In Silico Screening and Molecular Dynamics Simulations against Tyrosine-protein Kinase Fyn Reveal Potential Novel Therapeutic Candidates for Bovine Papillomatosis.

BACKGROUND: Decreased beef productivity due to papillomatosis has led to the development and identification of novel targets and molecules to treat the disease. Protein kinases are promising targets for the design of numerous chemotherapy drugs. OBJECTIVE: This study aimed to screen and design new inhibitors of bovine Fyn, a protein kinase, using structure-based computational methods, such as molecular docking and molecular dynamics simulation (MDS). METHODS: To carry out the molecular docking analysis, five ligands obtained through structural similarity between active compounds along with the cross-inhibition function between the ChEMBL and Drugbank databases were used. Molecular modeling was performed, and the generated models were validated using PROCHECK and Verify 3D. Molecular docking was performed using Autodock Vina. The complexes formed between Fyn and the three best ligands had their stability assessed by MDS. In these simulations, the complexes were stabilized for 100 ns in relation to a pressure of 1 atm, with an average temperature of 300 k and a potential energy of 1,145,336 kJ/m converged in 997 steps. RESULTS: Docking analyses showed that all selected ligands had a high binding affinity with Fyn and presented hydrogen bonds at important active sites. MDS results support the docking results, as the ligand showed similar and stable interactions with amino acids present at the binding site of the protein. In all simulations, sorafenib obtained the best results of interaction with the bovine Fyn. CONCLUSION: The results highlight the identification of possible bovine Fyn inhibitors; however, further studies are important to confirm these results experimentally.

Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation.

Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.

Fast and Efficient Method to Obtain Tagitinin F by Photocyclization of Tagitinin C.

There is some evidence in the literature of the photocyclization reaction of Tagitinin C (1) to Tagitinin F (2). Compound 2 has high pharmacological potential, but it is not easy to obtain, while compound 1 is easily obtained from a widespread plant, Tithonia diversifolia. Among different reaction conditions monitored, one was found that allowed the cyclization of 1 into 2 in <15 min in a photo-dependent reaction. Scaling-up the photocyclization of the pure compound 1 into 2 demonstrated 100% yield, and the isolation of 2 from a UV-irradiated extract was eight-fold higher than the quantity isolated from the non-UV-irradiated extract. We were also able to better understand the process of photoconversion and determine methods to isolate and quantify these compounds, which are known for their important antitumoral activities among other important pharmacological properties.

Search for Potential Inducible Nitric Oxide Synthase Inhibitors with Favorable ADMET Profiles for the Therapy of Helicobacter pylori Infections.

BACKGROUND: Helicobacter pylori is a gram-negative bacterium related to chronic gastritis, peptic ulcer and gastric carcinoma. During its infection process, promotes excessive inflammatory response, increasing the release of reactive species and inducing the production of pro-inflammatory mediators. Inducible Nitric Oxide Synthase (iNOS) plays a crucial role in the gastric carcinogenesis process and a key mediator of inflammation and host defense systems, which is expressed in macrophages induced by inflammatory stimuli. In chronic diseases such as Helicobacter pylori infections, the overproduction of NO due to the prolonged induction of iNOS is of major concern. OBJECTIVES: In this sense, the search for potential iNOS inhibitors is a valuable strategy in the overall process of Helicobacter pylori pathogeny. METHODS: In silico techniques were applied in the search of interesting compounds against Inducible Nitric Oxide Synthase enzyme in a chemical space of natural products and derivatives from the Analyticon Discovery databases. RESULTS: The five compounds with the best iNOS inhibition profile were selected for activity and toxicity predictions. Compound 9 (CAS 88198-99-6) displayed significant potential for iNOS inhibition, forming hydrogen bonds with residues from the active site and an ionic interaction with heme. This compound also displayed good bioavailability and absence of toxicity/or from its probable metabolites. CONCLUSION: The top-ranked compounds from the virtual screening workflow show promising results regarding the iNOS inhibition profile. The results evidenced the importance of the ionic bonding during docking selection, playing a crucial role in binding and positioning during ligand-target selection for iNOS.

Computational Approaches in Multitarget Drug Discovery.

Current therapeutic strategies entail identifying and characterizing a single protein receptor whose inhibition is likely to result in the successful treatment of a disease of interest, and testing experimentally large libraries of small molecule compounds "in vitro" and "in vivo" to identify promising inhibitors in model systems and determine if the findings are extensible to humans. This highly complex process is largely based on tests, errors, risk, time, and intensive costs. The virtual computational study of compounds simulates situations predicting possible drug linkages with multiple protein target atomic structures, taking into account the dynamic protein inhibitor, and can help identify inhibitors efficiently, particularly for complex drug-resistant diseases. Some discussions will relate to the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. Some authors have proposed a virtual drug discovery that not only identifies efficient inhibitors but also helps to minimize side effects and toxicity, thus increasing the likelihood of successful therapies. This chapter discusses concepts and research of bioactive multitargets related to toxicology.

SistematX, an Online Web-Based Cheminformatics Tool for Data Management of Secondary Metabolites.

The traditional work of a natural products researcher consists in large part of time-consuming experimental work, collecting biota to prepare and analyze extracts and to identify innovative metabolites. However, along this long scientific path, much information is lost or restricted to a specific niche. The large amounts of data already produced and the science of metabolomics reveal new questions: Are these compounds known or new? How fast can this information be obtained? To answer these and other relevant questions, an appropriate procedure to correctly store information on the data retrieved from the discovered metabolites is necessary. The SistematX (http://sistematx.ufpb.br) interface is implemented considering the following aspects: (a) the ability to search by structure, SMILES (Simplified Molecular-Input Line-Entry System) code, compound name and species; (b) the ability to save chemical structures found by searching; (c) compound data results include important characteristics for natural products chemistry; and (d) the user can find specific information for taxonomic rank (from family to species) of the plant from which the compound was isolated, the searched-for molecule, and the bibliographic reference and Global Positioning System (GPS) coordinates. The SistematX homepage allows the user to log into the data management area using a login name and password and gain access to administration pages. In this article, we introduced a modern and innovative web interface for the management of a secondary metabolite database. With its multiplatform design, it is able to be properly consulted via the internet and managed from any accredited computer. The interface provided by SistematX contains a wealth of useful information for the scientific community about natural products, highlighting the locations of species from which compounds are isolated.

The Correlation between Chemical Structures and Antioxidant, Prooxidant, and Antitrypanosomatid Properties of Flavonoids.

Flavonoids have demonstrated in vivo and in vitro leishmanicidal, trypanocidal, antioxidant, and prooxidant properties. The chemotherapy of trypanosomiasis and leishmaniasis lacks efficacy, presents high toxicity, and is related to the development of drug resistance. Thus, a series of 40 flavonoids were investigated with the purpose of correlating these properties via structure and activity analyses based on integrated networks and QSAR models. The classical groups for the antioxidant activity of flavonoids were combined in order to explain the influence of antioxidant and prooxidant activities on the antiparasitic properties. These analyses become useful for the development of efficient treatments for leishmaniasis and trypanosomiasis. Finally, the dual activity of flavonoids presenting both anti- and prooxidant activities revealed that the existence of a balance between these two features could be important to the development of adequate therapeutic strategies.

Outstanding Anti-inflammatory Potential of Selected Asteraceae Species through the Potent Dual Inhibition of Cyclooxygenase-1 and 5-Lipoxygenase.

Cyclooxygenase and 5-lipoxygenase are enzymes that catalyze important inflammatory pathways, suggesting that dual cyclooxygenase/lipoxygenase inhibitors should be more efficacious as anti-inflammatory medicines with lower side effects than the currently available nonsteroidal anti-inflammatory drugs. Many plants from the family Asteraceae have anti-inflammatory activities, which could be exerted by inhibiting the cyclooxygenase-1 and 5-lipoxygenase enzymes. Nevertheless, only a small number of compounds from this family have been directly evaluated for their ability to inhibit the enzymes in cell-free assays. Therefore, this study systematically evaluated 57 Asteraceae extracts in vitro in enzyme activity experiments to determine whether any of these extracts exhibit dual inhibition of cyclooxygenase-1 and 5-lipoxygenase. The chemical profiles of the extracts were obtained by the high-performance liquid chromatography-ultraviolet-diode array detector method, and their major constituents were dereplicated. Of the 57 tested extracts, 13 (26.6 %, IC50 range from 0.03-36.2 µg/mL) of them displayed dual inhibition. Extracts from known anti-inflammatory herbs, food plants, and previously uninvestigated species are among the most active. Additionally, the extract action was found to be specific with IC50 values close to or below those of the standard inhibitors. Thus, the active extracts and active substances of these species are potent inhibitors acting through the mechanism of dual inhibition of cyclooxygenase-1 and 5-lipoxygenase. The extracts were prepared for this study using nontoxic extraction solvents (EtOH-H2O), requiring only a small amount of plant material to carry out the bioassays and the phytochemical analyses. In summary, this study demonstrated the potential of the investigated species as dual inhibitors, revealing their potential as pharmaceuticals or nutraceuticals.

Prediction of Anti-inflammatory Plants and Discovery of Their Biomarkers by Machine Learning Algorithms and Metabolomic Studies.

Nonsteroidal anti-inflammatory drugs are the most used anti-inflammatory medicines in the world. Side effects still occur, however, and some inflammatory pathologies lack efficient treatment. Cyclooxygenase and lipoxygenase pathways are of utmost importance in inflammatory processes; therefore, novel inhibitors are currently needed for both of them. Dual inhibitors of cyclooxygenase-1 and 5-lipoxygenase are anti-inflammatory drugs with high efficacy and low side effects. In this work, 57 leaf extracts (EtOH-H2O 7 : 3, v/v) from Asteraceae species with in vitro dual inhibition of cyclooxygenase-1 and 5-lipoxygenase were analyzed by high-performance liquid chromatography-high-resolution-ORBITRAP-mass spectrometry analysis and subjected to in silico studies using machine learning algorithms. The data from all samples were processed by employing differential expression analysis software coupled to the Dictionary of Natural Products for dereplication studies. The 6052 chromatographic peaks (ESI positive and negative modes) of the extracts were selected by a genetic algorithm according to their respective anti-inflammatory properties; after this procedure, 1241 of them remained. A study using a decision tree classifier was carried out, and 11 compounds were determined to be biomarkers due to their anti-inflammatory potential. Finally, a model to predict new biologically active extracts from Asteraceae species using liquid chromatography-mass spectrometry information with no prior knowledge of their biological data was built using a multilayer perceptron (artificial neural networks) with the back-propagation algorithm using the biomarker data. As a result, a new and robust artificial neural network model for predicting the anti-inflammatory activity of natural compounds was obtained, resulting in a high percentage of correct predictions (81 %), high precision (100 %) for dual inhibition, and low error values (mean absolute error = 0.3), as also shown in the validation test. Thus, the biomarkers of the Asteraceae extracts were statistically correlated with their anti-inflammatory activities and can therefore be useful to predict new anti-inflammatory extracts and their anti-inflammatory compounds using only liquid chromatography-mass spectrometry data.

Encontro de Rhabditis sp em alface Lactuca sativa comercializada em Anápolis, Goiás, Brasil / Finding of Rhabditis sp. in lettuce Lactuca sativa commercialized in Anápolis, GO, Brazil

O consumo de vegetais in natura é recomendado como alimentação saudável em razão de seuconsiderável percentual de vitaminas, sais minerais e fibras alimentares. Frequentemente as verduras são adubadas com dejetos ou irrigadas com águas contaminadas com matéria fecal. Em razão da ocorrência de enteroparasitos em consumidores de hortaliças, avaliou-se, por meio deste trabalho, a presença de parasitos em alfaces hidropônicas e convencionais comercializadas na cidade de Anápolis-GO. Utilizado o método de sedimentação espontânea, foram encontradas larvas de nematoides do gênero Rhabditis. Neste gênero, há espécies de vida livre que, eventualmente, podem parasitar o homem e animais domésticos. Diante dessas evidências, recomenda-se a higienização adequada destes alimentos tanto pelos consumidores domésticos como pelos manipuladores de hortaliças a serem servidas em restaurantes e lanchonetes. Trata-se do primeiro relato de Rhabditis em hortaliças no Brasil.