Towards Data-Efficient Deep Learning for RNA 3D Structure Prediction and Design

Abstract

RNA 3D structure prediction is essential for understanding regulatory mechanisms, catalysis, and therapeutic RNA design, yet progress has lagged behind proteins due to limited structural data and the complexity of RNA folding. This work proposes a data-efficient, physics-informed deep learning framework for full atomistic prediction of transfer RNA (tRNA) tertiary structures directly from sequence. Our approach will integrate pretrained RNA embeddings, predicted secondary structure constraints, and SE(3)-equivariant graph attention to model long-range geometric relationships. A two-stage design will first predict global phosphate backbone coordinates, then reconstruct nucleobase atoms using a local geometry-aware decoder. A multi-objective loss will combine geometric accuracy with chemical and biophysical plausibility to enforce valid torsion angles, base-pairing, and steric constraints. We will benchmark against physics-based (VFold) and neural network–based (DeepFoldRNA) models to assess generalization under data scarcity. Ultimately, this framework aims to advance RNA 3D modeling with improved stability, interpretability, and capacity to generalize beyond well-characterized RNA families, supporting future applications in rational RNA engineering and structure-guided RNA design.