Transition Bio Ltd, Cambridge, UK.
Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Nat Commun. 2024 Jul 10;15(1):5418. doi: 10.1038/s41467-024-48496-7.
Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.
生物分子凝聚物帮助细胞在空间和时间上组织其内容。细胞中存在着多种具有不同成分的凝聚物类型，其中许多可能尚未被发现。在这里，我们开发了一种预测生物分子凝聚物组成的方法。我们首先分析了现有的细胞凝聚物蛋白质组学数据，发现决定蛋白质定位到凝聚物的生物物理特征与已知的同型相分离过程的驱动因素不同，带电荷的蛋白质-RNA 相互作用和疏水性介导的蛋白质-蛋白质相互作用在前者中起着关键作用。然后，我们开发了一种机器学习模型，将蛋白质序列与其定位到异源凝聚物的倾向联系起来。我们将该模型应用于整个蛋白质组，发现许多排名靠前的目标都位于原始训练数据之外，这可以通过正交免疫组织化学染色成像来证实。最后，我们根据与生物分子相互作用谱的重叠，将易于凝聚的蛋白质组分割成凝聚物类型，从而生成蛋白质凝聚物图谱。图谱中的几个凝聚物簇与实验表征的凝聚物或其中的区域的组成非常匹配，这表明该图谱可用于识别已知凝聚物系统中的其他成分，并发现以前未被表征的凝聚物。
