Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein−protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

大多数细胞过程受相互作用的蛋白质组调节以形成蛋白质复合物。蛋白质组成在不同组织或疾病状况之间有所不同，从而能够或阻止某些蛋白质与蛋白质的相互作用，并导致复合体的变化。特定于情境的蛋白质复合物的定量和定性表征将有助于更好地理解细胞生理行为中的情境相关变异。在这里，我们介绍SiComPre 1.0，这是一种通过集成多组学信​​息源来预测特定于上下文的蛋白质复合物的计算工具。SiComPre在定性预测方面优于其他蛋白质复合物预测工具，并且在根据用户定义的特定相互作用和蛋白质丰度对复合物组进行定量预测方面具有独特性。