Pseudodata-guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction

TL;DR

This paper introduces O$^2$DENet, a novel module that improves the robustness and accuracy of enzyme kinetic parameter predictions on out-of-distribution data by using biologically informed perturbations and invariant representation learning.

Contribution

The paper presents O$^2$DENet, a plug-and-play module that enhances OOD generalization in enzyme-substrate interaction models through perturbation augmentation and invariant learning.

Findings

O$^2$DENet improves prediction accuracy on OOD benchmarks.

Achieves state-of-the-art robustness in enzyme kinetics prediction.

Enhances model stability for enzyme engineering applications.

Abstract

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.