Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914298779926528 |
|---|---|
| author | Wang, Shih-Hsin Huang, Yuhao Transue, Taos Baker, Justin Forstater, Jonathan Strohmer, Thomas Wang, Bao |
| author_facet | Wang, Shih-Hsin Huang, Yuhao Transue, Taos Baker, Justin Forstater, Jonathan Strohmer, Thomas Wang, Bao |
| contents | Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based methods often face challenges in learning multiscale representations and modeling long-range dependencies efficiently. In this work, we propose an efficient multiscale graph-based learning framework tailored to proteins. Our proposed framework contains two crucial components: (1) It constructs a hierarchical graph representation comprising a collection of fine-grained subgraphs, each corresponding to a secondary structure motif (e.g., $α$-helices, $β$-strands, loops), and a single coarse-grained graph that connects these motifs based on their spatial arrangement and relative orientation. (2) It employs two GNNs for feature learning: the first operates within individual secondary motifs to capture local interactions, and the second models higher-level structural relationships across motifs. Our modular framework allows a flexible choice of GNN in each stage. Theoretically, we show that our hierarchical framework preserves the desired maximal expressiveness, ensuring no loss of critical structural information. Empirically, we demonstrate that integrating baseline GNNs into our multiscale framework remarkably improves prediction accuracy and reduces computational cost across various benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00862 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs Wang, Shih-Hsin Huang, Yuhao Transue, Taos Baker, Justin Forstater, Jonathan Strohmer, Thomas Wang, Bao Machine Learning Artificial Intelligence Numerical Analysis 68T07 Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based methods often face challenges in learning multiscale representations and modeling long-range dependencies efficiently. In this work, we propose an efficient multiscale graph-based learning framework tailored to proteins. Our proposed framework contains two crucial components: (1) It constructs a hierarchical graph representation comprising a collection of fine-grained subgraphs, each corresponding to a secondary structure motif (e.g., $α$-helices, $β$-strands, loops), and a single coarse-grained graph that connects these motifs based on their spatial arrangement and relative orientation. (2) It employs two GNNs for feature learning: the first operates within individual secondary motifs to capture local interactions, and the second models higher-level structural relationships across motifs. Our modular framework allows a flexible choice of GNN in each stage. Theoretically, we show that our hierarchical framework preserves the desired maximal expressiveness, ensuring no loss of critical structural information. Empirically, we demonstrate that integrating baseline GNNs into our multiscale framework remarkably improves prediction accuracy and reduces computational cost across various benchmarks. |
| title | Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs |
| topic | Machine Learning Artificial Intelligence Numerical Analysis 68T07 |
| url | https://arxiv.org/abs/2602.00862 |