通过知识引导分解，让蛋白结构表示更清晰可解释。
Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
- 用信息瓶颈原理分离出与功能相关的独立结构特征
- 在12个下游任务中均有提升，结构分割下效果最显著
- 适合研究蛋白功能差异和可解释性建模的学者
蛋白质在复杂的三维结构中编码多样功能，但多数深度学习表示仍高度耦合，掩盖了底层生物物理信号。本文提出ProtDiS，一种基于知识引导的框架，将预训练的蛋白微环境嵌入分解为生物基础且任务相关的维度。受信息瓶颈原理启发，ProtDiS学习兼具信息量与压缩性的表示，得到更具体、独立且信息高效的结构特征，在十二项下游任务中均实现一致提升，尤其在基于结构的划分下表现最佳。蛋白级与残基级分析显示，ProtDiS能区分结构相似但功能不同的蛋白，并捕捉关键的细微生物物理信号。结果表明，知识引导分解为蛋白结构建模中的隐空间构建提供了通用且可解释的方法。源代码与实现细节已公开于 https://github.com/AI-HPC-Research-Team/ProtDiS。
原文摘要 · Abstract (English)
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.
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