基于自我训练的半监督代和贝叶斯动态调的氧酶热稳定性预测
Sujuan Liu1, Mengyu Yu1, Lei Zhang1
1College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, P. R. China.
Journal of chemical information and modeling
|March 5, 2026
概括
一个新的框架,HyS-BST,提高了基酶热稳定性预测的准确性. 这种专门的模型显著改善了预测,降低了酶工程的实验成本.
科学领域:
- 生物化学和分子生物学
- 计算生物学和生物信息学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 现有的酶热稳定性预测模型往往缺乏基酶的特异性,限制了它们在向酶设计中的应用.
- 特定于基酶的模型对于提高蛋白质工程工作的准确性和效率至关重要.
- 开发精确的基酶热稳定性的预测工具对于减少实验成本和时间至关重要.
研究的目的:
- 开发一个专门的,自我训练的半监督框架 (HyS-BST) 来准确地预测基酶热稳定性.
- 为了改善对氧化酶的ΔΔG的变异性热稳定性的预测.
- 为基酶工程提供一个高效和具有成本效益的解决方案.
主要方法:
- 开发HyS-BST,一个专门的自我训练的半监督框架,适用于基酶.
- 整合自训练策略与贝叶斯动态调,以提高预测准确度.
- 使用有限的基酶数据集进行培训和验证.
主要成果:
- 在十次训练代后,HyS-BST实现了0.96的确定系数 (R2) 和0.98的皮尔森相关系数 (PCC).
- 该模型在测试组上显示了0.06的低根平均平方误差 (RMSE).
- 与跨家族模型相比,HyS-BST将PCC和RMSE改进了约70%,显示出优异的基酶特异性性能.
结论:
- HyS-BST框架为预测氧化酶热稳定性提供了专门和高度准确的解决方案.
- 这种方法显著减少了对酶变体的搜索空间,并节省了实验资源.
- HyS-BST代表了基酶工程和热稳定性设计的经济有效的进步.
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