Daniela de Melo Resende
CiPharma-Universidade Federal de Ouro Preto, Brazil
Daniela de Melo Resende graduated in Veterinary Medicine from the Federal University of Lavras (2000), master's degree in Veterinary Medicine from the Federal University of Viçosa (2003) and doctorate by the Federal University of Minas Gerais, at the Institute of Biological Sciences in the Department of Biochemistry and Immunology (2008) . Conducted postdoctoral studies at the Cellular and Molecular Immunology Laboratory at the René cracked Institute (Fiocruz / MG). It is currently a researcher at the National Postdoctoral Program (PNPD-CAPES) Post-Graduate Program in Pharmaceutical Sciences (Cipharma) of the Federal University of Ouro Preto (UFOP). Has experience in the area of Immunology, with emphasis on development of vaccines against parasites, acting on the following topics: immunohistochemistry, apoptosis, leishmaniasis, humoral and cellular immunology, cell culture, zoonosis, construction of recombinant vectors and bioinformatics (reverse vaccinology, assembly and . annotation of bacterial genomes)
Immunoinformatics is an innovative strategy for selection of targets for vaccine and diagnostics with reduced time and costs. Data mining of essential sequences for eliciting protective immune responses through immunoinformatics has been used for indicating good vaccine candidatesfor Neisseria meningitides and Staphylococcus aureus showing the efficacy of this approach. It was also shown that instrinsically disorded proteins play important role in trypanosomatids virulence. Our hypothesis is that protein disordered regions could be related to immunogenic epitopes facilitating their exposure to the immune system. In this work, we developed a computational approach that integrates: a) T and B cell epitope predictors, namely: NetCTL and NetMHC for T CD8+ epitope prediction; NetMHCII for T CD4+ epitope prediction; and BepiPred for B cell epitope prediction; and b) structural disorder predictors, namely: DisEMBL, IUPred, GlobPipe and VSL2B. In addition, data associated with subcellular location predictions performed by the algorithms WoLF PSORT (eukaryotic genomes), PSORTb (procariotic genomes), Sigcleave (signal peptides) and TMHMM (transmembrane domains) were integrated in a relational database. The workflow had been used for searching vaccine or diagnostic targets in prokaryotic and eukaryotic organisms, including Leishmania infantum, Leishmania braziliensis, Schistosoma mansoni and Ehrlichia canis. Experiments in wet lab are being performed in order to confirm the immunogenicity of the selected proteins from Leishmania and S. mansoni. The correlation between structural disorder and the epitope location will be presented together with the analytical approach developed.