Biology subjects

Rudolfs, B.

Publications and source records attributed to Rudolfs, B..

6 recordsLinked to original sources

bioRxiv2026

Cryo-EM of a nucleotide-polymerizing ribozyme enables its predictive improvement

Ribozymes capable of self-replication from nucleotides would have been central to the hypothesized RNA World. The leading laboratory models for such molecules were converted from a class I ligase by in vitro evolution but then developed without 3D structures. Here, scaffolded cryo-EM of the substrate-free tC19Z RNA polymerase ribozyme at 3.1 [A] resolution shows how this conversion was achieved. An accessory domain evolved from random sequence grips the ancestral ligase through a loop-loop contact, a seam of magnesium ions, and a six-base stack, and rebuilds the ligases substrate binding site from different residues of its own. A previously unrecognized pairing, present before substrate binds, sequesters the 5' end that must otherwise pair with the template. Compensatory mutations to the ribozyme and template, designed to break this ectopic pairing, increase the extension rate. These results suggest that accelerating RNA structure determination may speed progress toward nucleotide-based self-replication.

biochemistry↗

bioRxiv2026

De novo design of RNA pseudoknots with deep learning

RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative AI methods matched experienced human designers in solving most blind challenges, evaluated by single-nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryogenic electron microscopy. AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by an RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.

biophysics↗

bioRxiv2025

Template-based RNA structure prediction advanced through a blind code competition

Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

biophysics↗

bioRxiv2025

Structural evidence for metal ion catalysis in the ribosome

Ribosomes synthesize proteins with an RNA-only active site across all domains of life, yet the details of the catalytic mechanism have remained elusive despite decades of high-resolution ribosome structures. Here, we provide structural evidence for the involvement of two metal ions in peptide bond formation, drawn from ribosome structures spanning bacteria, archaea, and eukaryotes. These metal ions reside in the peptidyl transferase center, one of them adjacent to a universally conserved stack of three base triples, reminiscent of the catalytic triplex in group I/II introns and the spliceosome, which catalyze pre-mRNA splicing. The second metal ion is positioned to stabilize the oxyanion of the tetrahedral intermediate. Metal ion catalysis thus emerges as a recurring mechanistic theme across the central dogma spanning protein synthesis, RNA splicing, and nucleic acid replication.

biochemistry↗

bioRxiv2024

Scaffold-enabled high-resolution cryo-EM structure determination of RNA

Cryo-EM structure determination of protein-free RNAs has remained difficult with most attempts yielding low to moderate resolution and lacking nucleotide-level detail. These difficulties are compounded for small RNAs as cryo-EM is inherently more difficult for lower molecular weight macromolecules. Here we present a strategy for fusing small RNAs to a group II intron that yields high resolution structures of the appended RNA, which we demonstrate with the 86-nucleotide thiamine pyrophosphate (TPP) riboswitch, and visualizing the riboswitch ligand binding pocket at 2.5 [A] resolution. We also determined the structure of the ligand-free apo state and observe that the aptamer domain of the riboswitch undergoes a large-scale conformational change upon ligand binding, illustrating how small molecule binding to an RNA can induce large effects on gene expression. This study both sets a new standard for cryo-EM riboswitch visualization and offers a versatile strategy applicable to a broad range of small to moderate-sized RNAs, which were previously intractable for high-resolution cryo-EM studies.

biochemistry↗

bioRxiv2021

Redesigning the Eterna100 for the Vienna 2 folding engine

The rational design of RNA is becoming important for rapidly developing technologies in medicine and biochemistry, spurring development of numerous RNA secondary structure design algorithms and benchmarks to evaluate their performance. However, the problem of RNA design is dependent upon the reverse problem of RNA structure prediction through "folding engines" that predict structure from sequence. We hypothesized that differences in RNA folding engines could impact design algorithms, and recruited an online community of RNA design experts to modify the widely-used RNA secondary structure design benchmark, Eterna100, to address unsolvability of some cases when changing the folding engine used (Vienna 1.8 updated to Vienna 2.6). We tested this new Eterna100-V2 benchmark with five RNA design algorithms, and found that while overall rankings remained similar, the performance of RNA design algorithms that depended on folding engines in their training did indeed depend on which underlying parameter set was used in training. This work demonstrates that the design "difficulty" of RNA structures is intrinsically linked to thermodynamic models, and suggests that future RNA design algorithms that are agnostic to thermodynamic models will result in optimal performance and development. Eterna100-V1 and Eterna100-V2 benchmarks and example solutions are freely available at https://github.com/eternagame/eterna100-benchmarking. Author SummaryDesigning RNA molecules that fold to a desired target structure is an algorithmic problem gathering increasing attention due to the emergence of RNA-based therapies and the need for rational design of RNAs. The Eterna100 dataset, a collection of target structures with increasing design difficulty, designed and selected by players of the crowdsourced RNA game Eterna, has been widely used to benchmark RNA design algorithms. However, these puzzles were originally developed using the now-deprecated version 1 of the ViennaRNA folding engine. In this manuscript, we introduce an updated benchmark, called the Eterna100-V2. We found that nineteen puzzles using Vienna 1 were unsolvable in Vienna 2, but that Eterna participants were able to re-design the puzzles with minimal modifications to make them solvable in Vienna 2. We confirmed that the rankings of 5 RNA design algorithms remained consistent between Eterna100-V1 and -V2. However, discrepancies in performance from algorithms that relied on thermodynamic models in their training suggest that algorithms will benefit from being agnostic to thermodynamic models as these models continue to improve.

bioinformatics↗