预测和设计具有所需化温度的热稳定蛋白质
Purva Tijare1, Nishant Kumar1, Gajendra P S Raghava2
1Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Industrial Estate, Phase III (Near Govind Puri Metro Station), Office: A-302 (R&D Block), New Delhi, 110020, India.
Scientific reports
|May 14, 2025
概括
研究人员开发了一种新方法,使用机器学习和大型语言模型来预测蛋白质化温度 (Tm). 该工具有助于设计具有所需热稳定的蛋白质,并选耐热蛋白质.
科学领域:
- 蛋白质工程是指蛋白质工程.
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 在高温下蛋白质的稳定性对功能至关重要,其量度是化温度 (Tm).
- 现有的Tm预测方法由于对冗余数据的培训而受到影响,并且缺乏设计能力.
- 预测和设计具有特定Tm值的蛋白质对于各种生物技术应用至关重要.
研究的目的:
- 开发一种可靠的回归方法来预测蛋白质化温度 (Tm).
- 为了使蛋白质的设计具有目标的热稳定性.
- 创建一个用户友好的工具来选热稳定蛋白质.
主要方法:
- 利用17,312个蛋白质的非冗余数据集进行培训,测试和验证.
- 开发了使用标准蛋白序列特征 (例如,香农) 的机器学习模型.
- 精心调整的大型语言模型 (ProtBert,ProtGPT2,ProtT5) 来生成嵌入用于增强预测.
- 创建了一个整体模型,结合了序列特征和语言模型嵌入.
主要成果:
- 使用香农的机器学习模型实现了0.80的皮尔森相关性和0.63.2的R2.
- 使用ProtBert嵌入式的模型实现了0.89的最大相关性和0.80.2的R2.
- 合奏方法进一步提高了预测准确度.
- 证明了该工具在不同生物体中识别热稳定蛋白的实用性.
结论:
- 开发的回归方法显著提高了蛋白质Tm预测的准确性.
- 该工具有助于设计具有所需化温度的蛋白质.
- 一个Web服务器和Python包 (PPTstab) 可供公众使用,支持蛋白质工程和研究.
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