Coarse RNA coordinate representations are widely used, yet the biological information they encode remains unquantified. We introduce FORGE, which converts a seven-atom RNA geometry representation into 935 interpretable descriptors and reports which residue-level annotations this geometry supports. On 4,135 post-2025 RNA chains, FORGE recovered 64.6% of native nucleotides; a six-atom control lacking the glycosidic nitrogen retained 58.5%, locating most of this signal in phosphate–sugar geometry. Confidence was sharply graded: abstaining from the least-confident half of positions raised accuracy to 94.4%, yet many chains remained only partially identifiable. The same descriptors predicted base-pair state far better than a DMS-like proxy or protein-proximal context. Native–decoy, OpenKnot and solved-pseudoknot analyses showed that nucleotide identifiability, foldability and experimental design score are separable: AlphaFold 3 reproduced the experimental fold for one of four AI-designed constructs and none of the sequences FORGE read from their geometry. FORGE provides a reproducible audit layer for RNA structural interpretation.
RNA function is dictated by 3D architecture. Although 2D structural models based on canonical Watson-Crick base pairs are widely used, they often fail to capture the non-canonical interactions, tertiary contacts, and coaxial stacking important for biological activity. We have developed RNAbridge, a comprehensive databa...
Damian Zakrzewski, M. Antczak, T. Zok· bioRxiv· 0 citations
Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator t...
Andrea Zerio, Yi-Song Yao, Alessandro Micheli et al.· 0 citations
NucleicBERT is developed, a self-supervised masked-language model that learns contextual representations from single sequences without evolutionary information that advances RNA structure prediction and informs how large language models encode biological information.
Utkarsh Upadhyay, Julian Herold, Markus Götz et al.· Nature Machine Intelligence· 1 citation
This work introduces DNAreader, the first predictor specifically designed to predict DBRs in the structured and disordered sequence regions, and develops the DNAreaderDBIDR module, which accurately predicts DNA-binding IDRs, providing flexibility to identify DBRs within IDRs or to predict entire disordered DNA-binding...
Different RNA sequences do not have equal access to nearby structural sequence space, but how strongly local sequence neighborhoods depend on starting-sequence identity and individual nucleotide positions remains unclear. Here, we compared the fully mutated neighborhoods of three isolated 20-nucleotide RNA seeds inspir...
M. Tirumalai, R. Rastogi, George E. Fox· Journal of Molecular Evoluti...· 0 citations
RNA inverse folding asks for an RNA sequence whose prescribed secondary structure is the unique maximum-base-pair compatible fold. In the four-letter Watson-Crick model (A-U and C-G pairs only, no pseudoknots, and zero minimum base-pair span), Hales et al. introduced a separated-coloring certificate and an even-odd dev...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
