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Talk- Michelle Aranha

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Metal oxide nanoparticle – lipid bilayer interactions and vaccine development against Group A streptococcus In this talk, I will discuss the preliminary results from two topics: 1) metal oxide - lipid bilayer interactions and 2) vaccine development against Group A streptococcus (GAS) using a combination of computational, statistical and experimental approaches. Metal oxide - lipid bilayer interactions: In this study, we have assessed the effects of a metal oxide nanoparticle namely titanium dioxide (TiO 2 ) on unsaturated bilayers composed of uncharged DOPC lipids and negatively charged DOPG lipids. TiO 2 is a transition metal oxide and finds applications in pigments, additives, catalysts and construction materials. However, some experimental studies have uncovered the potential cytotoxic effects of transition metal oxide nanoparticles which include changes in cell morphology, mitochondrial functions, membrane leakage, necrosis and apoptosis. With their extensive use in a wide array of applications it has become of vital importance to understand their potential cytotoxic effect to design them for safe applications. Molecular dynamics (MD) simulations can help shed some light on the nature of the interaction when the metal oxide nanoparticle first encounters the cell membrane. For instance, in the case of DOPC, we have found a localized decrease in the bilayer thickness on account of a compression of head groups together rather than any perturbation of the tail groups, a depletion in the hydration layer of the DOPC leaflet at the point of contact with the nanoparticle and a slowdown in diffusion of lipids after it comes in contact with nanoparticle. Through our atomistic MD study, we have assessed the effect on structural and dynamical properties of both DOPC and DOPG bilayers. These preliminary studies may pave the way for a more detailed study into the differential interactions of various other metal oxide nanoparticles with mixed lipid bilayers. Vaccine development against (GAS): This study will demonstrate how the use of combined computational, statistical and experimental approaches can exploit the structural similarities of the N -terminal region of M proteins of the more than 200 strains of GAS bacteria to predict the M peptides that will evoke cross-opsonic antibodies against heterologous strains of GAS. We have used homology modeling to predict the N - terminal structures of M proteins of 21 strains belonging to a sequence -based cluster. Unsupervised learning on structure-based features and supervised learning employing regression were used to detect relatedness among different M peptides as well as to identify any peptide structure - activity relationship. Clusters of related M peptides identified by these methods were used to select a few M peptides to incorporate into the first vaccine construct.

  • cmb seminar
When May 07, 2018
from 11:00 AM to 12:30 PM
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