用对比学习和交叉注意力预测酶促反应的催化常数与米氏常数。
EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
- 双编码器融合蛋白序列与底物结构，通过交叉注意力动态建模互作关系。
- 在2.3万组实验数据上，对Kcat和Km的预测决定系数分别达0.593和0.607。
- 适合药物研发、代谢工程等需要精准酶动力学参数的研究者使用。
准确预测酶动力学参数对药物发现、代谢工程和合成生物学至关重要。现有计算方法难以捕捉复杂的酶-底物相互作用，且多聚焦单一参数，忽视对催化周转数（Kcat）和米氏常数（Km）的联合预测。我们提出EnzyCLIP，一种基于对比学习与交叉注意力机制的新型双编码框架，可从蛋白质序列和底物分子结构中预测酶动力学参数。该方法结合ESM-2蛋白语言模型嵌入与ChemBERTa化学表征，采用受CLIP启发的架构，并引入双向交叉注意力以实现动态酶-底物交互建模。模型使用包含23,151条Kcat和41,174条Km实验验证数据的CatPred-DB数据库进行训练，通过InfoNCE对比损失与Huber回归损失联合优化，实现对取对数后动力学参数的预测。最终在测试集上取得Kcat预测R²为0.593、Km预测R²为0.607的性能。进一步利用XGBoost集成学习方法对嵌入表示建模，使Km预测性能提升至R²=0.61，同时保持对Kcat的良好表现。
原文摘要 · Abstract (English)
Accurate prediction of enzyme kinetic parameters is crucial for drug discovery, metabolic engineering, and synthetic biology applications. Current computational approaches face limitations in capturing complex enzyme-substrate interactions and often focus on single parameters while neglecting the joint prediction of catalytic turnover numbers (Kcat) and Michaelis-Menten constants (Km). We present EnzyCLIP, a novel dual-encoder framework that leverages contrastive learning and cross-attention mechanisms to predict enzyme kinetic parameters from protein sequences and substrate molecular structures. Our approach integrates ESM-2 protein language model embeddings with ChemBERTa chemical representations through a CLIP-inspired architecture enhanced with bidirectional cross-attention for dynamic enzyme-substrate interaction modeling. EnzyCLIP combines InfoNCE contrastive loss with Huber regression loss to learn aligned multimodal representations while predicting log10-transformed kinetic parameters. The model is trained on the CatPred-DB database containing 23,151 Kcat and 41,174 Km experimentally validated measurements, and achieved competitive performance with R2 scores of 0.593 for Kcat and 0.607 for Km prediction. XGBoost ensemble methods applied to the learned embeddings further improved Km prediction (R2 = 0.61) while maintaining robust Kcat performance.
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