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NETWORK PHARMACOLOGY OF ANNONA CRASSIFLORA ALKALOIDAL FRACTION ON ALZHEIMER’S AND ITS EFFECT ON DROSOPHILA MELANOGASTER MODEL

NETWORK PHARMACOLOGY OF ANNONA CRASSIFLORA ALKALOIDAL FRACTION ON ALZHEIMER’S AND ITS EFFECT ON DROSOPHILA MELANOGASTER MODEL

Heitor Cappato Guerra Silva, Serena Mares Malta, Alice Norberto de Carvalho, Luiz Gabriel Alves Santos, Natieli Saito, Carlos Ueira-Vieira and Foued Salmen Espindola

From an alkaloid fraction already identified in a semi-purification of Annona crassiflora previously, a specific enzymatic inhibition was shown. And to harness the full potential of the alkaloid fraction, a network approach was then used. Thus, this work aims to search possible human targets for these alkaloids, and from the targets found evaluate the effect of the alkaloid fraction on the Alzheimer model and predict its pathways of action. Swisstargetprediction and targetnet platforms were used for predicting targets. After the interaction between these targets was predicted with STRING 11.0, the analysis of the interactions to elucidate potential diseases that may be affected was done with DAVID 6.8 platform. All network preparation was done with Cytoscape 3.8.0 software. One of the predicted diseases was Alzheimer’s and as cholinesterase inhibitors are currently the main treatment for Alzheimer’s, and cholinesterase was a predicted target, I first confirmed that the alkaloid fraction had this activity in an in vitro enzyme assay. With this confirmed, I used the genotype Drosophila melanogaster, which expresses human APP and BACE, generating beta-amyloid, to test the alkaloid fraction by evaluating its motor function intervention with behavioral tests and acetylcholinesterase activity in vivo as well. I observed an improvement in motor behavior and a decrease in acetylcholinesterase activity in vivo and in vitro. After that, we evaluated which other pathways could be affected in drosophila and the impact on Alzheimer’s, we made a network with drosophila targets using the DRSC prediction tool – Integrative Ortholog, and with these new networks, we showed other pathways related to Alzheimer’s, such as inflammation and oxidative stress. To conclude, these results confirmed acetylcholinesterase as a target and showed a perspective of a potential fraction that can participate in distinct pathways, and then be used for further studies for Alzheimer’s.

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IN-SILICO ANALYSIS OF THE STRUCTURE AND BINDING SITE FEATURES OF THE 3CL PROTEASE FROM SARS-COV-2: PARAMETERIZATION FOR VIRTUAL SCREENING PROTOCOLS

IN-SILICO ANALYSIS OF THE STRUCTURE AND BINDING SITE FEATURES OF THE 3CL PROTEASE FROM SARS-COV-2: PARAMETERIZATION FOR VIRTUAL SCREENING PROTOCOLS

Maria Eduarda Alves Esteves, Tácio Vinício Amorim Fernandes and Manuela Leal da Silva

The new SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2) emerged at the end of 2019 as a global emergency. Due to its high rate of transmission and the absence of specific treatment or vaccine, around 1 million people over the world have died, according to World Health Organization until October 2020. Nowadays, thousands of people still get infected every day and many of them do not survive due to the complications of the disease associated with the acute respiratory syndrome. Thus, once the pharmacological therapy has shown to be deficient because of its non-specificity, this work intends to conduct an in silico research for possible drugs and bioactive substances, including those belonging to Brazilian biodiversity, that can act as inhibitors of the main viral protease (3CLpro) for the treatment of COVID-19.
In this work, the prediction of the amino acid residues’ pKa of the receptor protein (PDBid: 6XQT) through the PDB2PQR server and the selection of the ionizable residues’ protonation probable state of the 3CLpro three-dimensional structure using the pdb2gmx module were performed as parameterization methods. The anchorage site of the ligands was delimited by the grid center x, y, z: -11, 1, 45 and size x, y, z: 32, 35, 33, respectively, involving the catalytic dyad His41 and Cys145. In the redocking stage, the exhaustiveness of 8, 16, 32, 64 and 100 were tested, with the result of less exhaustiveness being selected with the affinity calculated by Autodock Vina equal to -10.4 kcal/mol. In this step it was possible to obtain an RMSD (Root Mean Square deviation) of 0.97 Å between the original ligand of the crystal and the first model generated from the docking. It was possible to stipulate through the performed methodology the parameters for the next stage of virtual screening, whose results are under analysis.

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Identification of potential molecular targets related to cancer for the formicamycin’s family

Identification of potential molecular targets related to cancer for the formicamycin’s family

Bruna Schuck de Azevedo and Rafael Andrade Caceres

According to the Global Cancer Observatory, 18 million new cases and 9.5 million deaths were estimated for all types of cancer in 2018. The World Health Organization predicts that in 2030 there will be a 70% increase in new cases and 45% in deaths. Due to the rise of cancer incidence and mortality, it is necessary to invest in the discovery and development of new antineoplastic drugs. The novel family of molecules called formicamycin, active against some antibiotic-resistant microorganisms, had a tyrosine kinase enzyme predicted as one of its molecular targets. As this enzyme plays a role in the progression of cancer, the potential antineoplastic action of the formicamycins has been studied. In order to identify the potential molecular targets for an antineoplastic action of the compounds of the formicamycin family, a reverse virtual screening (RVS) was performed using two web servers, PharmMapper and SwissTargetPrediction, to establish the potential targets which interact with them. The targets obtained concomitantly on both servers had their influence on carcinogenesis verified through a literature review in PubMed. The binding energy between target and compound was determined for the targets that seemed to influence carcinogenesis through simulations of molecular docking, with Autodock 4.2 and Autodock Vina, and molecular dynamics, with the GROMACS v.4.6.7 package. Fifteen potential molecular targets were obtained at the intersection of the two RVS servers used. In the literary review, twelve of them were associated with carcinogenesis. These twelve molecular targets were subjected to molecular docking and molecular dynamics simulations. At the end of the RVS process, three potential molecular targets for the formicamycins were identified. Among these macromolecules, nuclear receptor subfamily 1 group I member 2 and matrix metalloproteinase 3 are the most promising targets for an antineoplastic action of these compounds.

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Interactome of Corynebacterium ulcerans toxigenic strains reveals hub proteins being potential drug targets

Interactome of Corynebacterium ulcerans toxigenic strains reveals hub proteins being potential drug targets

Gustavo Andrew Mahon Mendes Pereira, Luis Felipe de Morais Melo, Luis Carlos Guimarães, Vasco Ariston de Carvalho Azevedo and Edson Luiz Folador

Corynebacterium ulcerans has toxigenic strains that produce the diphtheria toxin similar to C. diphtheriae. Among the bacteria causing diphtheria, C. ulcerans has a greater mutagenic potential because it has both humans and animals as reservoirs. Being reemergent, there was an increase in cases even in immunized countries, requiring new approaches for new drug targets selection. Applying the interolog mapping method we map interactions with confidence score >= 700 from 5090 STRING database organisms, generating the protein-protein interaction network and identifying 22,347 interactions conserved in 10 toxigenic C. ulcerans strains. Selecting by highest degree interaction, 457 hub proteins were identified, 421 (92.12%) of them have the essentiality validated by homology in the Database of Essential Genes (DEG) and 36 (7.88%) were considered essential after functional and enrichment analysis. The Clusters of Orthologous Groups (COG) analysis highlighted the more representative groups: “Translation, ribosomal structure and biogenesis (J)” (74%), “Amino acid transport and metabolism (E)” (13.96%), “Replication, recombination and repair (L)” (8.21%) and only 5.34% “Function Unknown (S)” composed mostly of hypothetical proteins. The Gene Ontology (GO) enrichment analysis identified the most significant biological processes (p>0.95): “Cell redox homeostasis”, “DNA recombination”, “Cell wall organization”, “SOS response”, among others. Aiming to select targets do not favoring toxicity, we identified 351 (76.8%) non-host homologous hub proteins, some having higher degree interaction are: “Inosine 5-monophosphate dehydrogenase” (195, CulFRC58_1614), “Protein RecA” (182, recA), “DNA-directed RNA polymerase subunit alpha” (165, rpoA), “2-oxoglutarate dehydrogenase E1 component” (156, odhA) and “DNA-directed RNA polymerase subunit beta” (154, rpoB). All non-host homologous hub proteins possess potential for drug targets and are useful to evaluate the affinity of candidate compounds, experimentally or, similarly that our group performed in-silico affinity test against unpublished synthetic derivatives of tetraisoquinoline alkaloids.