Voelz VA, Bowman GR, Beauchamp K, Pande VS. Journal of the American Chemical Society (2010)
Simulating protein folding on the millisecond timescale has been a major challenge for many years. When we started Folding@home, our first goal was to break the microsecond barrier. This barrier is 1000x fold harder and represents a major step forward in molecular simulation. Specifically, in a recent paper (http://pubs.acs.org/doi/abs/10.1021/ja9090353), Folding@home researchers Vincent Voelz, Greg Bowman, Kyle Beauchamp, and Vijay Pande have broken this barrier. The movie below is of one of the trajectories that folded (i.e. started unfolded and ended up in the folded state). From simulations like these, we have found some new surprises in how proteins fold. Please see the paper (url above) for more details.
Why is this important? This is important since protein misfolding occurs on long timescales and this first simulation on the millisecond simulation for protein folding means we have demonstrated our new Markov State Model (MSM) technology can successfully simulate long timescales. It make sense to go after protein folding first, since there is a wealth of experimental data for us to test our simulations. While this paper on protein folding has just come out, we have already been using this MSM technology to study protein misfolding in Alzheimer’s Disease, following up from our 2008 paper. While our previous paper (#58 below) was able to get to long enough timescales to see small molecular weight oligomers, this new methodology gives us hope to push further with our simulations of Alzheimer’s, making more direct connections to larger, more complex Abeta oligomers than we were previously able to do.
This is a pretty exciting moment for us in terms of what we can now do with simulations, and we’re looking forward to new applications of this technology.
To date, the slowest-folding proteins folded ab initio by all-atom molecular dynamics simulations have had folding times in the range of nanoseconds to microseconds. We report simulations of several folding trajectories of NTL9(1−39), a protein which has a folding time of 1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajectories out to 40 μs for several temperatures and starting states. At a temperature less than the melting point of the force field, we observe a small number of productive folding events, consistent with predictions from a model of parallel uncoupled two-state simulations. The posterior distribution of the folding rate predicted from the data agrees well with the experimental folding rate (640/s). Markov State Models (MSMs) built from the data show a gap in the implied time scales indicative of two-state folding and heterogeneous pathways connecting diffuse mesoscopic substates. Structural analysis of the 14 out of 2000 macrostates transited by the top 10 folding pathways reveals that native-like pairing between strands 1 and 2 only occurs for macrostates with pfold > 0.5, suggesting β12 hairpin formation may be rate-limiting. We believe that using simulation data such as these to seed adaptive resampling simulations will be a promising new method for achieving statistically converged descriptions of folding landscapes at longer time scales than ever before.