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In silico approaches for Mycoplasma pneumoniae multi-epitope vaccine construction

In silico approaches for Mycoplasma pneumoniae multi-epitope vaccine construction

Thaís Cristina Vilela Rodrigues, Sandeep Tiwari, Vasco Ariston de Carvalho Azevedo, Rodrigo Bentes Kato, Stephane Fraga de Oliveira Tosta and Siomar de Castro Soares

Pneumonia is a serious health problem with global effects, being the death cause of over one million people annually. Among the main microorganisms responsible by pneumonia, Mycoplasma pneumoniae is one of the most common, with a significant increase in the last years. The vaccines are fundamental in diseases prevention besides to considerably avoid the need of health services and funding resources. In this way, the proposal of the present study is to construct through immunoinformatic tools, a multi-epitope vaccine against M. pneumoniae. Multi-epitope vaccines are constituted by epitopes properly selected to induce targeted immune responses and avoid adverse reactions. First the core proteins were previously determined through reverse vaccinology, then the search for MHCI, MHCII and B epitopes were performed as well as the check for overlapping epitopes, capable to induce both humoral and cellular responses. Those epitopes were filtered according to their immunogenicity, population coverage, among others. The final epitopes were joined with heat-labile enterotoxin from Escherichia coli as adjuvant and the structure of the vaccine was predicted. The vaccine was considered physically stable, non-toxic, non-allergen, not significantly similar to human proteome and with appropriate antigenic and immunogenic properties. The molecular docking of the vaccine with the Toll-Like Receptor 2 was performed as well as the dynamic simulation to ensure the affinity and stability between this complex. In silico cloning was tested in an expression vector with positive results. In addition, the immune simulation for vaccine efficacy will be test. Through immunoinformatic approaches we constructed an effective multi-epitope vaccine candidate, that with further tests could contribute to prevention of pneumonia in a massive scale. Besides that, the study assists to better understanding of the immune mechanisms regarding M. pneumoniae infections and its interaction with the host.

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DRUG-GENE EXPRESSION PROFILES AND SYSTEMS BIOLOGY APPROACH TO IDENTIFY REPURPOSED DRUG CANDIDATES FOR TARGETING SCLEROSTIN IN PERI-IMPLANTITIS DISEASE.

DRUG-GENE EXPRESSION PROFILES AND SYSTEMS BIOLOGY APPROACH TO IDENTIFY REPURPOSED DRUG CANDIDATES FOR TARGETING SCLEROSTIN IN PERI-IMPLANTITIS DISEASE.

Dr.Pradeep Kumar Yadalam

Successful identification of a therapeutic strategy to treat patients with periimplantitis remains extremely important as post-implant bone degradation leads to implant failure and extreme bone loss. Given that the establishment of a new drug is quite expensive and time-consuming, the drug repurposing approach has come in handy. It helps to identify the experimental drugs that are beyond the purview of the initial clinical indication. In our current study, we propose a three-step drug repurposing approach in treating peri-implant bone defects and investigating the action of the FDA approved drugs to inhibit the key protein Sclerostin, involved in bone degradation. As the preliminary step, we differentiated the gene expression pattern in periimplantitis and dentate patients with their drug-induced profiles to identify the primary lead candidates. As the second step, we employed the computational biology approach to evaluate the protein-drug interaction and segregate the best hits among the identified lead compounds for sclerostin. Finally, the mode of action network for each candidate is established with the help of literature support, and the drug enrichment and pathway analysis are performed on the target genes in the network to evaluate the drug efficacy. This approach provided us with a drug interaction profile and specific genes and biomarkers to target bone mineralization in peri-implantitis. Thus, our three-step drug repurposing method is consistent with identifying the drug molecules with high efficacy and developing an efficient therapeutic strategy to treat peri-implantitis.

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VIRTUAL SCREENING OF SUBSTANCES WITH POTENTIAL ANTIVIRAL ACTIVITY AGAINST THREE FLAVIVIRUSES: dengue virus, yellow fever virus and Zika virus

VIRTUAL SCREENING OF SUBSTANCES WITH POTENTIAL ANTIVIRAL ACTIVITY AGAINST THREE FLAVIVIRUSES: dengue virus, yellow fever virus and Zika virus

Mateus Serafim, Thales Kronenberger, Rafael Rocha, Rafaela Ferreira, Vinícius Maltarollo, Bruno Mota and Erna Kroon

Approximately three billion people live in regions at risk of infections by flaviviruses. Dengue virus (DENV), Zika virus (ZIKV) and Yellow fever virus (YFV) presents outbreaks and severe complications. Currently, there are no antivirals available to treat these diseases. We screened and evaluated the potential antiviral activity of small molecules against these viruses, targeting the viral protease NS2B-NS3 (NS3PRO). We used a combination of HQSAR models and structural molecular modelling, based on structures of peptidomimetic DENV-3 NS3PRO inhibitors and molecular docking studies to screen for new compounds. Binding sites of DENV-3 and ZIKV NS3PRO were assessed to build a pharmacophoric model for virtual screening. Hits were selected after molecular dynamics simulations, with predictions of toxicity and biological activity. Biological activities were evaluated by the MTT assay. Antiviral activity was evaluated by plaque reduction, pre-treatment and virucide activity assays. Enzymatic inhibition assays against ZIKV NS3PRO were carried out. An optimal HQSAR model (q2 = 0.67; r2 = 0.87) was selected. A virtual screening of ~7,600,000 compounds was conducted (pharmacophore, docking and molecular dynamics), identifying eight potential inhibitors to the NS3PRO, with favorable biological activity (5/8) and toxicity (8/8) predictions. Five were active against ZIKV, YFV, DENV-2 or DENV-3 (EC50 from 4.21 ± 0.14 to 37.51 ± 0.8 µM, with selective indexes from 1.42 to 3.74), with one being active against all viruses. In plaque reduction assays, two substances reduced about 1.0 to 1.5 log10 of the viral titer of ZIKV, YFV and DENV-2. One also reduced about 1.0 log10 of YFV titer in pre-treatment assays. We have identified five compounds with antiviral activity, with one showing a potential panflavivirus activity. Preliminary ZIKV NS3PRO inhibition assays showed three active compounds with IC50 values between 28 and 69 µM.

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CONSTRUCTION OF A NANOPARTICLE BASED ON A SYNTHETIC VIRUS-LIKE PROTEIN WITH CHEMOTHERAPY POTENTIAL

CONSTRUCTION OF A NANOPARTICLE BASED ON A SYNTHETIC VIRUS-LIKE PROTEIN WITH CHEMOTHERAPY POTENTIAL

Amanda Patrícia Gonçalves, Karoline Hellen Madureira de Melo, Daniela Aparecida Silva, Marcela de Sá Hauck, Mariá Aparecida Braga Rocha E Oliveira, Isabela Malo Lopes, Gabriela Pereira Paschoalini, José Ésio Bessa Ramos Junior, Renko de Vries and Anésia Aparecida dos Santos

Cancer is a devastating disease whose treatment tends to be very aggressive due to its side effects and low selectivity. Nanotechnology has emerged as an alternative in medicine, especially in cancer treatments. In this case, molecular tools can be used to enhance chemotherapy delivery-drugs nanoparticles, making them more selective. DNA molecules have been suggested as a great material for nano-constructions once it can be associated with some chemotherapy molecules such as doxorubicin and cisplatin. In 2014, Hernandez-Garcia and colleagues designed the C4S10K12 protein, a synthetic viral coat protein which self-assembles with dsDNA molecules forming rod-shaped virus-like nanoparticles. Based on these insights, we designed a biopolymeric doxorubicin-carrier nanoparticle coated by the C4S10K12 protein and evaluated its stability in physiological conditions as well its internalization, cytotoxicity and selectivity on murine melanoma tumor cells lines. Through non-denaturing electrophoresis we demonstrated that DNA molecules remain intact in physiological conditions and can tolerate the action of DNAse enzyme. Fluorescence Microscopy showed that the constructed nanoparticle can enter melanoma murine tumor cells after 1 hour of treatment and release its content inside those cells after 12 hours. This controlled and delayed release caused an increase in doxorubicin cytotoxicity when compared to non encapsulated-doxorubicin treated cells, which was demonstrated through MTT assays. These experiments also showed that the DNA-Doxorubicin complex coated by C4S10K12 was more toxic to tumor cells than to non tumor cells, which did not occur in non encapsulated-doxorubicin treatment. These results show that our construction is a stable nanoparticle capable of entering tumor cells in vitro, triggering increased cytotoxicity and selectivity. These features demonstrate that these nanoparticles have a high potential for chemotherapy and open new perspectives to study drug-targeting in tumor microenvironments.

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WARFARIN DOSE PREDICTION THROUGH A USER INTERFACE USING CLINICAL, DEMOGRAPHIC AND PHARMACOGENETIC DATA

WARFARIN DOSE PREDICTION THROUGH A USER INTERFACE USING CLINICAL, DEMOGRAPHIC AND PHARMACOGENETIC DATA

Jennifer Eliana Montoya Neyra, Júlia Soler and Paulo Santos

We work on a user interface (UI) designed to assist in warfarin therapy by predicting a best therapeutic dose, calculated from the data entered into the UI. It will be able to predict more accurate doses for patients diagnosed with atrial fibrillation, stroke, thrombosis or heart valve prosthesis in whom it is desired to maintain an international normalized ratio (INR) between two and three, using their clinical, demographic and pharmacogenetic data. The prediction models that were considered for the construction of this prediction interface were the International Warfarin Pharmacogenetics Consortium (IWPC), multiple linear regression, regression using regularizers (Lasso regression, Ridge regression), Elastic net regression, regression of selected variables by AIC, Ridge Regression with Variable Selection (foba package in R) and a simple neural network model that consists of 3 hidden layers of 100 neurons each, using data from patients of the Brazilian Heart Institute (InCor – USP). This data include clinical, demographic, and pharmaceutical factors and and risk genotypes of the cytochrome P450 2C9 (CYP2C9), vitamin K epoxy reductase (VKORC1), leukotriene B(4) omega-hydroxylase 1 (CYP4F2) and NAD(P)H dehydrogenase (quinone) 1 (NQO1) genes. The models were trained with the information of 614 individuals, which reached INR values between 2 and 3 when receiving a maintenance dose of warfarin, and tested in a subset of 152 patients. To evaluate the accuracy of the models, the mean absolute error (MAE), root-mean-square error (RMSE) and R-squared were calculated. The best adjusted model was the Ridge Regression with Variable Selection, which obtained the best performance when analyzing both the training group (MAE = 7.54, RMSE = 0.993, R-squared = 0.296) and the evaluation group (MAE = 0.766, RMSE = 1.07, R-squared = 0.282). This tool is still under development, but we have great expectations about its applicability and usefulness for patients who require it.

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HTP SurflexDock 1.2: Improving SBVS campaign by including the post-processing stage

HTP SurflexDock 1.2: Improving SBVS campaign by including the post-processing stage

João Luiz De Almeida Filho and Jorge H. Fernandez

Structure-Based Virtual Screening (SVBS) is an essential tool that may be used to delimit a sub-set of the more specific inhibitors for a receptor of interest during the early stages of drug discovery studies. We developed the HTP SurFlexDock, a web server that improves SBVS campaigns by the use of ensemble docking pipeline in order to simulate the protein receptor flexibility. However, like other SVBS tools, HTP SurflexDock uses a scoring function based on the ΔG of the best pose to classify the compounds. This function is subject to enrich poses with unnatural artifacts such as improper ligand torsions and malformed hydrogen bonds, among others. In this sense, we include a post-processing phase in the HTP SurflexDock, where the user can select up to 10 promising compounds from the initial classification to boost the exploratory of the active site conformational space. At this stage, the user is presented with up to 30 more poses per complex using AutoDock 4.2. Through qualitative analysis of the three-dimensional interactions of the obtained complexes in ensemble docking, the users takes a better picture of the sub-set of the compounds with better interactions and consequently choose the compounds that will go to future stages of the nest drug discovery experiments with greater fidelity. The HTP SurFlexDock is freely available as a web service or download at http://biocomp.uenf.br:81.

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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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NETWORK OF POSSIBLE TARGETS WITH CLINICAL-PHARMACOLOGICAL POTENTIAL AROUND THE COMPOUNDS IDENTIFIED IN SYZYGIUM CUMINI

NETWORK OF POSSIBLE TARGETS WITH CLINICAL-PHARMACOLOGICAL POTENTIAL AROUND THE COMPOUNDS IDENTIFIED IN SYZYGIUM CUMINI

Victor Hugo Oliveira de Andrade, Heitor Cappato Guerra Silva and Foued Salmen Espindola

Originally from Asia, Syzygium cumini is part of the Myrtaceae family and is currently part of the Brazilian Cerrado. This plant is gaining notoriety through the potential clinical-pharmacological effects of extracts made from its parts and compounds identified in its composition. A comparative analysis of the 2D and 3D structure of the compounds allows its chemical association with possible biological targets involved in metabolic pathways related to diseases. Besides, an investigation of the relationship between the targets helps to elucidate some functions to establish a priority for the compounds according to the degree of involvement in metabolic pathways. To enhance the biodiversity of the Cerrado and present a range of biotechnologically interesting S. cumini compounds, the molecular structures of the compounds were collected in the Pubchem database and prepared in the molecular modeling software VIDA 4.4.0. These compounds were inserted in the Swisstargetprediction platform, which searches 2D and 3D similarity targets from a small molecule. The interaction between the targets was analyzed in the STRING 11.0 platform and the degree of involvement with the metabolic pathways included in the DAVID 6.8 platform. As a result, a network of interactions was prepared with the help of the Cytoscape 3.8.0 software, thus gathering valuable information for potential drug research. The results of the correlations made between the compounds and the metabolic pathways indicated influence in diseases such as lung cancer, bladder and breast cancer, chronic obstructive pulmonary disease, and among others. This work will facilitate the evolution of studies involving these diseases and will also provoke the search for new effects for the extracts made from the parts of S. cumini.

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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.