通过检索增强提升酶底物互作预测在低同源性下的鲁棒性
Retrieval-Augmented Multimodal Learning for Enzyme-Substrate Interaction Prediction Under Low-Homology Shift
- 引入检索模块，在推理时动态找相似酶并融合信息
- 在低序列同源性场景下准确率显著提升，优于现有模型
- 适用于酶工程与药物发现，尤其适合数据稀疏场景
酶-底物互作（ESI）预测是生物催化发现和反应筛选中的基础计算任务。实际应用中，由于正样本标注稀疏且测试酶与训练集同源性低，预测面临挑战。为此，我们提出RAMMESI，一种基于检索增强的多模态框架，通过方向性跨模态交互建模和自适应融合学习显式酶-底物配对表示。为提升鲁棒性，该框架在推理时检索邻近酶，将其与查询底物重组并聚合配对预测结果作为上下文证据。为应对稀疏正样本，进一步采用不平衡感知加权BCE损失。在两个含序列同源性划分的基准上实验表明，RAMMESI性能稳定优异，尤其在低同源性条件下优势明显。此外，检索模块可即插即用地提升多个ESI骨干模型，说明检索是缓解同源性漂移的通用机制。
原文摘要 · Abstract (English)
Enzyme substrate interaction (ESI) prediction is a fundamental computational task for biocatalyst discovery and reaction screening in large biochemical spaces. In practical settings, ESI prediction is challenged by sparse positive supervision and low-homology distribution shift, where test enzymes share limited sequence identity with those observed during training. To address these challenges, we propose RAMMESI, a retrieval-augmented multimodal framework for robust ESI prediction. RAMMESI learns explicit pairwise enzyme-substrate representations through directional cross-modal interaction modeling and adaptive fusion. To enhance robustness, RAMMESI retrieves neighboring enzymes at inference time, recombines them with the query substrate, and aggregates the resulting pairwise predictions as contextual evidence. To improve learning under sparse positive supervision, we further adopt an imbalance-aware weighted-BCE objective. Experiments on two ESI benchmarks under sequence-identity-aware splits demonstrate that RAMMESI achieves consistently strong performance, with particular advantages in more challenging low-identity regimes. In addition, the retrieval module improves multiple ESI backbones in a plug-and-play manner, suggesting that retrieval provides a general mechanism for improving robustness under homology shift.
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