Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914593603846144 |
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| author | Wang, Mingqing Nie, Zhiwei Vasilakos, Athanasios V. He, Yonghong Ren, Zhixiang |
| author_facet | Wang, Mingqing Nie, Zhiwei Vasilakos, Athanasios V. He, Yonghong Ren, Zhixiang |
| contents | Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals that underlie function. Here we introduce ProtDiS, a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings into biologically grounded and task-relevant dimensions. Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression, yielding structural features that are more specific, independent, and information-efficient, and achieving consistent improvements across twelve downstream tasks, with the largest gains under structure-based splits. Protein- and residue-level analyses further show that ProtDiS differentiates proteins with similar folds but divergent functions and captures fine-grained biophysical signals critical. These findings suggest that knowledge-guided decomposition provides a general and interpretable approach for structuring latent spaces in protein structural modeling. The source code and implementation details are publicly available at https://github.com/AI-HPC-Research-Team/ProtDiS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23960 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition Wang, Mingqing Nie, Zhiwei Vasilakos, Athanasios V. He, Yonghong Ren, Zhixiang Biomolecules Machine Learning Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals that underlie function. Here we introduce ProtDiS, a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings into biologically grounded and task-relevant dimensions. Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression, yielding structural features that are more specific, independent, and information-efficient, and achieving consistent improvements across twelve downstream tasks, with the largest gains under structure-based splits. Protein- and residue-level analyses further show that ProtDiS differentiates proteins with similar folds but divergent functions and captures fine-grained biophysical signals critical. These findings suggest that knowledge-guided decomposition provides a general and interpretable approach for structuring latent spaces in protein structural modeling. The source code and implementation details are publicly available at https://github.com/AI-HPC-Research-Team/ProtDiS. |
| title | Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2605.23960 |