Modelling Viral Channel Proteins

Molecular self assembly:

Molecular self-assembly is one of the techniques which is used by nature to compose almost all living matter. In its extreme cases, molecular self-assembly covers the range of modulator or ligand binding up to the large scale 2D assembly of membrane proteins within the lipid bilayer e.g. during the budding process. We investigate the conformation space of a series of viral channel and pore forming proteins. The exact number of assembling proteins forming the homo oligomeric bundles is unknown. Thus, computer simulations play an important role to (i) explore pathways for protein approach and (ii) finally to predict protein-protein interactions until further experimental evidence is available.

Diffusion of ions and substrates:

The proper simulation allows us to monitor the reliability of the protein assembly and to derive protocols for ligand diffusion, thereby supporting modulator development for bionanotechnology. Currently we are using steered molecular dynamics (MD) simulations. With steered MD an additional directional force is applied to specific parts of a molecule at each time step during the simulation. This protocol allows to model the flux of ions and substrates into narrow pore geometries and to derive an estimate of the free energy profile along the reaction coordinate. In this respect we develop novel computational protocols for calculating more accurately binding dynamics data for ligand - protein interactions.

Protein folding:

For only a few viral membrane proteins experimentally derived structural information is available. With the rapidly increasing number of viral proteins discovered to be located within or attached to the membrane computational tools become a valuable tool to predict the fold of these proteins. We aim to develop computational methods that enhance the accuracy of the prediction. The concepts of folding will also be adapted to describe the mode of action of these proteins.

 

Hardware

Continuously updating local workstations.

NCHC (National Center for High-Performance Computing, HsinChu, TW)

NYMU cluster