Disorder guides protein function.

Cellular function requires biomolecules to undergo dynamic transitions that include folding, conformational rearrangements, and large-scale assembly. The result is a highly interdependent network of processes that is maintained by a balance of thermodynamic and kinetic factors. In molecular machines, each constituent biopolymer (i.e., a chain of residues) first folds to a lowenergy configuration/ensemble. These ordered polymers can then assemble into sophisticated architectures, which undergo conformational transitions during function. In contrast to the dynamics of macroscopic machines, molecular-level processes are stochastic, where the molecular interactions that ensure structural integrity are weak (i.e., on the scale of energetic fluctuations from solvent). In this dynamic environment, biomolecules constantly fluctuate (1), and the extent of disorder is heterogeneous between residues. Inspired by this, in 2003, Miyashita et al. postulated that biomolecules may exploit disorder to accelerate functional kinetics (2). In their theoretical investigation of protein function, the authors found large levels of strain energy accumulate in isolated residues. The predicted level of strain exceeded the stability of most proteins under cellular conditions, suggesting that these highly-strained regions may locally unfold, or “crack.” By cracking, the molecule may gain configurational entropy and thereby reduce the strain-induced barrier (Fig. 1). Subsequently, many theoretical and computational investigations have found evidence of cracking during function. These studies have primarily used simplified models (3), with which millisecondscale dynamics are computationally accessible. In contrast, simulations with explicit-solvent models are typically limited to nanoseconds, or occasionally microseconds (4, 5). Because cracking and large-scale rearrangements occur on relatively long timescales (microseconds to milliseconds), evidence of cracking with explicit-solvent models has been sparse. In PNAS, Shan et al. (6) report the most definitive evidence of cracking from explicit-solvent simulations, to date. Using a specialized computer, they performed multiple simulations of EGFR kinase in solvent for tens of microseconds and found cracking to spontaneously occur. Although open questions remain about the precise details of cracking properties, Shan et al.’s study highlights how convergent theoretical descriptions of biological dynamics are emerging as explicit-solvent simulations are pushed to longer timescales. Grounded in the statistical physics of glasses, energy landscape theory (7, 8) provides a framework for understanding the relationship between protein disorder and energetics, at global (folding) and local (cracking) scales. A key finding has been that proteins do not fold along precisely defined pathways, but there is a multitude of routes by which proteins navigate between extended (unfolded) and compact (folded/native) ensembles. The theory further predicts that folding energetics are dominated by the interactions formed in the folded configuration, which has allowed for extensive application of simplified “structure-based” models for folding (3). Although folding of individual domains can often be described as a pseudo first-order phase transition (9), the process is not perfectly cooperative. Many residues cooperatively organize, although some atoms remain free to undergo separate order-disorder events. Simplified models demonstrated this point (10), which was later corroborated by long-timescale simulations from Shaw et al. (11). This intuitive finding is one example of how longertimescale explicit-solvent simulations are reinforcing predictions from simple models, in this case suggesting a propensity for localized disorder that is separable from full folding transitions. Simplified models built on energy landscape principles have repeatedly implicated cracking during function. Structure-based models approximate the landscape by a few dominant basins of attraction, each corresponding to an experimentally determined configuration. In doing so, the models use knowledge of these low-energy configurations to provide a first-pass description of the potential energy surface. These models carry the added bonus of being computationally inexpensive, enabling long-timescale simulations to be obtained, even for large assemblies (3, 12, 13). One may then identify statistically significant correlations