UC Berkeley

We present a novel sampling approach to explore large protein conformational transitions by determining unique substates from instantaneous normal modes calculated from an elastic network model, and applied to a progression of atomistic molecular dynamics snapshots. This unbiased sampling scheme allows us to direct the path sampling between the conformational end states over simulation timescales that are greatly reduced relative to the known experimental timescales. We use adenylate kinase as a test system to show that instantaneous normal modes can be used to identify substates that drive the structural fluctuations of adenylate kinase from its closed to open conformations, in which we observe 16 complete transitions in 4 mus of simulation time, reducing the timescale over conventional simulation timescales by two orders of magnitude. Analysis shows that the unbiased determination of substates is consistent with known pathways determined experimentally.