Title: Enzyme-Unified: Learning Holistic Representations of Enzyme Function with a Hybrid Interaction Model
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: AI for science, Enzyme function prediction, biotechnology, cross attention

Abstract: Predicting diverse functional properties of enzymes is a crucial challenge in biotechnology. Current machine learning approaches often fall short due to two key limitations: they predict properties in isolation using simplistic feature concatenation, thereby missing crucial inter-property relationships, and their performance is frequently overestimated on biased, homology-unaware datasets.  To overcome these challenges, we introduce Enzyme-Unified, a unified framework built upon a single, powerful architecture. We train distinct instances of our model to predict a comprehensive suite of five key properties: turnover number, Michaelis constant, catalytic efficiency, optimal temperature, and optimal pH. At its core is our novel Hybrid Interaction Model, which dynamically integrates fine-grained local interactions via cross-attention with global feature representations through a trainable gate, enabling a more holistic representation of enzyme function. For robust evaluation, we developed three new large-scale, sequence-dissimilar datasets. Our experiments show that Enzyme-Unified achieves state-of-the-art results and significantly outperforms previous models on our rigorously curated datasets, demonstrating the powerful synergy of its hybrid architecture. The code and dataset will be available upon acceptance.