Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
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 ...
| Authors | Mingqing Wang, Zhiwei Nie, Athanasios V. Vasilakos et al. |
|---|---|
| Primary category | q-bio.BM |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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