Coarse-grained simulations for membranes and membrane proteins: rafts folding and fusion

Using computers to simulate biological processes at the level of individual molecules is now quite common. However real cells are very complicated, involving mixtures of very large numbers of proteins, DNA molecules, lipids, and water. Unfortunately, simulating very large numbers of these molecules is beyond the scope of current computational resources. If this limitation is to be overcome, we have to find simpler ways of representing the molecules, so that we can simulate more for longer. In this proposal, we are planning to develop simple models of biological membranes and proteins. A very large number of proteins are buried in membranes, and being able to simulate these large systems for long times will allow important aspects of the cell to be studied. These include how proteins fold in the membrane, and how proteins are involved in the fusion of cell membranes.

Membranes and their proteins are of central importance to a wide range of biological processes and systems. As quantitative evidence of this, one may note that approximately 25 % of genes code for membrane proteins, and about 50 % of drug targets are membrane proteins. Membranes play key roles in many biological processes in prokaryotes and eukaryotes, including transport, signalling, and enery transduction. Membrane proteins are notoriously difficult to study. In particular, the interactions between multiple lipid species and multiple proteins unlerlie the biology of membranes, yet are incompletely understood, especially at a molecular level. Simulations already offer a route to understanding the relationship between conformation, dynamics and function of membrane proteins. However, at best all atom MD simulations can reach timescales of approximately one microsecond. This is sufficient for smaller scale dynamics events, but extending this approach to more 'biological' studies and timescales is a problem. The solution to this difficulty is to use a multilevel simulation approach, whereby more coarse-grained (CG) models of lipids and/or proteins are employed to reach higher timescales, and yield predictions/models which may subsequently be refined by inclusion of (near) atomic level detail. In this proposal, our focus is on novel methods of coarse graining (of both lipids and membrane proteins) and on selected key biological applications.