesized that essential oil components may interact with key protein targets of the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A molecular docking analysis was carried out using 171 essential oil components with SARS-CoV-2 main protease (SARS-CoV-2 Mpro), SARS-CoV-2 endoribonucleoase (SARS-CoV-2 Nsp15/NendoU), SARS-CoV-2 ADP-ribose-1″-phosphatase (SARS-CoV-2 ADRP), SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp), the binding domain of the SARS-CoV-2 spike protein (SARS-CoV-2 rS), and human angiotensin-converting enzyme (hACE2). The compound with the best normalized docking score to SARS-CoV-2 Mpro was the sesquiterpene hydrocarbon (E)-ß-farnesene. The best docking ligands for SARS-CoV Nsp15/NendoU were (E,E)-α-farnesene, (E)-ß-farnesene, and (E,E)-farnesol. (E,E)-Farnesol showed the most exothermic docking to SARS-CoV-2 ADRP. Unfortunately, the docking energies of (E,E)-α-farnesene, (E)-ß-farnesene, and (E,E)-farnesol with SARS-CoV-2 targets were relatively weak compared to docking energies with other proteins and are, therefore, unlikely to interact with the virus targets. However, essential oil components may act synergistically, essential oils may potentiate other antiviral agents, or they may provide some relief of COVID-19 symptoms.

Protein Targets of Frankincense: A Reverse Docking Analysis of Terpenoids from Boswellia Oleo-Gum Resins.

Background: Frankincense, the oleo-gum resin of Boswellia trees, has been used in traditional medicine since ancient times. Frankincense has been used to treat wounds and skin infections, inflammatory diseases, dementia, and various other conditions. However, in many cases, the biomolecular targets for frankincense components are not well established. Methods: In this work, we have carried out a reverse docking study of Boswellia diterpenoids and triterpenoids with a library of 16034 potential druggable target proteins. Results:Boswellia diterpenoids showed selective docking to acetylcholinesterase, several bacterial target proteins, and HIV-1 reverse transcriptase. Boswellia triterpenoids targeted the cancer-relevant proteins (poly(ADP-ribose) polymerase-1, tankyrase, and folate receptor ß), inflammation-relevant proteins (phospholipase A2, epoxide hydrolase, and fibroblast collagenase), and the diabetes target 11ß-hydroxysteroid dehydrogenase. Conclusions: The preferential docking of Boswellia terpenoids is consistent with the traditional uses and the established biological activities of frankincense.

Natural Products as New Treatment Options for Trichomoniasis: A Molecular Docking Investigation.

Trichomoniasis, caused by the parasitic protozoan Trichomonas vaginalis, is the most common non-viral sexually-transmitted disease, and there can be severe complications from trichomoniasis. Antibiotic resistance in T. vaginalis is increasing, but there are currently no alternatives treatment options. There is a need to discover and develop new chemotherapeutic alternatives. Plant-derived natural products have long served as sources for new medicinal agents, as well as new leads for drug discovery and development. In this work, we have carried out an in silico screening of 952 antiprotozoal phytochemicals with specific protein drug targets of T. vaginalis. A total of 42 compounds showed remarkable docking properties to T. vaginalis methionine gamma-lyase (TvMGL) and to T. vaginalis purine nucleoside phosphorylase (TvPNP). The most promising ligands were polyphenolic compounds, and several of these showed docking properties superior to either co-crystallized ligands or synthetic enzyme inhibitors.

In-silico screening for anti-Zika virus phytochemicals.

Zika virus (ZIKV) is an arbovirus that has infected hundreds of thousands of people and is a rapidly expanding epidemic across Central and South America. ZIKV infection has caused serious, albeit rare, complications including Guillain-Barré syndrome and congenital microcephaly. There are currently no vaccines or antiviral agents to treat or prevent ZIKV infection, but there are several ZIKV non-structural proteins that may serve as promising antiviral drug targets. In this work, we have carried out an in-silico search for potential anti-Zika viral agents from natural sources. We have generated ZIKV protease, methyltransferase, and RNA-dependent RNA polymerase using homology modeling techniques and we have carried out molecular docking analyses of our in-house virtual library of phytochemicals with these protein targets as well as with ZIKV helicase. Overall, 2263 plant-derived secondary metabolites have been docked. Of these, 43 compounds that have drug-like properties have exhibited remarkable docking profiles to one or more of the ZIKV protein targets, and several of these are found in relatively common herbal medicines, suggesting promise for natural and inexpensive antiviral therapy for this emerging tropical disease.

Alphavirus protease inhibitors from natural sources: A homology modeling and molecular docking investigation.

Alphaviruses such as Chikungunya virus (CHIKV), O'Nyong-Nyong virus (ONNV), Ross River virus (RRV), Eastern equine encephalitis virus (EEEV), Venezuelan equine encephalitis virus (VEEV), and Western equine encephalitis virus (WEEV), are mosquito-transmitted viruses that can cause fevers, rash, and rheumatic diseases (CHIKV, ONNV, RRV) or potentially fatal encephalitis (EEEV, VEEV, WEEV) in humans. These diseases are considered neglected tropical diseases for which there are no current antiviral therapies or vaccines available. The alphavirus non-structural protein 2 (nsP2) contains a papain-like protease, which is considered to be a promising target for antiviral drug discovery. In this work, molecular docking analyses have been carried out on a library of 2174 plant-derived natural products (290 alkaloids, 664 terpenoids, 1060 polyphenolics, and 160 miscellaneous phytochemicals) with the nsP2 proteases of CHIKV, ONNV, RRV, EEEV, VEEV, WEEV, as well as Aura virus (AURV), Barmah Forest Virus (BFV), Semliki Forest virus (SFV), and Sindbis virus (SINV) in order to identity structural scaffolds for inhibitor design or discovery. Of the 2174 phytochemicals examined, a total of 127 showed promising docking affinities and poses to one or more of the nsP2 proteases, and this knowledge can be used to guide experimental investigation of potential inhibitors.

Cruzain inhibitors: efforts made, current leads and a structural outlook of new hits.

Human African trypanosomiasis and Chagas disease are the main causes of heart failure in developing countries. The disadvantages of current therapy include: undesirable side-effects, resistance, lack of efficacy on late-stage disease and lack of pediatric formulations. Efforts to find new compound hits have spanned SAR studies to very high-throughput and virtual screens and drug repurposing. The integrated analysis of these strategies on the discovery of anti-Chagas agents is timely. This work accounts for the progress on the development of cruzain inhibitors following these avenues, with emphasis on structural aspects of the ligand-cruzain recognition process.

Ligand/kappa-opioid receptor interactions: insights from the X-ray crystal structure.

During the past five years, the three-dimensional structures of 14 different G-protein coupled receptors (GPCRs) have been resolved by X-ray crystallography. The most recently published structures, those of the opioid receptors (ORs), are remarkably important in pain modulation, drug addiction, and mood disorders. These structures, confirmed previously proposed key interactions conferring potency and antagonistic properties, including the well-known interaction with Asp138, conserved in all aminergic GPCRs. In addition, crystallization of the opioid receptors highlighted the potential function of the ECL2 and ICL2 loops. We have previously reported a set of potent and selective kappa opioid receptor peptide agonists, of which ff(D-nle)r-NH2 is among the most potent and selective ones. These peptides were identified from the deconvolution of a 6,250,000 tetrapeptide combinatorial library. A derivative of this set is currently the subject of a phase 2 clinical trial in the United States. In this work, we describe comparative molecular modeling studies of kappa-OR peptide agonists with the co-crystallized antagonist, JDTic, and also report structure-activity relationships of 23 tetrapeptides. The overall binding and contact interactions are sound and interactions known to favor selectivity and potency were observed. Additional modeling studies will reveal conformational changes that the kappa-OR undergoes upon binding to these peptide agonists.

The role of imidazole in peptide cyclization by transesterification: parallels to the catalytic triads of serine proteases.

The improved bioavailability, stability and selectivity of cyclic peptides over their linear counterparts make them attractive structures in the design and discovery of novel therapeutics. In our previous work, we developed an imidazole-promoted preparation of cyclic depsipeptides in which we observed that increasing the concentration of imidazole resulted in the concomitant increase in the yield of cyclic product and reduction in dimerization, but also resulted in the generation of an acyl-substituted side product. In this work, we used transition state analysis to explore the mechanism of the imidazole-catalyzed esterification of one such peptide, Ac-SAFYG-SCH2φ, and determined the acyl substitution product to be an intermediate in a competing reaction pathway involving acyl substitution of the thioester by imidazole. Our findings indicate that imidazole plays an essential role in this side-chain to C-terminal coupling, and by extension, in transesterifications in general, through a concerted mechanism wherein imidazole deprotonates the nucleophile as the nucleophile attacks the carbonyl. The system under study is identical to the histidine-serine portion of the catalytic triads in serine proteases and it is likely that these enzymes employ the same concerted mechanism in the first step of peptide cleavage. Additionally, relatively high concentrations of imidazole must be used to effectively catalyze reactions in aprotic solvents since the overall reaction involves imidazole acting both as an acid and as a base, existing in solution as an equilibrium distribution between the neutral form and its conjugate acid.

Identification of benzoylisoquinolines as potential anti-Chagas agents.

A set of three 3-benzoyl substituted isoquinolones was synthesized in good yields and assayed for in vitro trypanocidal activity against Trypanosoma cruzi, the protozoan parasite that causes Chagas' disease. Depending on the concentration evaluated, a greater or equivalent reduction in the number of bloodborne trypomastigotes compared to that observed with benznidazole, a drug currently used to attack the parasite, was observed for two of the samples. In order to assess the potential of the 3-benzoylisoquinolone nucleus as a possible scaffold in the design of novel anti-trypanosomal lead structures, a computational analysis was performed using structural and inhibition information from both functional and target assays archived in the online database, ChEMBL. Chemical space projection of the synthesized compounds along with 3067 structures with known activities against T. cruzi shows that the isoquinolones occupy a sparsely-populated region of chemical space, indicating their potential for development as a novel class of trypanocidals. In addition, 2D and 3D structural similarity analyses revealed micromolar and submicromolar inhibitors of T. cruzi in ChEMBL with high similarity to the synthesized structures.

Consensus models of activity landscapes with multiple chemical, conformer, and property representations.

We report consensus Structure-Activity Similarity (SAS) maps that address the dependence of activity landscapes on molecular representation. As a case study, we characterized the activity landscape of 54 compounds with activities against human cathepsin B (hCatB), human cathepsin L (hCatL), and Trypanosoma brucei cathepsin B (TbCatB). Starting from an initial set of 28 descriptors we selected ten representations that capture different aspects of the chemical structures. These included four 2D (MACCS keys, GpiDAPH3, pairwise, and radial fingerprints) and six 3D (4p and piDAPH4 fingerprints with each including three conformers) representations. Multiple conformers are used for the first time in consensus activity landscape modeling. The results emphasize the feasibility of identifying consensus data points that are consistently formed in different reference spaces generated with several fingerprint models, including multiple 3D conformers. Consensus data points are not meant to eliminate data, disregarding, for example, "true" activity cliffs that are not identified by some molecular representations. Instead, consensus models are designed to prioritize the SAR analysis of activity cliffs and other consistent regions in the activity landscape that are captured by several molecular representations. Systematic description of the SARs of two targets give rise to the identification of pairs of compounds located in the same region of the activity landscape of hCatL and TbCatB suggesting similar mechanisms of action for the pairs involved. We also explored the relationship between property similarity and activity similarity and found that property similarities are suitable to characterize SARs. We also introduce the concept of structure-property-activity (SPA) similarity in SAR studies.

Mapping the protein interaction network in methicillin-resistant Staphylococcus aureus.

Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13,219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets.

Quinoline alkaloids as intercalative topoisomerase inhibitors.

Quinoline alkaloids are abundant in the Rutaceae, and many have exhibited cytotoxic activity. Because structurally related antitumor alkaloids such as camptothecin and fagaronine are known to function as intercalative topoisomerase poisons, it is hypothesized that cytotoxic Stauranthus alkaloids may also serve as intercalative topoisomerase inhibitors. To test this hypothesis theoretically, ten Stauranthus quinoline alkaloids were examined for potential intercalation into DNA using a molecular docking approach. Four of the alkaloids (stauranthine, skimmianine, 3',6'-dihydroxy-3',6'-dihydrostauranthine, and trans-3',4'-dihydroxy-3',4'-dihydrostauranthine) were able to intercalatively dock consistently into DNA. In order to probe the intermolecular interactions that may be responsible for intercalation of these quinoline alkaloids, density functional calculations have been carried out using both the B3LYP and M06 functionals. M06 calculations indicated favorable pi-pi interactions between either skimmianine or stauranthine and the guanine-cytosine base pair. Furthermore, the lowest-energy face-to-face orientation of stauranthine with guanine is consistent with favorable dipole-dipole orientations, favorable electrostatic interactions, and favorable frontier molecular orbital interactions. Likewise, the lowest-energy face-to-face orientation of stauranthine with the guanine-cytosine base pair reveals favorable electrostatic interactions as well as frontier molecular orbital interactions. Thus, not only can quinoline alkaloids dock intercalatively into DNA, but the docked orientations are also electronically favorable.

Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors.

UNLABELLED: Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. AVAILABILITY: THE SOURCE CODE AND EXECUTABLE PROGRAM OF THE HUB CLASSIFIER ARE AVAILABLE FOR DOWNLOAD AT: http://www.cnbi2.ca/hub-analysis/

The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks.

BACKGROUND: Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions. RESULTS: A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species. CONCLUSION: The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.