通过机器学习预测金属蛋白还氧化潜力:关注铁硫系统
Francesca Persico1, Bruno G Galuzzi2,3, Miriana Pellegrino4
1Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza Dell'Ateneo Nuovo 1, Milano 20126, Italy.
Journal of chemical information and modeling
|October 30, 2025
概括
我们开发了FeS-RedPred,这是一种机器学习模型,用于预测铁硫 (Fe-S) 蛋白质的降解潜力. 这个工具准确地预测了氧化还原行为,有助于蛋白质的设计和理解生物能量学.
科学领域:
- 生物化学和生物物理学
- 计算生物学 计算生物学
- 生物能源学 生物能源学
背景情况:
- 铁硫 (Fe-S) 蛋白对于许多生物过程至关重要,包括能量转化和DNA修复.
- 它们的功能依赖于由金属辅助因子确定的微调降解潜力 (RP),但从结构中预测RP是具有挑战性的.
- 这种困难阻碍了针对蛋白质设计的RP系统调制.
研究的目的:
- 引入FeS-RedPred,一个机器学习 (ML) 框架,用于准确和可扩展的预测Fe-S蛋白中的RP.
- 提供一个工具,帮助理解RP的决定因素,并指导蛋白质工程的努力.
- 为了使各种金属蛋白家族的氧化还原潜力的高通量预测.
主要方法:
- 开发了一个机器学习 (ML) 框架,FeS-RedPred,使用极端梯度增强 (XGB) 模型.
- 在多个空间尺度 (从本地到全球) 上计算的结构衍生分子描述器.
- 专注于单核和双核Fe-S集群 (例如,rubredoxins, [2Fe-2S] ferredoxins),并提供可用的数据.
主要成果:
- 在RP预测中达到约40mV的平均绝对误差,与最先进的方法相竞争.
- 证明了预测准确性和计算成本之间的高效平衡.
- 该模型为RP的关键决定因素提供了洞察力,促进了解释.
结论:
- FeS-RedPred为了解金属蛋白的氧化还原行为提供了有价值的基础.
- 能够对氧化还原潜力的高通量预测,为数据驱动的蛋白质设计提供信息.
- 通过提高我们设计Fe-S蛋白质的能力,推进生物能学和人类健康领域.
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