一个深度检索增强的超级学习框架,用于酶最佳pH预测.
Liang Zhang1,2, Kuan Luo1, Ziyi Zhou3
1Shanghai-Chongqing Institute of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200240, China.
Journal of chemical information and modeling
|March 24, 2025
概括
预测酶的最佳pH值 (pHopt) 对设计至关重要. 使用kNN和少数射击学习的新计算模型Venus-DREAM准确地预测了酶pHopt,减少了昂贵的实验室实验.
科学领域:
- 生物化学和生物信息学
- 计算生物学 计算生物学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 酶功能对pH高度敏感,使得最佳pH (pHopt) 预测对酶设计和应用至关重要.
- 对许多新发现或设计的酶进行pHopt的实验性确定是耗时且昂贵的.
- 式方法,特别是机器学习,为预测酶pHopt.提供了具有成本效益的替代方案.
研究的目的:
- 开发和验证Venus-DREAM,一种用于预测酶最佳pH值 (pHopt) 的新型计算模型.
- 通过蛋白质语言模型利用少数射击学习和k-最近邻居 (kNN) 来进行准确的pHopt预测.
- 为了使酶的高通量虚拟选能够达到所需的pH档案.
主要方法:
- 维纳斯-DREAM使用k-最近邻居 (kNN) 回归算法来确定最佳邻居数量 (k).
- 酶相似性是使用从蛋白质语言模型 (PLM) 获得的嵌入式的共因相似性来量化.
- 基于爬行动物算法的一些射击学习方法,将模型调整为k-最近的酶,用于专门的pHopt预测.
主要成果:
- 在预测酶最佳pH (pHopt) 时,Venus-DREAM实现了最先进的准确性.
- 该模型有效地将phopt预测视为几次学习任务,从有限的标记数据中学习.
- 该方法促进了对具有特定pH的酶的蛋白质序列空间的高效,高通量虚拟探索.
结论:
- 维纳斯-DREAM提供了一个高度准确和高效的计算工具,用于预测酶最佳pH值 (pHopt).
- 这种方法显著减少了对酶工程中广泛实验验证的需求.
- 该方法可适应预测其他酶功能,扩大其在蛋白质科学中的适用性.
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