PatchET: Learning Enzyme Temperature Properties Through Patch-Based Neural Architectures

Abstract

Understanding enzyme thermal properties is essential for biotechnology and protein engineering, yet experimental measurements of attributes such as temperature optimum, stability, and range remain labor-intensive and costly. Prior studies have shown that specific regions within enzyme sequences disproportionately influence thermal behavior—an aspect often overlooked by existing deep learning models. In this work, we introduce PatchET, a biologically inspired deep learning model that predicts enzyme thermal properties directly from amino acid sequences. PatchET employs a dual-stage, patch-based architecture that captures both intra-patch local features and inter-patch global dependencies, reflecting the hierarchical nature of protein thermal adaptation. Alongside the model, we curate a comprehensive benchmark, including a refined dataset for temperature optimum and the first publicly available dataset for temperature range prediction. PatchET achieves state-of-the-art performance across three key tasks—temperature optimum, stability, and range—and serves as the first dedicated model for temperature range prediction. Extensive ablation studies further validate the effectiveness of our architectural design. Together, PatchET and the accompanying benchmark provide a unified and generalizable framework for modeling enzyme thermal properties, offering new tools for the rational design of thermostable enzymes.