Speaker : Frank Noe Abstract: Spinning Transition Networks for Proteins Determining transition states for conformational changes in proteins is often unfeasible in experiment. Consequently, simulation methods are used to gain insight in the mechanism of such transitions. Molecular Dynamics (MD) can simulate the natural dynamics of any molecule, but computational limitations currently do not allow to observe transitions which occur on a timescale longer than nanoseconds. To overcome this problem, Targeted Molecular Dynamics (TMD) and Minimum Energy Path (MEP) methods have been developed. Both of them suffer from the fact that their results very much depend on the initial guess (in TMD the guessed reaction coordinate, for MEPs the initial path). This project proposes a more global view on conformational transitions. By sampling a number of interesting degrees of freedom and connecting the resulting configurations with MEPs, one generates a Transition Network. This Network has the structure of a weighted directed graph and is therefore appropriate for the application of many algorithms from combinatorial optimization. Macroscopic properties such as shortest paths, effective rate constants, population distributions and cuts of minimum flow can be computed from it in polynomial time. This talk will focus on how Transition Networks for Proteins can be efficiently generated.