Biology subjects

Krizanc, D.

Publications and source records attributed to Krizanc, D..

11 recordsLinked to original sources

bioRxiv2026

Anticodon nucleotide modifications affect translational tuning by the ribosomal CAR surface

Nucleotide modifications of the tRNA anticodon can affect protein translation fidelity and speed. Chemical modifications of the anticodon nucleotide 34 are regulated under cellular stress and associated with several translational defects and pathologies. Here, we investigate how these modifications influence A-site codon recognition interactions and their coupling to the CAR site that lies adjacent to nucleotide 34 in the ribosome. The conserved three-residue CAR interface hydrogen bonds in a sequence-dependent manner to the mRNA +1 codon 3-adjacent to the A-site codon and is implicated in tuning translational speed. The C of CAR is pi-stacked with the nucleotide 34 of the A site tRNA anticodon. The A site and the CAR site influence each others hydrogen bonding and stacking interactions, and these codon-adjacency effects potentially provide a layer of regulation affecting translational fidelity and kinetics. Through molecular dynamics simulations of a subsystem of a translocating ribosome IRES-model, we observed that nucleotide 34 modifications affect the hydrogen bonding and stacking interactions at the A site and CAR site as well as CARs influence on the A site interactions. Integrating these results with gene sequence and ribosome profiling analyses, we propose that nucleotide 34 modifications help modulate CARs sequence-dependent tuning of translation in response to cellular stress. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/740946v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1971867org.highwire.dtl.DTLVardef@c31822org.highwire.dtl.DTLVardef@6597f6org.highwire.dtl.DTLVardef@1dbd747_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

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↗

bioRxiv2025

StACKER, A TOOL FOR SYSTEMS LEVEL ANALYSIS OF BASE STACKING IN NUCLEOTIDE-RICH STRUCTURES

Nucleic acid macromolecules can undergo significant structural changes--often mediated by nucleotide modification or molecular interactions. These alterations can be critical to their function. An ongoing challenge is to develop useful tools to quantify system-wide changes in nucleic acid-rich structures. We introduce StACKER, a robust Python package for observing conformational changes in a nucleic acid structure through its pi-stacking of base rings. StACKER creates System Stacking Fingerprints (SSFs) which highlight the landscape of pi-stacking throughout a molecule and can be used to show widespread conformational adjustments. Additionally, StACKERs Pairwise Stacking Fingerprint (PSF) can further characterize pi-stacking in a select residue pair, showing how the effects of localized residue changes spread through a nucleic acid system. We apply StACKER to molecular dynamics (MD) simulations of a subsystem of the ribosome to reveal that alternative codons at the ribosome A-site and 3 adjacent +1 codon position induce allosteric structure changes in the decoding center neighborhood, leading to a toggle between two conformational states. Through previous analysis of ribosome profiling data, we link these states to an observed fast/slow translation phenotype (Sun et al. 2024). We benchmark StACKER alongside other lenses for observing allostery to demonstrate its use in assessing allosteric shifts and its relevance to future structure-to-function studies.

bioinformatics↗

bioRxiv2024

GNN codon adjacency regulates protein translation

The central dogma treats the ribosome as a molecular machine that reads one mRNA codon at a time as it adds each amino acid to its growing peptide chain. However, this and previous studies suggest that ribosomes actually perceive pairs of adjacent codons as they take three-nucleotide steps along the mRNA. We examined GNN codons which we find are surprisingly overrepresented in eukaryote protein-coding open reading frames (ORFs), especially immediately after NNU codons. Ribosome profiling experiments in yeast revealed that ribosomes with NNU at their aminoacyl (A) site have particularly elevated densities when NNU is immediately followed (3) by a GNN codon, indicating slower mRNA threading of the NNU codon from the ribosomes A to peptidyl (P) sites. Moreover, if the assessment was limited to ribosomes that have only recently arrived at the next codon, by examining 21-nucleotide ribosome footprints (21-nt RFPs), elevated densities were observed for multiple codon classes when followed by GNN. This striking translation slowdown at adjacent 5-NNN GNN codon pairs is likely mediated in part by the ribosomes CAR surface which acts as an extension of the A-site tRNA anticodon during ribosome translocation and interacts through hydrogen bonding and pi stacking with the GNN codon. The functional consequences of 5-NNN GNN codon adjacency are expected to influence the evolution of protein coding sequences. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/583757v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@d29087org.highwire.dtl.DTLVardef@e8f7dorg.highwire.dtl.DTLVardef@1ee5792org.highwire.dtl.DTLVardef@b750fb_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

molecular 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↗

bioRxiv2020

Arginine Methylation Regulates Ribosome CAR Function

The ribosome CAR interaction surface is hypothesized to provide a layer of translation regulation through hydrogen-bonding to the +1 mRNA codon that is next to enter the ribosome A site during translocation. The CAR surface consists of three residues, 16S/18S rRNA C1054, A1196 (E. coli 16S numbering), and R146 of yeast ribosomal protein Rps3. R146 can be methylated by the Sfm1 methyltransferase which is downregulated in stressed cells. Through molecular dynamics analysis, we show here that methylation of R146 compromises the integrity of CAR by reducing the pi stacking of the R146 guanidinium group with A1196, leading to reduced CAR hydrogen-bonding with the +1 codon. We propose that ribosomes assembled under stressed conditions have unmethylated R146, resulting in elevated CAR/+1 codon interactions, which tunes translation levels in response to the altered cellular context.

biophysics↗

bioRxiv2020

GCN sensitive protein translation in yeast

Levels of protein translation by ribosomes are governed both by features of the translation machinery as well as sequence properties of the mRNAs themselves. We focus here on a striking three-nucleotide periodicity, characterized by overrepresentation of GCN codons and underrepresentation of G at the second position of codons, that is observed in Open Reading Frames (ORFs) of mRNAs. Our examination of mRNA sequences in Saccharomyces cerevisiae revealed that this periodicity is particularly pronounced in the initial codons--the ramp region--of ORFs of genes with high protein expression. It is also found in mRNA sequences immediately following non-standard AUG start sites, located upstream or downstream of the standard annotated start sites of genes. To explore the possible influences of the ramp GCN periodicity on translation efficiency, we tested edited ramps with accentuated or depressed periodicity in two test genes, SKN7 and HMT1. Greater conformance to (GCN)n was found to significantly depress translation, whereas disrupting conformance had neutral or positive effects on translation. Our recent Molecular Dynamics analysis of a subsystem of translocating ribosomes in yeast revealed an interaction surface that H-bonds to the +1 codon that is about to enter the ribosome decoding center A site. The surface, comprised of 16S/18S rRNA C1054 and A1196 (E. coli numbering) and R146 of ribosomal protein Rps3, preferentially interacts with GCN codons, and we hypothesize that modulation of this mRNA-ribosome interaction may underlie GCN-mediated regulation of protein translation. Integration of our expression studies with large-scale reporter studies of ramp sequence variants suggests a model in which the C1054-A1196-R146 (CAR) interaction surface can act as both an accelerator and braking system for ribosome translation.

molecular biology↗

bioRxiv2020

A Ribosome Interaction Surface Sensitive to mRNA GCN Periodicity

GCN codons are over-represented in initial codons of ORFs of prokaryote and eukaryote mRNAs. We describe a ribosome rRNA-protein surface that interacts with an mRNA GCN codon when next-in-line for the ribosome A site. The interaction surface is comprised of the edges of two stacked rRNA bases: the Watson-Crick edge of 16S/18S rRNA C1054 and adjacent Hoogsteen edge of A1196 (Escherichia coli 16S rRNA numbering). Also part of the interaction surface, the planar guanidinium group of a conserved Arginine (R146 of yeast ribosomal protein Rps3) is stacked adjacent to A1196. On its other side, the interaction surface is anchored to the ribosome A site through base stacking of C1054 with the wobble anticodon base of the A-site tRNA. Using Molecular Dynamics simulations of a 495-residue subsystem of translocating ribosomes, we observe base pairing of C1054 to nucleotide G at position 1 of the next-in-line codon, consistent with previous cryo-EM observations, and hydrogen bonding of A1196 and R146 to C at position 2. Hydrogen bonding to both of these codon positions is significantly weakened when C at position 2 is changed to G, A or U. These sequence-sensitive mRNA-ribosome interactions at the C1054-A1196-R146 (CAR) surface potentially contribute to GCN-mediated regulation of protein translation.

biophysics↗

bioRxiv2020

Ecotype Simulation 2: An improved algorithm for efficiently demarcating microbial species from large sequence datasets

BackgroundMicrobial systematists have used molecular cutoffs to classify the vast diversity present within a natural microbial community without invoking ecological theory. The use of ecological theory is needed to identify whether or not demarcated groups are the ecologically distinct, fundamental units (ecotypes), necessary for understanding the system. Ecotype Simulation, a Monte-Carlo approach to modeling the evolutionary dynamics of a microbial population based on the Stable Ecotype Model of microbial speciation, has proven useful for finding these fundamental units. For instance, predicted ecotypes of Synechococcus forming microbial mats in Yellowstone National Park hot springs, which were previously considered to be a single species based on phenotype, have been shown to be ecologically distinct, with specialization to different temperature and light levels. Unfortunately, development of high-throughput DNA sequencing methods has outpaced the ability of the program to analyze all of the sequence data produced. ResultsWe developed an improved version of the program called Ecotype Simulation 2, which can rapidly analyze alignments of very large sequence datasets. For instance, while the older version takes days to analyze 200 sequences, the new version can analyze 1.92 x 105 sequences in about six hours. The faster simulation identified similar ecotypes as found with the slower version, but from larger amounts of sequence data. ConclusionsBased on ecological theory, Ecotype Simulation 2 provides a much-needed approach that will help guide microbial ecologists and systematists to the natural, fundamental units of bacterial diversity.

bioinformatics↗

bioRxiv2019

Delving below the species level to characterize the ecological diversity within the global virome: An exploration of West Nile Virus

Efforts to describe the diversity of viruses have largely focused on classifying viruses at the species level. However, substantial ecological diversity, both in virulence level and host range, is known within virus species. Here we demonstrate a proof of concept for easily discovering ecological diversity within a virus species taxon. We have focused on the West Nile Virus to take advantage of its broad host range in nature. We produced a genome-based phylogeny of world diversity of WNV and then used Ecotype Simulation 2 to hypothesize demarcation of genomes into 69 putative ecotypes (ecologically distinct populations), based only on clustering of genome sequences. Then we looked for evidence of ecological divergence among ecotypes based on differences in host bird associations within the Connecticut-New York region. Our results indicated significant heterogeneity among ecotypes for their associations with different bird hosts. Ecological diversity within other zoonotic viruses could be easily discovered using this approach. Opportunities for extending this line of research to human associations of virus ecotypes are limited by missing geographic metadata on human samples.

microbiology↗