Modeling enzyme temperature stability from sequence segment perspective
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908468499185664 |
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| author | Zhang, Ziqi Chen, Shiheng Yang, Runze Wei, Zhisheng Zhang, Wei Wang, Lei Liu, Zhanzhi Zhang, Fengshan Wu, Jing Pan, Xiaoyong Shen, Hongbin Cao, Longbing Deng, Zhaohong |
| author_facet | Zhang, Ziqi Chen, Shiheng Yang, Runze Wei, Zhisheng Zhang, Wei Wang, Lei Liu, Zhanzhi Zhang, Fengshan Wu, Jing Pan, Xiaoyong Shen, Hongbin Cao, Longbing Deng, Zhaohong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling enzyme temperature stability from sequence segment perspective Zhang, Ziqi Chen, Shiheng Yang, Runze Wei, Zhisheng Zhang, Wei Wang, Lei Liu, Zhanzhi Zhang, Fengshan Wu, Jing Pan, Xiaoyong Shen, Hongbin Cao, Longbing Deng, Zhaohong Machine Learning Artificial Intelligence Biomolecules Quantitative Methods 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. |
| title | Modeling enzyme temperature stability from sequence segment perspective |
| topic | Machine Learning Artificial Intelligence Biomolecules Quantitative Methods |
| url | https://arxiv.org/abs/2507.19755 |