The case for defined protein folding pathways

Abstract

We consider the differences between the many-pathway protein folding model derived from theoretical energy landscape considerations and the defined-pathway model derived from experiment. A basic tenet of the energy landscape model is that proteins fold through many heterogeneous pathways by way of amino acid-level dynamics biased toward selecting native-like interactions. The many pathways imagined in the model are not observed in the structure-formation stage of folding by experiments that would have found them, but they have now been detected and characterized for one protein in the initial prenucleation stage. Analysis presented here shows that these many microscopic trajectories are not distinct in any functionally significant way, and they have neither the structural information nor the biased energetics needed to select native vs. nonnative interactions during folding. The opposed defined-pathway model stems from experimental results that show that proteins are assemblies of small cooperative units called foldons and that a number of proteins fold in a reproducible pathway one foldon unit at a time. Thus, the same foldon interactions that encode the native structure of any given protein also naturally encode its particular foldon-based folding pathway, and they collectively sum to produce the energy bias toward native interactions that is necessary for efficient folding. Available information suggests that quantized native structure and stepwise folding coevolved in ancient repeat proteins and were retained as a functional pair due to their utility for solving the difficult protein folding problem.

Keywords: energy landscape theory; foldons; protein folding.

Fig. 1.

Alternative protein folding models. (A) ELT proposes that proteins fold to their native state by energetically downhill conformational searching through innumerable pathways at the level of residue-level dynamics. The selection of native as opposed to nonnative interactions during folding is directed by a funnel-shaped energy landscape (80). (B) Experiment shows that, under equilibrium native conditions, cyt c unfolds by stepping energetically uphill through a ladder of forms that differ one from the next by the unfolding of one more native-like foldon (far right) (16, 17). HX MS experiments during kinetic folding demonstrate a pathway that steps sequentially downhill through the same intermediates (40). These results are able to specify the stepwise pathway in close to 3D structural detail (rather than as a 1D projection onto some reaction coordinate) because the downhill kinetic folding units and the uphill equilibrium unfolding units are very similar to the foldons that compose the native structure.

Fig. 2.

The stepwise folding of RNase H. Reprinted from ref. . The HX MS experiment together with HX pulse labeling monitors kinetic folding in real time, resolved to the level of short protein segments. (A–D) MS isotopic envelopes for four segments representative of the four color-coded foldon units in RNase H. In a series of folding experiments, a brief D-to-H labeling pulse (10 ms) was imposed after the folding times noted. The bimodal MS envelopes show that each segment folds in a concerted step (unprotected to protected) and persists through later folding steps elsewhere in the protein. (E and F) The time sequence for the folding of many high signal-to-noise peptides.

Fig. 3.

Many trajectories by single-molecule dual-trap optical tweezers experiments. (A) Dwell times in the fast folding and unfolding of a monomeric prion (19). (B) The exponential distribution of folding times (19). (C) Transit time between the unfolded and first folded state of a covalently dimeric prion (20), measured between the two blue lines for one successful unfolding step and one folding step. (D) The up-and-down distribution of folding and unfolding transit times matches the shape expected when successful transitions require the crossing of two positions (the blue lines) (55). (E) The entire downsweep part of the distribution is well fit by a single exponential indicating a common homogeneous population rate constant rather than the diverse heterogeneous trajectories expected for the many-pathway model.