Diffuse Ions Coordinate Dynamics in a Ribonucleoprotein Assembly.

Proper ionic concentrations are required for the functional dynamics of RNA and ribonucleoprotein (RNP) assemblies. While experimental and computational techniques have provided many insights into the properties of chelated ions, less is known about the energetic contributions of diffuse ions to large-scale conformational rearrangements. To address this, we present a model that is designed to quantify the influence of diffuse monovalent and divalent ions on the dynamics of biomolecular assemblies.

Modeling of sustained spontaneous network oscillations of a sexually dimorphic brainstem nucleus: the role of potassium equilibrium potential.

Intrinsic oscillators in the central nervous system play a preeminent role in the neural control of rhythmic behaviors, yet little is known about how the ionic milieu regulates their output patterns. A powerful system to address this question is the pacemaker nucleus of the weakly electric fish Apteronotus leptorhynchus. A neural network comprised of an average of 87 pacemaker cells and 20 relay cells produces tonic oscillations, with higher frequencies in males compared to females.

A steric gate controls P/E hybrid-state formation of tRNA on the ribosome.

The ribosome is a biomolecular machine that undergoes multiple large-scale structural rearrangements during protein elongation. Here, we focus on a conformational rearrangement during translocation, known as P/E hybrid-state formation. Using a model that explicitly represents all non-hydrogen atoms, we simulated more than 120 spontaneous transitions, where the tRNA molecule is displaced between the P and E sites of the large subunit.

Using SMOG 2 to Simulate Complex Biomolecular Assemblies.

Over the last 20 years, the application of structure-based (Gō-like) models has ranged from protein folding with coarse-grained models to all-atom representations of large-scale molecular assemblies. While there are many variants that may be employed, the common feature of these models is that some (or all) of the stabilizing energetic interactions are defined based on the knowledge of a particular experimentally obtained conformation. With the generality of this approach, there was a need for a versatile computational platform for designing and implementing this class of models.

Studying ribosome dynamics with simplified models.

With the broad accessibility of high-performance computing resources, the significance of a molecular dynamics simulation is now rarely limited by hardware and/or software availability. Rather, the scientific value of each calculation is determined by the principles that underlie the theoretical model. The current review addresses this topic in the context of simplified models applied to large-scale (∼20-100 Å) dynamics in the ribosome.

Dissecting the Energetics of Subunit Rotation in the Ribosome.

The accurate expression of proteins requires the ribosome to efficiently undergo elaborate conformational rearrangements. The most dramatic of these motions is subunit rotation, which is necessary for tRNA molecules to transition between ribosomal binding sites. While rigid-body descriptions provide a qualitative picture of the process, obtaining quantitative mechanistic insights requires one to account for the relationship between molecular flexibility and collective dynamics.

Quantifying the Relationship between Single-Molecule Probes and Subunit Rotation in the Ribosome.

A major challenge in the study of biomolecular assemblies is to identify reaction coordinates that precisely monitor conformational rearrangements. This is central to the interpretation of single-molecule fluorescence resonance energy transfer measurements, where the observed dynamics depends on the labeling strategy. As an example, different probes of subunit rotation in the ribosome have provided qualitatively distinct descriptions.

SMOG 2: A Versatile Software Package for Generating Structure-Based Models.

Molecular dynamics simulations with coarse-grained or simplified Hamiltonians have proven to be an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Originally developed in the context of protein folding, structure-based models (SBMs) have since been extended to probe a diverse range of biomolecular processes, spanning from protein and RNA folding to functional transitions in molecular machines. The hallmark feature of a structure-based model is that part, or all, of the potential energy function is defined by a known structure.