Computational methods for transport properties

Through antimicrobial resistance many bacteria can survive to an ever larger number of antibiotics. This is true in particular for a category of bacteria classified as gram–negative. These kinds of bacteria differ from the other ones by the presence of an outer membrane, which is able to protect them from the fast access (and consequently the action) of any antibiotics. The increasing capability of antibiotics to survive to many kinds of drugs has given rise to the Multiple Drug Resistance (MDR). New antibiotics could help to mitigate the MDR problem, but the poor understanding of permeability through outer membranes has given an ever littler number of new patented antibiotics. This is due to a lack of experimental methods which are able to explain with a sufficient detail the permeation and, on the other side, to the difficulty in reaching the typical time scales (ms or even more) of these processes. The category of antibiotics studied in this thesis can permeate the membrane crossing some porins (beta barrel proteins nestled in bacterial outer membrane) so the permeation happens when we observe a transport of the antibiotic through a porin. In this thesis we will focus on some computational methods, which are suitable to increase our understanding of transport processes. We will start with a post elaboration algorithm, that can be used to extract from an electrophysiology time series transport events apparently lower than the experimental device temporal sensitivity, continuing with another post elaboration algorithm that allows to extract the real transition time from a metadynamics simulation, skipping in this way the timescale problem in computer simulations, and we will finish with an ultra coarse grained model, that can be used to study the transport properties through a bacterial channel. Finally we will list the results obtained using the three aforementioned methods and we will summarise this thesis with the conclusions.