从序列中预测无序蛋白质的相分离倾向
Sören von Bülow1, Giulio Tesei1, Fatima Kamal Zaidi2
1Department of Biology, Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen 2200, Denmark.
概括
我们开发了一个机器学习模型来预测从蛋白质序列中的生物分子相分离. 该工具准确地预测哪些内在无序区域 (IDR) 将逐步分离,有助于细胞过程研究.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 像转录和免疫信号这样的细胞过程依赖于选择性的生物分子丰富.
- 这种丰富通常由宏分子的相分离,特别是内在无序的蛋白质和区域 (IDRs) 介导.
- 从序列中预测IDR相态行为的热力学仍然是一个重大挑战.
研究的目的:
- 开发一种快速准确的机器学习模型,直接从氨基酸序列中预测IDR相分离的自由能量和和度.
- 精细化和量化序列特征和相位分离倾向之间的关系.
主要方法:
- 结合粗粒度分子动力学模拟与主动学习.
- 开发了一种机器学习模型,可以从序列中预测相位分离热力学.
- 使用计算,先前测量的实验数据和新的实验数据验证了模型.
主要成果:
- 该模型准确地预测了IDR相分离的自由能量和和度.
- 应用于人类蛋白质组,预计5%的内在无序区域 (共27,663个中1420个) 将经历同型相分离.
- 确定从电荷转向疏水性介导相互作用的转变可以改变分子内和分子间相互作用之间的平衡,影响相位分离.
- 证明了模型在力场改进中的实用性.
结论:
- 开发的机器学习模型为内在无序的区域提供了相分离行为的准确预测.
- 这项工作完善了我们对基于阶段分离的序列决定因素的理解.
- 预测模型是解释细胞实验和设计具有特定相分离特性的IDR的宝贵工具.
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