Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data—such as protein abundances, domain-domain interactions and functional annotations—to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.

尽管蛋白质组学领域已取得诸多进展，但绝大多数蛋白质复合物仍未被探明。探明这些复合物将助力我们解析细胞调控机制，并为新药研发提供支撑。因此，获取蛋白质复合物的组成与丰度的详细信息至关重要，但现有算法仅能提供定性预测。为此，我们提出一种基于蛋白质复合物形成随机模拟的新方法，该方法整合了多源数据——包括蛋白质丰度、结构域-结构域相互作用以及功能注释——以预测蛋白质复合物的多种存在形式及其丰度。该方法名为SiComPre（基于模拟的复合物预测，Simulation based Complex Prediction），相较于现有方法，其对酵母和人类蛋白质复合物的定性预测效果更优，且是首个可预测蛋白质复合物丰度的方法。此外，我们以硼替佐米（bortezomib）为例，证实SiComPre可用于预测药物处理后复合物组的变化。SiComPre是首个可同时实现分子复合物丰度定量预测与最优定性预测的方法。随着组织特异性蛋白质复合物相关新数据的陆续公布，SiComPre将能够预测不同组织类型及不同条件下复合物组的定性与定量差异。