从序列段的角度建模酶温度稳定性
Ziqi Zhang1, Shiheng Chen2, Runze Yang3,4
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Journal of chemical information and modeling
|October 1, 2025
概括
我们开发了一个新的深度学习模型,分段变压器,以预测酶温度稳定性,克服数据限制. 该模型准确地预测了热性质,有助于工业应用的酶工程.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 酶的热稳定性对于工业和研究应用至关重要.
- 确定酶热性质的实验方法是资源密集的.
- 由于数据有限且不平衡,现有的计算模型面临着挑战.
研究的目的:
- 为 benchmarking 酶热建模创建一个精选的数据集.
- 开发一种高效准确的深度学习框架,用于预测酶温度稳定性.
- 为了证明该模型在指导酶工程工作中的实用性.
主要方法:
- 策划了一套用于酶温度稳定的新型数据集.
- 开发了分段变压器,这是一种使用分段级蛋白质序列表示的深度学习模型.
- 使用RMSE,MAE,Pearson和Spearman相关性评估模型性能.
主要成果:
- 分段变压器在预测酶温度稳定性方面取得了最先进的性能.
- 该模型证明了细分级特征在热行为预测中的重要性.
- 通过使用17种突变,设计了一种带有1.64倍热处理活性改善的丁酶.
结论:
- 分段变压器提供了一种高效和准确的方法来预测酶的热稳定性.
- 开发的数据集和模型促进了酶热建模和工程方面的进步.
- 该研究验证了该模型能够指导蛋白质工程以提高热稳定性的能力.
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