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We performed molecular dynamics simulations of the collapse of a two-domain protein, the BphC enzyme, into a globular structure to examine how water molecules mediate Hydrophobic collapse of proteins. In the interdomain region, liquid water persists with a density 10 to 15% lower than in the bulk, even at small domain separations. Water depletion and hydrophobic collapse occur on a nanosecond time scale, which is two orders of magnitude slower than that found in the collapse of idealized paraffin-like plates. When the electrostatic protein-water forces are turned off, a dewetting transition occurs in the interdomain region and the collapse speeds up by more than an order of magnitude. When attractive van der Waals forces are turned off as well, the dewetting in the interdomain region is more profound, and the collapse is even faster. [ABSTRACT FROM AUTHOR]

Characterization of transient intermediate or transition states is crucial for the description of biomolecular folding pathways, which is, however, difficult in both experiments and computer simulations. Such transient states are typically of low population in simulation samples. Even for simple systems such as RNA hairpins, recently there are mounting debates over the existence of multiple intermediate states. In this paper, we develop a computational approach to explore the relatively low populated transition or intermediate states in biomolecular folding pathways, based on a topological data analysis tool, MAPPER, with simulation data from large-scale distributed computing. The method is inspired by the classical Morse theory in mathematics which characterizes the topology of high-dimensional shapes via some functional level sets. In this paper we exploit a conditional density filter which enables us to focus on the structures on pathways, followed by clustering analysis on its level sets, which helps separate low populated intermediates from high populated folded/unfolded structures. A successful application of this method is given on a motivating example, a RNA hairpin with GCAA tetraloop, where we are able to provide structural evidence from computer simulations on the multiple intermediate states and exhibit different pictures about unfolding and refolding pathways. The method is effective in dealing with high degree of heterogeneity in distribution, capturing structural features in multiple pathways, and being less sensitive to the distance metric than nonlinear dimensionality reduction or geometric embedding methods. The methodology described in this paper admits various implementations or extensions to incorporate more information and adapt to different settings, which thus provides a systematic tool to explore the low-density intermediate states in complex biomolecular folding systems. [ABSTRACT FROM AUTHOR]

Markov models and master equations are a powerful means of modeling dynamic processes like protein conformational changes. However, these models are often difficult to understand because of the enormous number of components and connections between them. Therefore, a variety of methods have been developed to facilitate understanding by coarse-graining these complex models. Here, we employ Bayesian model comparison to determine which of these coarse-graining methods provides the models that are most faithful to the original set of states. We find that the Bayesian agglomerative clustering engine and the hierarchical Nyström expansion graph (HNEG) typically provide the best performance. Surprisingly, the original Perron cluster cluster analysis (PCCA) method often provides the next best results, outperforming the newer PCCA+ method and the most probable paths algorithm. We also show that the differences between the models are qualitatively significant, rather than being minor shifts in the boundaries between states. The performance of the methods correlates well with the entropy of the resulting coarse-grainings, suggesting that finding states with more similar populations (i.e., avoiding low population states that may just be noise) gives better results. [ABSTRACT FROM AUTHOR]

The glutamine binding protein (GlnBP) binds l-glutamine and cooperates with its cognate transporters during glutamine uptake. Crystal structure analysis has revealed an open and a closed conformation for apo- and holo-GlnBP, respectively. However, the detailed conformational dynamics have remained unclear. Herein, we combined NMR spectroscopy, MD simulations, and single-molecule FRET techniques to decipher the conformational dynamics of apo-GlnBP. The NMR residual dipolar couplings of apo-GlnBP were in good agreement with a MD-derived structure ensemble consisting of four metastable states. The open and closed conformations are the two major states. This four-state model was further validated by smFRET experiments and suggests the conformational selection mechanism in ligand recognition of GlnBP. [ABSTRACT FROM AUTHOR]