Thursday, 2nd June, 2011, 11:00-12:00

Unravelling the complexity of molecular kinetics: states, pathways and experimental evidence

Simulations of the conformational equilibrium of macromolecules typically reveal a complex network of conformational states and transition rates between the states. In contrast, experimental results, such as dynamical fingerprints of macromolecules, often seem to indicate two- or three state kinetics. Markov state models, which are an efficient method to capture and summarize the information obtained from molecular simulation, can be used to predict these dynamical fingerprints and to reconcile experiment with simulation. In the first part of the lecture, I will given an overview of how to extract metastable states and dominant pathways from a given Markov model. Then, I will use a four-state model of a protein folding equilibrium to illustrate how dynamical fingerprints can be predicted from a given Markov model. From the equations of this method it becomes evident that (i) there might be no process which corresponds to the common notion of folding; (ii) often the experiment will be insensitive to some of the processes present in the system. These effects cause the difference in complexity between experimental and simulation results. Lastly, it is not only possible to predict dynamical fingerprints, but one can also use Markov models to design experiments to selectively measure specific processes. This will be demonstrated for a fluorescence quenching experiment of the MR121-G9-W peptide.

Speaker: Dr. Bettina Keller (Computational Molecular Biology, Freie Universität Berlin, Germany)

Room Seminar Room “Xipre” 173.06 (PRBB – 1st Floor)