In this work we use mesoscopic simulation to investigate the short and medium range interaction of transmembrane helices. First, we develop a coarse-grained model of a generic transmembrane α-helix. We use the geometry constraints of an α-helix, along with a set
of homogeneous hydrophobic beads. We discuss the reasoning behind this modeling and why various other plausible models have failed in describing the short-range interactions of transmembrane helices.
We then show the effect of hydrophobic mismatch on these model helices. The tilt angle of a single helix is affected by its hydrophobic mismatch with the surrounding bilayer. We show that the hydrophobic mismatch, via its effect on the tilt angle, has a crucial effect on the cross angle of two packed transmembrane helices as well.
We further show the effect of hydrophobic mismatch on the potential of mean force between the helices as well as on the thickness and tilt angle of the lipids surrounding the helices. We introduce a class of especially long helices, termed super-positive mismatched
helices, to which the response of the lipids differs and discuss the origin of this behavior.
These coarse-grained simulations are performed using the Dissipative Particle Dynamics (DPD) scheme Performing large time and length scale simulations using this scheme is highly CPU-time consuming. To enhance our simulation capabilities we develop a novel, massively parallel simulation algorithm. We introduce this algorithm and the original concepts we developed for parallelizing the DPD scheme. We show that this novel approach provides
up to 30 times speed up on a Graphical Processing Unit (GPU) over the non-parallel CPU version.
Finally, we use our parallel algorithm to perform simulation on a large time and length scale system. We investigate the effect of Cholesterol on the bending rigidity of lipid bilayers and show the effect of a phase transition on the bending modulus. Extracting the bending modulus from simulation requires a large system that enables long-range height fluctuations. Using a fast, parallel simulation algorithm for such a task is therefore crucial.