Karissa Y. Sanbonmatsu

Head swivel on the ribosome facilitates translocation by means of intra-subunit tRNA hybrid sites

The elongation cycle of protein synthesis involves the delivery of aminoacyl-transfer RNAs to the aminoacyl-tRNA-binding site (A site) of the ribosome, followed by peptide-bond formation and translocation of the tRNAs through the ribosome to reopen the A site. The translocation reaction is catalysed by elongation factor G (EF-G) in a GTP-dependent manner. Despite the availability of structures of various EF-G–ribosome complexes, the precise mechanism by which tRNAs move through the ribosome still remains unclear. Here we use multiparticle cryoelectron microscopy analysis to resolve two previously unseen subpopulations within Thermus thermophilus EF-G–ribosome complexes at subnanometre resolution, one of them with a partly translocated tRNA. Comparison of these substates reveals that translocation of tRNA on the 30S subunit parallels the swivelling of the 30S head and is coupled to unratcheting of the 30S body. Because the tRNA maintains contact with the peptidyl-tRNA-binding site (P site) on the 30S head and simultaneously establishes interaction with the exit site (E site) on the 30S platform, a novel intra-subunit ‘pe/E’ hybrid state is formed. This state is stabilized by domain IV of EF-G, which interacts with the swivelled 30S-head conformation. These findings provide direct structural and mechanistic insight into the ‘missing link’ in terms of tRNA intermediates involved in the universally conserved translocation process.

SMOG@ctbp: simplified deployment of structure-based models in GROMACS.

Molecular dynamics simulations with coarse-grained and/or simplified Hamiltonians are an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Structure-based Hamiltonians, simplified models developed from the energy landscape theory of protein folding, have become a standard tool for investigating biomolecular dynamics. SMOG@ctbp is an effort to simplify the use of structure-based models. The purpose of the web server is two fold. First, the web tool simplifies the process of implementing a well-characterized structure-based model on a state-of-the-art, open source, molecular dynamics package, GROMACS. Second, the tutorial-like format helps speed the learning curve of those unfamiliar with molecular dynamics. A web tool user is able to upload any multi-chain biomolecular system consisting of standard RNA, DNA and amino acids in PDB format and receive as output all files necessary to implement the model in GROMACS. Both Cα and all-atom versions of the model are available. SMOG@ctbp resides at http://smog.ucsd.edu.

Structural architecture of the human long non-coding RNA, steroid receptor RNA activator

While functional roles of several long non-coding RNAs (lncRNAs) have been determined, the molecular mechanisms are not well understood. Here, we report the first experimentally derived secondary structure of a human lncRNA, the steroid receptor RNA activator (SRA), 0.87 kB in size. The SRA RNA is a non-coding RNA that coactivates several human sex hormone receptors and is strongly associated with breast cancer. Coding isoforms of SRA are also expressed to produce proteins, making the SRA gene a unique bifunctional system. Our experimental findings (SHAPE, in-line, DMS and RNase V1 probing) reveal that this lncRNA has a complex structural organization, consisting of four domains, with a variety of secondary structure elements. We examine the coevolution of the SRA gene at the RNA structure and protein structure levels using comparative sequence analysis across vertebrates. Rapid evolutionary stabilization of RNA structure, combined with frame-disrupting mutations in conserved regions, suggests that evolutionary pressure preserves the RNA structural core rather than its translational product. We perform similar experiments on alternatively spliced SRA isoforms to assess their structural features.

Biomolecular dynamics: order?disorder transitions and energy landscapes

While the energy landscape theory of protein folding is now a widely accepted view for understanding how relatively weak molecular interactions lead to rapid and cooperative protein folding, such a framework must be extended to describe the large-scale functional motions observed in molecular machines. In this review, we discuss (1) the development of the energy landscape theory of biomolecular folding, (2) recent advances toward establishing a consistent understanding of folding and function and (3) emerging themes in the functional motions of enzymes, biomolecular motors and other biomolecular machines. Recent theoretical, computational and experimental lines of investigation have provided a very dynamic picture of biomolecular motion. In contrast to earlier ideas, where molecular machines were thought to function similarly to macroscopic machines, with rigid components that move along a few degrees of freedom in a deterministic fashion, biomolecular complexes are only marginally stable. Since the stabilizing contribution of each atomic interaction is on the order of the thermal fluctuations in solution, the rigid body description of molecular function must be revisited. An emerging theme is that functional motions encompass order-disorder transitions and structural flexibility provides significant contributions to the free energy. In this review, we describe the biological importance of order-disorder transitions and discuss the statistical-mechanical foundation of theoretical approaches that can characterize such transitions.

Excited states of ribosome translocation revealed through integrative molecular modeling

The dynamic nature of biomolecules leads to significant challenges when characterizing the structural properties associated with function. While X-ray crystallography and imaging techniques (such as cryo-electron microscopy) can reveal the structural details of stable molecular complexes, strategies must be developed to characterize configurations that exhibit only marginal stability (such as intermediates) or configurations that do not correspond to minima on the energy landscape (such as transition-state ensembles). Here, we present a methodology (MDfit) that utilizes molecular dynamics simulations to generate configurations of excited states that are consistent with available biophysical and biochemical measurements. To demonstrate the approach, we present a sequence of configurations that are suggested to be associated with transfer RNA (tRNA) movement through the ribosome (translocation). The models were constructed by combining information from X-ray crystallography, cryo-electron microscopy, and biochemical data. These models provide a structural framework for translocation that may be further investigated experimentally and theoretically to determine the precise energetic character of each configuration and the transition dynamics between them.

Computational studies of molecular machines: the ribosome

The past decade has produced an avalanche of experimental data on the structure and dynamics of the ribosome. Groundbreaking studies in structural biology and kinetics have placed important constraints on ribosome structural dynamics. However, a gulf remains between static structures and time dependent data. In particular, X-ray crystallography and cryo-EM studies produce static models of the ribosome in various states, but lack dynamic information. Single molecule studies produce information on the rates of transitions between these states but do not have high-resolution spatial information. Computational studies have aided in bridging this gap by providing atomic resolution simulations of structural fluctuations and transitions between configurations.

Sizing up long non-coding RNAs: do lncRNAs have secondary and tertiary structure?

Long noncoding RNAs (lncRNAs) play a key role in many important areas of epigenetics, stem cell biology, cancer, signaling and brain function. This emerging class of RNAs constitutes a large fraction of the transcriptome, with thousands of new lncRNAs reported each year. The molecular mechanisms of these RNAs are not well understood. Currently, very little structural data exist. We review the available lncRNA sequence and secondary structure data. Since almost no tertiary information is available for lncRNAs, we review crystallographic structures for other RNA systems and discuss the possibilities for lncRNAs in the context of existing constraints.

Rise of the RNA Machines: Exploring the Structure of Long Non-Coding RNAs

Novel, profound and unexpected roles of long non-coding RNAs (lncRNAs) are emerging in critical aspects of gene regulation. Thousands of lncRNAs have been recently discovered in a wide range of mammalian systems, related to development, epigenetics, cancer, brain function and hereditary disease. The structural biology of these lncRNAs presents a brave new RNA world, which may contain a diverse zoo of new architectures and mechanisms. While structural studies of lncRNAs are in their infancy, we describe existing structural data for lncRNAs, as well as crystallographic studies of other RNA machines and their implications for lncRNAs. We also discuss the importance of dynamics in RNA machine mechanism. Determining commonalities between lncRNA systems will help elucidate the evolution and mechanistic role of lncRNAs in disease, creating a structural framework necessary to pursue lncRNA-based therapeutics.

Connecting the Kinetics and Energy Landscape of tRNA Translocation on the Ribosome

Functional rearrangements in biomolecular assemblies result from diffusion across an underlying energy landscape. While bulk kinetic measurements rely on discrete state-like approximations to the energy landscape, single-molecule methods can project the free energy onto specific coordinates. With measures of the diffusion, one may establish a quantitative bridge between state-like kinetic measurements and the continuous energy landscape. We used an all-atom molecular dynamics simulation of the 70S ribosome (2.1 million atoms; 1.3 microseconds) to provide this bridge for specific conformational events associated with the process of tRNA translocation. Starting from a pre-translocation configuration, we identified sets of residues that collectively undergo rotary rearrangements implicated in ribosome function. Estimates of the diffusion coefficients along these collective coordinates for translocation were then used to interconvert between experimental rates and measures of the energy landscape. This analysis, in conjunction with previously reported experimental rates of translocation, provides an upper-bound estimate of the free-energy barriers associated with translocation. While this analysis was performed for a particular kinetic scheme of translocation, the quantitative framework is general and may be applied to energetic and kinetic descriptions that include any number of intermediates and transition states.

Magnesium fluctuations modulate RNA dynamics in the SAM-I riboswitch.

Experiments demonstrate that Mg(2+) is crucial for structure and function of RNA systems, yet the detailed molecular mechanism of Mg(2+) action on RNA is not well understood. We investigate the interplay between RNA and Mg(2+) at atomic resolution through ten 2-μs explicit solvent molecular dynamics simulations of the SAM-I riboswitch with varying ion concentrations. The structure, including three stemloops, is very stable on this time scale. Simulations reveal that outer-sphere coordinated Mg(2+) ions fluctuate on the same time scale as the RNA, and that their dynamics couple. Locally, Mg(2+) association affects RNA conformation through tertiary bridging interactions; globally, increasing Mg(2+) concentration slows RNA fluctuations. Outer-sphere Mg(2+) ions responsible for these effects account for 80% of Mg(2+) in our simulations. These ions are transiently bound to the RNA, maintaining interactions, but shuttled from site to site. Outer-sphere Mg(2+) are separated from the RNA by a single hydration shell, occupying a thin layer 3-5 Å from the RNA. Distribution functions reveal that outer-sphere Mg(2+) are positioned by electronegative atoms, hydration layers, and a preference for the major groove. Diffusion analysis suggests transient outer-sphere Mg(2+) dynamics are glassy. Since outer-sphere Mg(2+) ions account for most of the Mg(2+) in our simulations, these ions may change the paradigm of Mg(2+)-RNA interactions. Rather than a few inner-sphere ions anchoring the RNA structure surrounded by a continuum of diffuse ions, we observe a layer of outer-sphere coordinated Mg(2+) that is transiently bound but strongly coupled to the RNA.