A Mathematical Model for RNA 3D Structures

Sixiang Zhang and Liming Cai ()
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Sixiang Zhang: School of Computing, University of Georgia, Athens, GE 30602, USA
Liming Cai: School of Computing, University of Georgia, Athens, GE 30602, USA

Mathematics, 2025, vol. 13, issue 8, 1-17

Abstract: The computational prediction of RNA three-dimensional (3D) structures remains a significant challenge, largely due to the limited understanding of RNA folding pathways. Although the scarcity of resolved native RNA structures has hindered the effectiveness of machine learning-based prediction methods, small, local structural motifs are both recurring and abundant in the available data. Precisely modeling these geometric motifs presents a promising approach to improving 3D structure prediction. In this paper, we introduce a novel mathematical model that represents RNA 3D structures as collections of interacting helices with concise geometric descriptions. By using a small set of parameters for each modeled helix, our method maps RNA strand segments onto helices within a 3D space, facilitating the effective assembly of large RNA structures. Preliminary tests on RNA sequences from the Protein Data Bank demonstrated the model’s potential in predicting key structural elements, including double helices, hairpin loops, and bulges.

Keywords: RNA 3D structure; nucleotide; base pair; RNA secondary structure; stem–loop; helix; double helix; bulge; rotation; translation; backbone conformation; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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