Slow unfolded-state structuring revealed by simulation and experiment

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Guest post from Dr. Vincent Voelz, Temple University

 

Using protein folding simulations alongside experiments remains challenging because the two techniques often "see" very different things. Simulation trajectories "see" every atom in a single protein in microscopic detail, while experiments often "see" only bulk properties averaged over large ensembles of molecules. For example, in the last few years, we have built kinetic network models of ever larger and slower-folding proteins. These models can have huge numbers of states and many possible folding pathways, yet experimental folding kinetics can be fit to models having only two or three states.

 

In a new paper, we try to bridge these two levels of detail using a combination of simulation and experiment to study the early folding events of ACBP, a 86-residue helix-bundle protein that folds on the ~10 millisecond timescale, one of the largest, slowest-folding proteins we have studied to date. Previous experiments suggested that ACBP folds via a "three-state" mechanism, with an intermediate forming on the ~100 µs timescale. To understand the molecular events underlying the formation of this intermediate, we used Folding@Home to generate tens of thousands of GPU-accelerated trajectories, and stitched these together to build a kinetic network model of the complete folding reaction (see figure below). By comparing our model to the results of state-of-the-art experiments (single-molecule FRET, Trp-Cys quenching, and time-resolved FRET) we found something surprising -- the folding relaxation timescale around ~100 µs corresponds to the heterogeneous formation of unfolded-state structure, rather than some discrete structural state.

 

This work is exciting because it shows that our models can predict atomically detailed mechanistic information about folding (currently very difficult to obtain experimentally) while simultaneously providing accurate predictions of quantities seen in bulk folding experiments.

 

 

 

 

 

 

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