John J. Skinner

Protein dynamics viewed by hydrogen exchange

To examine the relationship between protein structural dynamics and measurable hydrogen exchange (HX) data, the detailed exchange behavior of most of the backbone amide hydrogens of Staphylococcal nuclease was compared with that of their neighbors, with their structural environment, and with other information. Results show that H‐bonded hydrogens are protected from exchange, with HX rate effectively zero, even when they are directly adjacent to solvent. The transition to exchange competence requires a dynamic structural excursion that removes H‐bond protection and allows exposure to solvent HX catalyst. The detailed data often make clear the nature of the dynamic excursion required. These range from whole molecule unfolding, through smaller cooperative unfolding reactions of secondary structural elements, and down to local fluctuations that involve as little as a single peptide group or side chain or water molecule. The particular motion that dominates the exchange of any hydrogen is the one that allows the fastest HX rate. The motion and the rate it produces are determined by surrounding structure and not by nearness to solvent or the strength of the protecting H‐bond itself or its acceptor type (main chain, side chain, structurally bound water). Many of these motions occur over time scales that are appropriate for biochemical function.

Protein hydrogen exchange: Testing current models

To investigate the determinants of protein hydrogen exchange (HX), HX rates of most of the backbone amide hydrogens of Staphylococcal nuclease were measured by NMR methods. A modified analysis was used to improve accuracy for the faster hydrogens. HX rates of both near surface and well buried hydrogens are spread over more than 7 orders of magnitude. These results were compared with previous hypotheses for HX rate determination. Contrary to a common assumption, proximity to the surface of the native protein does not usually produce fast exchange. The slow HX rates for unprotected surface hydrogens are not well explained by local electrostatic field. The ability of buried hydrogens to exchange is not explained by a solvent penetration mechanism. The exchange rates of structurally protected hydrogens are not well predicted by algorithms that depend only on local interactions or only on transient unfolding reactions. These observations identify some of the present difficulties of HX rate prediction and suggest the need for returning to a detailed hydrogen by hydrogen analysis to examine the bases of structure‐rate relationships, as described in the companion paper (Skinner et al., Protein Sci 2012;21:996–1005).

Pump-probe molecular dynamics as a tool for studying protein motion and long range coupling.

A new method for analyzing the dynamics of proteins is developed and tested. The method, pump‐probe molecular dynamics, excites selected atoms or residues with a set of oscillating forces, and the transmission of the impulse to other parts of the protein is probed using Fourier transform of the atomic motions. From this analysis, a coupling profile can be determined which quantifies the degree of interaction between pump and probe residues. Various physical properties of the method such as reciprocity and speed of transmission are examined to establish the soundness of the method. The coupling strength can be used to address questions such as the degree of interaction between different residues at the level of dynamics, and identify propagation of influence of one part of the protein on another via “pathways” through the protein. The method is illustrated by analysis of coupling between different secondary structure elements in the allosteric protein calmodulin, and by analysis of pathways of residue–residue interaction in the PDZ domain protein previously elucidated by genomics and mutational studies. Proteins 2006.

Benchmarking all-atom simulations using hydrogen exchange

Significance Molecular dynamics simulations have recently become capable of observing multiple protein unfolding and refolding events in a single-millisecond–long trajectory. This major advance produces atomic-level information with nanosecond resolution, a feat unmatched by experimental methods. Such simulations are being extensively analyzed to assess their description of protein folding, yet the results remain difficult to validate experimentally. We apply a combination of hydrogen exchange, NMR, and other techniques to test the simulations with a resolution of single H-bonds. Several significant discrepancies between the simulations and experimental data were uncovered for regions of the energy surface outside of the native basin. This comparison yields suggestions for improving the force fields and provides a general method for experimentally validating folding simulations. Long-time molecular dynamics (MD) simulations are now able to fold small proteins reversibly to their native structures [Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) Science 334(6055):517–520]. These results indicate that modern force fields can reproduce the energy surface near the native structure. To test how well the force fields recapitulate the other regions of the energy surface, MD trajectories for a variant of protein G are compared with data from site-resolved hydrogen exchange (HX) and other biophysical measurements. Because HX monitors the breaking of individual H-bonds, this experimental technique identifies the stability and H-bond content of excited states, thus enabling quantitative comparison with the simulations. Contrary to experimental findings of a cooperative, all-or-none unfolding process, the simulated denatured state ensemble, on average, is highly collapsed with some transient or persistent native 2° structure. The MD trajectories of this protein G variant and other small proteins exhibit excessive intramolecular H-bonding even for the most expanded conformations, suggesting that the force fields require improvements in describing H-bonding and backbone hydration. Moreover, these comparisons provide a general protocol for validating the ability of simulations to accurately capture rare structural fluctuations.

The Mad2 partial unfolding model: regulating mitosis through Mad2 conformational switching

The metamorphic Mad2 protein acts as a molecular switch in the checkpoint mechanism that monitors proper chromosome attachment to spindle microtubules during cell division. The remarkably slow spontaneous rate of Mad2 switching between its checkpoint inactive and active forms is catalyzed onto a physiologically relevant time scale by a self–self interaction between its two forms, culminating in a large pool of active Mad2. Recent structural, biochemical, and cell biological advances suggest that the catalyzed conversion of Mad2 requires a major structural rearrangement that transits through a partially unfolded intermediate.

Noise-Driven Phenotypic Heterogeneity with Finite Correlation Time in Clonal Populations.

There has been increasing awareness in the wider biological community of the role of clonal phenotypic heterogeneity in playing key roles in phenomena such as cellular bet-hedging and decision making, as in the case of the phage-λ lysis/lysogeny and B. Subtilis competence/vegetative pathways. Here, we report on the effect of stochasticity in growth rate, cellular memory/intermittency, and its relation to phenotypic heterogeneity. We first present a linear stochastic differential model with finite auto-correlation time, where a randomly fluctuating growth rate with a negative average is shown to result in exponential growth for sufficiently large fluctuations in growth rate. We then present a non-linear stochastic self-regulation model where the loss of coherent self-regulation and an increase in noise can induce a shift from bounded to unbounded growth. An important consequence of these models is that while the average change in phenotype may not differ for various parameter sets, the variance of the resulting distributions may considerably change. This demonstrates the necessity of understanding the influence of variance and heterogeneity within seemingly identical clonal populations, while providing a mechanism for varying functional consequences of such heterogeneity. Our results highlight the importance of a paradigm shift from a deterministic to a probabilistic view of clonality in understanding selection as an optimization problem on noise-driven processes, resulting in a wide range of biological implications, from robustness to environmental stress to the development of drug resistance.