We have traditionally relied on extremely elevated temperatures (498 K, 225 8C) to investigate the unfolding process of proteins within the timescale available to molecular dynamics simulations with explicit solvent. However, recent advances in computer hardware have allowed us to extend our thermal denaturation studies to much lower temperatures. Here we describe the results of simulations of chymotrypsin inhibitor 2 at seven temperatures, ranging from 298 K to 498 K. The simulation lengths vary from 94 ns to 20 ns, for a total simulation time of 344 ns, or 0.34 ms. At 298 K, the protein is very stable over the full 50 ns simulation. At 348 K, corresponding to the experimentally observed melting temperature of CI2, the protein unfolds over the first 25 ns, explores partially unfolded conformations for 20 ns, and then refolds over the last 35 ns. Above its melting temperature, complete thermal denaturation occurs in an activated process. Early unfolding is characterized by sliding or breathing motions in the protein core, leading to an unfolding transition state with a weakened core and some loss of secondary structure. After the unfolding transition, the core contacts are rapidly lost as the protein passes on to the fully denatured ensemble. While the overall character and order of events in the unfolding process are well conserved across temperatures, there are substantial differences in the timescales over which these events take place. We conclude that 498 K simulations are suitable for elucidating the details of protein unfolding at a minimum of computational expense.

We study the unbiased folding/unfolding thermodynamics of the Trp-cage miniprotein using detailed molecular dynamics simulations of an all-atom model of the protein in explicit solvent, using the Amberff99SB force field. Replica-exchange molecular dynamics (REMD) simulations are used to sample the protein ensembles over a broad range of temperatures covering the folded and unfolded states, and at two densities. The obtained ensembles are shown to reach equilibrium in the 1 μs per replica timescale. The total simulation time employed in the calculations exceeds 100 μs. Ensemble averages of the fraction folded, pressure, and energy differences between the folded and unfolded states as a function of temperature are used to model the free energy of the folding transition, ΔG(P,T), over the whole region of temperature and pressures sampled in the simulations. The ΔG(P,T) diagram describes an ellipse over the range of temperatures and pressures sampled, predicting that the system can undergo pressure induced unfolding and cold denaturation at low temperatures and high pressures, and unfolding at low pressures and high temperatures. The calculated free energy function exhibits remarkably good agreement with the experimental folding transition temperature (Tf = 321 K), free energy and specific heat changes. However, changes in enthalpy and entropy are significantly different than the experimental values. We speculate that these differences may be due to the simplicity of the semi-empirical force field used in the simulations and that more elaborate force fields may be required to describe appropriately the thermodynamics of proteins.

It is controversial whether fast-folding proteins can form productive on-pathway intermediates that are more stable than the denatured state because noncovalent intermediates are usually evanescent. Here, we apply the classical criteria for the existence of intermediates: namely, the intermediates form and react rapidly enough to be on pathway and they can be isolated and characterized

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The goal of Dynameomics is to perform atomistic molecular dynamics (MD) simulations of representative proteins from all known folds in explicit water in their native state and along their thermal unfolding pathways. Here we present 188-fold representatives and their native state simulations and analyses. These 188 targets represent 67% of all the structures in the Protein Data Bank. The behavior of several specific targets is highlighted to illustrate general properties in the full dataset and to demonstrate the role of MD in understanding protein function and stability. As an example of what can be learned from mining the Dynameomics database, we identified a protein fold with heightened localized dynamics. In one member of this fold family, the motion affects the exposure of its phosphorylation site and acts as an entropy sink to offset another portion of the protein that is relatively immobile in order to present a consistent interface for protein docking. In another member of this family, a polymorphism in the highly mobile region leads to a host of disease phenotypes. We have constructed a web site to provide access to a novel hybrid relational/multidimensional database (described in the succeeding two papers) to view and interrogate simulations of the top 30 targets: http://www.dynameomics.org. The Dynameomics database, currently the largest collection of protein simulations and protein structures in the world, should also be useful for determining the rules governing protein folding and kinetic stability, which should aid in deciphering genomic information and for protein engineering and design.