用生物化学扰动提升酶动力学预测的泛化能力
Pseudodata-guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction
- 通过生物化学启发的扰动增强与不变表征学习提升鲁棒性
- 在序列差异大的外部数据上对kcat和Km预测精度显著提升
- 轻量模块可无缝集成，适合真实酶工程场景
准确预测酶动力学参数对理解催化机制和指导酶工程至关重要。然而，现有基于深度学习的酶-底物相互作用（ESI）预测模型在序列差异较大的分布外（OOD）情况下性能下降，限制了其在生物学相关扰动下的鲁棒性。我们提出O²DENet，一个轻量级、即插即用的模块，通过生物化学启发的扰动增强与不变表征学习提升分布外泛化能力。O²DENet引入酶-底物对的扰动，并强制原始与增强后酶-底物对表示的一致性，以实现对分布偏移的不变性。当集成至代表性ESI模型时，O²DENet在严格的序列相似性基OOD基准上持续提升kcat和Km的预测性能，在准确性和鲁棒性指标上达到所评估方法中的最先进水平。总体而言，O²DENet为数据驱动的酶动力学预测器提供了通用且有效的稳定性增强策略，适用于实际酶工程应用。
原文摘要 · Abstract (English)
Accurate prediction of enzyme kinetic parameters is essential for understanding catalytic mechanisms and guiding enzyme engineering.However, existing deep learning-based enzyme-substrate interaction (ESI) predictors often exhibit performance degradation on sequence-divergent, out-of-distribution (OOD) cases, limiting robustness under biologically relevant perturbations.We propose O$^2$DENet, a lightweight, plug-and-play module that enhances OOD generalization via biologically and chemically informed perturbation augmentation and invariant representation learning.O$^2$DENet introduces enzyme-substrate perturbations and enforces consistency between original and augmented enzyme-substrate-pair representations to encourage invariance to distributional shifts.When integrated with representative ESI models, O$^2$DENet consistently improves predictive performance for both $k_{cat}$ and $K_m$ across stringent sequence-identity-based OOD benchmarks, achieving state-of-the-art results among the evaluated methods in terms of accuracy and robustness metrics.Overall, O$^2$DENet provides a general and effective strategy to enhance the stability and deployability of data-driven enzyme kinetics predictors for real-world enzyme engineering applications.
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