Detailed understanding of complex processes such as nucleation, self-assembly, protein folding, binding, and fibril formation, requires knowledge of the underlying free energy landscape, kinetics and mechanisms. Molecular dynamics simulation can yield such information directly, for instance by constructing and analysing a Markov state model consisting of a coarse-grained network of metastable states between which transitions occur. However, in general, the direct construction of such a network is unpractical, since the time wasted in a metastable state grows exponentially with its stability. Transition path sampling (TPS) can harvest ensembles of unbiased dynamical trajectories overcoming high barriers in such complex landscapes. The great advantage of transition path sampling is that by focusing on the pathways undergoing the activated event, TPS avoids the problem of the choice of good collective variables to describe the reaction coordinate. This problem hampers many enhanced sampling methods based on molecular dynamics, as the omission of the pertinent collective variables can lead to dramatically wrong estimates of rate constants and mechanisms.TPS and related methods such as multiple state transition interface sampling can effectively sample an entire kinetic network, without any approximation.The resulting path ensembles also allow extraction of a low dimensional description of the reaction coordinates, and committor surfaces. I will show examples on protein folding, protein dissociation and fibril formation, as well as DNA base pair rolling, and conformational changes in a Ras protein. The recent development of the OpenPathSampling framework [1,2] has enabled easy and flexible use and implementation of novel path sampling algorithms on large systems.<br/><br/><br/>

Free energy, kinetics, and reaction coordinates of biomolecular processes from transition path sampling simulations

2019 Spring National Meeting

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