Hierarchical Discrete Representations for Coarse-to-Fine Protein Conformation Generation

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ID: 325008
2026
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Abstract
MOTIVATION: Protein conformation generation remains a fundamental challenge in structural biology and machine learning. Proteins are highly dynamic macromolecules, and their functions are governed not only by static 3D structures but also by their conformational flexibility. Therefore, generating a diverse ensemble of physically plausible structures is crucial for modeling conformational heterogeneity, characterizing intrinsically disordered proteins (IDPs), and supporting structure-based drug discovery. However, existing generative models often operate in continuous coordinate space. This approach makes it difficult to impose structural priors or handle complex, long-range dependencies, frequently leading to a significant trade-off where models must sacrifice structural validity to ensure conformational diversity. RESULTS: To overcome these limitations, we propose a novel approach that learns hierarchical discrete representations of protein structures using vector quantization. Inspired by the success of VQ-VAE models in vision, we discretize residue-level structural contexts into learnable codebooks at multiple levels of granularity. Our framework first constructs a coarse-grained scaffold that captures global topology and secondary structures, and subsequently conditions on this scaffold to progressively refine the fine-grained local geometry. This coarse-to-fine generation mechanism enables both efficient sampling and highly accurate reconstruction. Through extensive experiments, we demonstrate that our method outperforms state-of-the-art models such as ESMDiff across challenging benchmark datasets, including BPTI MD trajectories and conformational-changing pairs (Apo/holo and fold-switch). Our model mitigates the trade-off between conformational diversity and structural validity, generating realistic ensembles while providing interpretable and scalable representations of protein geometry. Ultimately, this work offers a powerful new paradigm for flexible biomolecular modeling. AVAILABILITY: The code is available at https://github.com/vanha9/hierarchical_conformation_generation. Archived at https://zenodo.org/records/21817083.
Reference Key
openalex_W7203475195 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors SeokJun On, Yujin Jeong, Kang-Hyeon Kim, Kyungheon Kang, Eun‐Sol Kim
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag611
URL
https://doi.org/10.1093/bioinformatics/btag611
Keywords Keywords not found

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