{"ID":2890059,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19755","arxiv_id":"2507.19755","title":"Modeling enzyme temperature stability from sequence segment perspective","abstract":"Developing enzymes with desired thermal properties is crucial for a wide range of industrial and research applications, and determining temperature stability is an essential step in this process. Experimental determination of thermal parameters is labor-intensive, time-consuming, and costly. Moreover, existing computational approaches are often hindered by limited data availability and imbalanced distributions. To address these challenges, we introduce a curated temperature stability dataset designed for model development and benchmarking in enzyme thermal modeling. Leveraging this dataset, we present the \\textit{Segment Transformer}, a novel deep learning framework that enables efficient and accurate prediction of enzyme temperature stability. The model achieves state-of-the-art performance with an RMSE of 24.03, MAE of 18.09, and Pearson and Spearman correlations of 0.33, respectively. These results highlight the effectiveness of incorporating segment-level representations, grounded in the biological observation that different regions of a protein sequence contribute unequally to thermal behavior. As a proof of concept, we applied the Segment Transformer to guide the engineering of a cutinase enzyme. Experimental validation demonstrated a 1.64-fold improvement in relative activity following heat treatment, achieved through only 17 mutations and without compromising catalytic function.","short_abstract":"Developing enzymes with desired thermal properties is crucial for a wide range of industrial and research applications, and determining temperature stability is an essential step in this process. Experimental determination of thermal parameters is labor-intensive, time-consuming, and costly. Moreover, existing computat...","url_abs":"https://arxiv.org/abs/2507.19755","url_pdf":"https://arxiv.org/pdf/2507.19755v1","authors":"[\"Ziqi Zhang\",\"Shiheng Chen\",\"Runze Yang\",\"Zhisheng Wei\",\"Wei Zhang\",\"Lei Wang\",\"Zhanzhi Liu\",\"Fengshan Zhang\",\"Jing Wu\",\"Xiaoyong Pan\",\"Hongbin Shen\",\"Longbing Cao\",\"Zhaohong Deng\"]","published":"2025-07-26T03:01:58Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"q-bio.BM\",\"q-bio.QM\"]","methods":"[\"Transformer\"]","has_code":false}