RIGA-Fold: A General Framework for Protein Inverse Folding via Recurrent Interaction and Geometric Awareness

• URL: http://arxiv.org/abs/2602.04637v1
• Date: Wed, 04 Feb 2026 15:07:13 GMT
• Title: RIGA-Fold: A General Framework for Protein Inverse Folding via Recurrent Interaction and Geometric Awareness
• Authors: Sisi Yuan, Jiehuang Chen, Junchuang Cai, Dong Xu, Xueliang Li, Zexuan Zhu, Junkai Ji,
• Abstract summary: RIGA-Fold is a framework that synergizes Recurrent Interaction with Geometric Awareness.<n>To bridge the gap between structural and sequence modalities, we introduce an enhanced variant, RIGA-Fold*.<n>Our framework significantly outperforms state-of-the-art baselines in both sequence recovery and structural consistency.
• Score: 14.42786271490985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Protein inverse folding, the task of predicting amino acid sequences for desired structures, is pivotal for de novo protein design. However, existing GNN-based methods typically suffer from restricted receptive fields that miss long-range dependencies and a "single-pass" inference paradigm that leads to error accumulation. To address these bottlenecks, we propose RIGA-Fold, a framework that synergizes Recurrent Interaction with Geometric Awareness. At the micro-level, we introduce a Geometric Attention Update (GAU) module where edge features explicitly serve as attention keys, ensuring strictly SE(3)-invariant local encoding. At the macro-level, we design an attention-based Global Context Bridge that acts as a soft gating mechanism to dynamically inject global topological information. Furthermore, to bridge the gap between structural and sequence modalities, we introduce an enhanced variant, RIGA-Fold*, which integrates trainable geometric features with frozen evolutionary priors from ESM-2 and ESM-IF via a dual-stream architecture. Finally, a biologically inspired ``predict-recycle-refine'' strategy is implemented to iteratively denoise sequence distributions. Extensive experiments on CATH 4.2, TS50, and TS500 benchmarks demonstrate that our geometric framework is highly competitive, while RIGA-Fold* significantly outperforms state-of-the-art baselines in both sequence recovery and structural consistency.

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