Biology subjects

Thayer, K.

Publications and source records attributed to Thayer, K..

4 recordsLinked to original sources

bioRxiv2026

Interplay of the ribosome A and CAR sites

Protein translation is a highly regulated process influenced by multiple factors at the initiation, elongation, and termination stages. One notable regulatory element of the ribosome is the CAR interaction surface, a three-residue motif in the structure of the ribosome composed of C1274 and A1427 of S. cerevisiae 18S rRNA (corresponding to C1054 and A1196 in E. coli 16S rRNA) and R146 of ribosomal protein Rps3. CAR is highly conserved and positioned adjacent to the amino-acyl (A site) decoding center. It establishes hydrogen bonds with the +1 codon next in line to enter the ribosome A site, acting as an extension of the tRNA anticodon and forming base-stacking interactions with nucleotide 34 of the tRNA. However, despite CARs enzymatically strategic positioning within the ribosome, its functional relationship with the A site remains poorly characterized. Using molecular dynamics (MD) simulations, we examined the interplay between the A site and CAR site, revealing sequence-dependent modulation of H-bonding and {pi}-stacking interactions within and between the two sites. These findings highlight the interplay between the A site and CAR site, suggesting a structural and functional connection between these two regions of the ribosome that may contribute to mRNA sequence-specific tuning of translation elongation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/714784v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1919efaorg.highwire.dtl.DTLVardef@15c4882org.highwire.dtl.DTLVardef@19c7782org.highwire.dtl.DTLVardef@16a1246_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2026

Systems analysis of ribosomal CAR-site dynamics

Developing approaches to link structure and function is an ongoing challenge in computational and structural biology. Using a systems-level framework, we present here an analysis pipeline in a Python package, mdsa-tools, that constructs network representations of structures in a time series of trajectory frames from molecular dynamics (MD) simulations. Here, we demonstrate its use on a ribosomal subsystem. The subsystem is centered on the CAR interaction surface, a "brake pad" adjacent to the aminoacyl (A-site) decoding center that tunes protein translation rates. We leverage unsupervised learning algorithms to explore the conformational landscape of behaviors visited by two versions of the subsystem (brake-on and brake-off) that differ at the codon 3 adjacent to the A-site codon. Our network representations of MD frames embody H-bond interactions between all pairwise combinations of residues in the system. By utilizing per-frame vector representations of network edges, we can apply standard clustering and dimensionality reduction methods to explore behavioral differences between the brake-on and brake-off versions of the system. K-means clustering of frame vectors revealed a striking separation of the two system versions, consistent with principal components analysis (PCA) embeddings and Uniform Manifold Approximation and Projection (UMAP) embeddings. Dissection of K-means centroids and PCA loadings highlighted H-bond interactions between residue pairs in the ribosomes peptidyl site (P site), suggesting potential allosteric signaling across the subsystem. Author summaryWith the impressive development of computational algorithms to successfully simulate the dynamics of biological molecules over time, the exploration and incorporation of systems modes of analysis is a natural next step to begin to understand the molecular dynamics behaviors that emerge from these experiments. Following the approaches of classical molecular genetics, we used a "computational genetics" paradigm where we introduced changes (mutations) in potentially important residues, changing their identities or modifying their chemical properties, and asked how the dynamic system responded to these changes, viewing the simulations as a series of movie frames of the dynamic structure over time. Starting with network representations of each frames structure, where the nodes are residues, and the edges denote H-bond interactions between the residues, we used several unsupervised machine learning algorithms to uncover behavioral changes in the different mutated versions of the system. Applied to our ribosome neighborhood, this revealed unexpected changes in behavior at the ribosome peptidyl site (P site) in response to mutating mRNA residues on the other side of the aminoacyl site (A site) codon, suggesting long-range allosteric interactions across the neighborhood.

systems biology↗

bioRxiv2022

The CAR mRNA-Interaction Surface is a Zipper Extension of the Ribosome A Site

The ribosome CAR interaction surface behaves like an extension of the decoding center A site and has H-bond interactions with the +1 codon that is next in line to enter the A site. Through molecular dynamics simulations, we investigated the codon sequence specificity of this CAR-mRNA interaction and discovered a strong preference for GCN codons, suggesting that there may be a sequence-dependent layer of translational regulation dependent on the CAR interaction surface. Dissection of the CAR-mRNA interaction through nucleotide substitution experiments showed that the first nucleotide of the +1 codon dominates over the second nucleotide position, consistent with an energetically favorable zipper-like activity that emanates from the A site through the CAR-mRNA interface. The +1 codon/CAR interaction is also affected by the identity of nucleotide 3 of +1 GCN codons which influences the stacking of G and C. Clustering analysis suggests that the A site decoding center adopts different neighborhood substates that depend on the identity of the +1 codon.

biophysics↗

bioRxiv2018

Towards a universal structural and energetic model for prokaryotic promoters

With almost no consensus promoter sequence in prokaryotes, recruitment of RNA polymerase (RNAP) to precise transcriptional start sites (TSSs) has remained an unsolved puzzle. Uncovering the underlying mechanism is critical for understanding the principle of gene regulation. We attempted to search the hidden code in ~16500 promoters, of twelve prokaryotes representing two kingdoms, in their structure and energetics. Twenty eight fundamental parameters of DNA structure including backbone angles, base pair axis, inter base pair and intra base pair parameters were used and information was extracted from X-ray crystallography (XRC) data. Three parameters (solvation energy, hydrogen bond energy and stacking energy) were selected for creating energetics profiles using in-house programs. DNA was found to be inherently designed to undergo a change in every parameter undertaken, from some distance upstream of TSSs to adopt a signature state at these locations in all prokaryotes. These signature states might be the universal hidden codes recognised by RNAP. This observation was reiterated when randomly selected promoter sequences (with little sequence conservation) were subjected to structure generation; all developed into very similar three dimensional structures, quite distinct from those of conventional B-DNA and coding sequences. Fine structural details at important motifs (viz. -11, -35, -75 positions relative to TSS) of promoters reveal novel and pointed insights for RNAP interaction at these locations; it could be correlated that how some particular structural changes at -11 region may allow insertion of RNAP amino acids in inter-base pair space as well as facilitate the flipping out of bases from DNA duplex.

genomics↗