评估蛋白质酸化,AlphaFold3模型和形状变异性之间的关系
Pathmanaban Ramasamy1,2,3,4, Jasper Zuallaert1,2, Lennart Martens1,2,5
1CompOmics, VIB Center for Medical Biotechnology, VIB, Ghent, Belgium.
Protein science : a publication of the Protein Society
|December 23, 2025
概括
像AlphaFold这样的深度学习模型很难预测酸化是如何改变蛋白质结构的关键蛋白质修饰. 目前的模型捕捉了主导状态,但错过了关键的酸化诱导的变化,限制了我们对蛋白质功能和疾病的理解.
科学领域:
- * 分子生物学 * 分子生物学
- * 结构生物学 * 结构生物学
- * 计算生物学 * 计算生物学
背景情况:
- *像酸化这样的翻译后修饰 (PTM) 调节蛋白质功能,并与疾病有关.
- * 了解PTM驱动的结构变化至关重要,但由于实验数据稀缺和蛋白质动力学受到限制.
- *深度学习,以AlphaFold为例,已经推进了蛋白质结构预测.
研究的目的:
- *评估AlphaFold模型预测酸化诱导蛋白质结构多样性的能力.
- * 评估酸化感知模型是否捕捉到PTM特有的形状变化.
- * 确定模拟PTM驱动的蛋白质结构景观的挑战.
主要方法:
- *对AlphaFold 2 (AF2),AlphaFold 3非 (AF3-非) 和AlphaFold 3 (AF3-) 模型进行系统评估.
- * 实验性衍生蛋白质构造组合的分析.
- * 模型预测与主导和PTM特定结构状态的比较.
主要成果:
- *所有评估的AlphaFold模型主要预测了主导蛋白质结构状态.
- * 模型未能捕捉显著的酸化诱导的形状变化.
- *AF3-在预测这些变化方面只比AF2和AF3-非略有改善.
结论:
- *目前的AlphaFold模型在预测PTM驱动的结构动力学,特别是酸化方面存在局限性.
- *捕捉修饰诱导的构造变异性仍然是一个重大挑战.
- *需要开发更具适应性的蛋白质结构预测框架.
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