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微生物最佳生长温度数据库的人工智能驱动的构建及其对酶最佳温度预测的影响

Xiaotao Wang^1,2, Yuwei Zong², Xuanjie Zhou²

¹State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing (SCICB), East China University of Science and Technology, Shanghai 200237, China.

The journal of physical chemistry. B

|March 4, 2024

概括

使用人工智能开发高质量的最佳生长温度 (OGT) 数据库可以提高酶最佳温度 (Topt) 的预测. 这些增强的OGT数据对于发现适合极端生物处理条件的酶至关重要.

科学领域:

生物技术和生物化学
生物信息学和计算生物学

背景情况:

准确预测酶最佳温度 (Topt) 对生物处理至关重要,特别是在极端条件下.
微生物的最佳生长温度 (OGT) 是Topt酶的一个关键指标,反映了进化适应.
现有的OGT数据库受限于对培养温度的依赖,这些温度不能准确地表示真正的OGT,从而导致糟糕的Topt预测模型.

研究的目的:

开发基于人工智能的模型,从科学文献中提取准确的OGT信息.
构建一个全面和高质量的OGT数据库.
评估新的OGT数据库对酶Topt预测准确性的影响.

主要方法:

开发了包括BioLinkBERT在内的AI模型,以从科学文献中提取OGT数据.
建立了一个全面的OGT数据库,包含1155个独特的生物和2142个OGT值.
人工智能模型的性能使用EM和F1评分来评估OGT提取的性能.

主要成果:

在OGT提取中,BioLinkBERT模型取得了高性能,EM得分为91.00,F1得分为91.91.
新建的OGT数据库显著改善了酶Topt预测,实现了0.698.8的R2.
这一表现超过了使用传统培养温度数据 (R2为0.686) 的预测.

结论:

从文献中提取OGT的AI驱动提取对于构建高质量的生物数据库是有效的.
与现有方法相比,开发的OGT数据库为预测酶Topt提供了更高的准确性.
这种资源对于识别具有先进生物处理应用所需的催化温度的酶至关重要.

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