Biophysics Doctoral Students - Efrosini Artikis and Jeffrey Maltas

Efrosini Artikis, Biophysics Doctoral Candidate “Exploration of pH dependent NMR chemical shift perturbations in peptides and proteins”

Abstract: Abstract:
In studying pH-mediated biological processes, NMR is an optimal experimental approach as the sensitivity of NMR chemical shifts to both molecular composition and local environment allows for a comprehensive description of pH-dependent effects. Currently, empirical and semi-empirical chemical shift predictors are largely insensitive to extreme pH conditions and compute absolute shifts only at physiological pH. Aiming to better understand the manifestation of pH in these NMR observables, we utilize a combination of MD and DFT calculations for the ab initio computation of NMR chemical shift perturbations (CSP) of model tri-peptides. By altering the protonation state of the titratable residue present in the peptides, we are able to recapitulate pH effects observed in experimental NMR measurements. Furthermore, we employ a similar methodology to examine electrostatically driven interactions in proteins. We anticipate that the computation of the pH-dependent CSPs for these systems, will lead to a generalized protocol through which these observables may be computed for a variety of proteins involved in pH-mediated processes.

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“Jeffrey Maltas, Biophysics Doctoral Candidate, “Collateral sensitivity in Enterococcus faecalis”

Abstract: Often when bacteria populations acquire resistance against an individual antibiotic, that resistance mechanism carries pleiotropic effects with it resulting in increased resistance or sensitivity to other antibiotics the population has yet to encounter. If these collateral sensitivity and resistance effects can be understood, and perhaps predicted, they can be a potent tool to slow down, or even reverse, the spread of antibiotic resistance. To date, the collateral sensitivity and resistance profiles and their molecular basis have only been studied in a few bacterial species and is unknown for Enterococcus faecalis. Here we present the collateral profile for 17 antibiotics to 68 independent populations of E. faecalis. By clustering these mutants using their collateral profiles we find that large collateral effects are predictable, while the more common, smaller collateral effects appear to be more random. Additionally, we highlight the time dependence of collateral effects for the first time in bacteria.