Biology subjects

Gezelle, J. G.

Publications and source records attributed to Gezelle, J. G..

3 recordsLinked to original sources

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

Functional relevance of CASP16 nucleic acid predictions as evaluated by structure providers

Accurate biomolecular structure prediction enables the prediction of mutational effects, the speculation of function based on predicted structural homology, the analysis of ligand binding modes, experimental model building and many other applications. Such algorithms to predict essential functional and structural features remain out of reach for biomolecular. Here, we report quantitative and qualitative evaluation of nucleic acid structures for the CASP16 blind prediction challenge by 12 of the experimental groups who provided nucleic acid targets. Blind predictions accurately model secondary structure and some aspects of tertiary structure, including reasonable global folds for some complex RNAs, however, predictions often lack accuracy in the regions of highest functional importance. All models have inaccuracies in non-canonical regions where, e.g., the nucleic-acid backbone bends or a base forms a non-standard hydrogen bond. These bends and non-canonical interactions are integral to form functionally important regions such as RNA enzymatic active sites. Additionally, the modeling of conserved and functional interfaces between nucleic acids and ligands, proteins, or other nucleic acids remains poor. For some targets, the experimental structures may not represent the only structure the biomolecular complex occupies in solution or in its functional life-cycle, posing a future challenge for the community.

biophysics↗

bioRxiv2024

The pseudoknot structure of a viral RNA reveals a conserved mechanism for programmed exoribonuclease resistance

Exoribonuclease-resistant RNAs (xrRNAs) are viral RNA structures that block degradation by cellular 5'-3' exoribonucleases to produce subgenomic viral RNAs during infection. Initially discovered in flaviviruses, xrRNAs have since been identified in wide range of RNA viruses, including those that infect plants. High sequence variability among viral xrRNAs raises questions about the shared molecular features that characterize this functional RNA class. Here, we present the first structure of a plant-virus xrRNA in its active exoribonuclease-resistant conformation. The xrRNA forms a 9 base pair pseudoknot that creates a knot-like topology similar to that of flavivirus xrRNAs, despite lacking sequence similarity. Biophysical assays confirm a compact pseudoknot structure in solution, and functional studies validate its relevance both in vitro and during infection. Our study reveals how viral RNAs achieve a common functional outcome through highly divergent sequences and identifies the knot-like topology as a defining feature of xrRNAs.

biochemistry↗