Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF) images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies.
生物相分离是一种重要的分子机制，可以调控蛋白质在细胞中的区室化分布。能在细胞中发生相分离的蛋白质通常具有特定的序列特征，如含有内部无序区（intrinsically disordered regions，IDRs）或多模块化结构域，因此，序列分析工具可被用于筛选这些蛋白质。然而，现有的相分离预测工具通常是基于IDR蛋白质的序列特征设计的，因此不可避免地会存在偏倚，忽略IDR含量相对较低的相分离蛋白。与此同时，除序列的氨基酸排列外，蛋白质的其他特征也可以为鉴定潜在的相分离蛋白提供关键信息：蛋白-蛋白相互作用（protein–protein interaction，PPI）网络可以提供蛋白间多价相互作用的信息，而多价相互作用与相分离关系密切；翻译后修饰（post-translational modifications，PTMs）也对于调节相分离过程至关重要。此外，蛋白发生相分离所形成的液滴，会在免疫荧光（immunofluorescence，IF）图像上显示为球形荧光结构，我们也可以基于此特征，将这些蛋白与非相分离蛋白区分开来。在本篇综述中，我们总结了用于预测相分离蛋白的序列分析工具，并着重探讨了在未来的研究中将PPI，PTM和IF图像特征纳入相分离预测的意义。





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