RNA’s conformational flexibility enables diverse biological functions but makes structure-guided design difficult. Although precise control of 3D structure is essential, existing methods often overlook the molecular surface, which critically shapes intermolecular interactions and functional outcomes. To address this, we propose gRFold, a multimodal framework that incorporates molecular surface information as a core design constraint. gRFold employs two geometric encoders: a Frame-Averaged Geometric Surface Encoder (FA-GSE) to learn E(3)-invariant surface features, and an Equivariant Backbone Graph Perceptron (EBGP) to capture backbone topology. The structural representations are integrated via hierarchical cross-attention to guide autoregressive generation; at inference, candidates are re-ranked by agreement between their predicted folds and the target base-pairing pattern. Across three RNA inverse-folding benchmarks covering RNA-protein complexes, monomeric RNAs and independently curated RNA-Puzzles targets, gRFold achieves strong sequence recovery and structural fidelity under both secondary-structure and 3D refolding evaluations, and ablations show that the surface and backbone branches contribute complementary gains. These results highlight the potential of surface-aware geometric modeling for structure-guided RNA design.

Proteins are critical for various molecular functions, relying on their precise tertiary structures. This structure-sequence relationship is complex and degenerate, meaning multiple sequences can fold into a similar structure. The challenges in protein prediction, design, and modification increase with sequence complexity, while research on RNA-protein interactions, especially RNA-binding proteins (RBPs), is gaining importance. Large-scale pre-trained language models (LLMs) have shown promising results in handling biological sequences by treating them as natural language, though integrating spatial structures remains complex due to the need for specialized visual and 3D modeling approaches. We introduce a method to integrate protein 3D structural data within a sequence processing framework, converting 3D coordinates into discrete structure tokens using a VQ-VAE-like network. This simplifies the handling of 3D data, avoiding complex pipelines and facilitating a unified sequence-to-sequence processing model. Our approach demonstrates strong performance across a range of tasks, achieving high sequence recovery in inverse folding and protein-conditioned RNA design. These outstanding results demonstrate significant potential for application in complex biological systems research.

RNA-binding proteins (RBPs) play essential roles in post-transcriptional gene regulation via recognizing specific RNA molecules as well as modulating several key physiological processes in cellulo, represented by alternative splicing and RNA degradation. Despite extensive research, most existing approaches still rely on superficial sequence features or coarse structural representations, limiting their ability to capture the intricate nature of RBP-RNA interactions. The recent surge in large language models (LLMs), combined with advances in geometric deep learning for extracting three-dimensional representations, enables the integration of multi-modal, multi-scale biological data for precise modeling and biologically informed de novo RNA design. In this work, we curate and extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset, and introduce RBPtool, a multi-task, multi-resolution framework that combines a geometric vector perception (GVP) module together with a deep language model encoder to fuse sequence and structural information. Our tool achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset, while also supporting fine-grained level predictions and enabling de novo RNA design through a generative module conditioned on protein, cell-type, and specified species. RBPtool provides a fast and versatile platform for both fundamental RBP-RNA research and practical RNA drug design, delivering enhanced predictive accuracy and fine-grained structural insights.