Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding. Proteins instead adopt complex three-dimensional conformations organized around secondary structure elements, such as $α$-helices and $β$-sheets, which encode recurring local motifs and stabilizing hydrogen-bond interactions. In this work, we introduce a secondary-structure-aware graph neural network for protein representation learning. Residue-level node representations are augmented with secondary structure assignments, and graph edges are constructed from hydrogen...
| Authors | Mohamed Mouhajir, Limei Wang, El Houcine Bergou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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