Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.
School of Mathematics, Shandong University, Jinan, Shandong 250100, China.
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad567.
Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning.
To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena.
All source code and data are freely available at https://github.com/zpliulab/ActivePPI.
蛋白质-蛋白质相互作用（PPI）是使细胞发挥功能的生物分子网络的关键组成部分。生物实验已经鉴定出大量的 PPI，这些相互作用被存储在知识库中。然而，这些相互作用通常局限于特定的细胞环境和条件。网络活性可以被描述为 PPI 网络（PPIN）与通过蛋白质质谱测量的特定细胞环境之间的一致性程度，也可以被量化为统计显著性得分。如果不知道这些 PPI 在细胞环境或特定表型中的活性，就不可能揭示这些 PPI 的功能以及它们如何影响细胞功能。
为了计算不同细胞条件下 PPIN 的活性，我们提出了一个名为 ActivePPI 的 PPIN 活性评估框架，用于测量网络结构和蛋白质测量数据之间的一致性。ActivePPI 通过基于马尔可夫随机场的方法来估计蛋白质质谱丰度的概率密度，并对 PPIN 进行建模。此外，基于非参数置换检验推导出经验 P 值，以量化 PPIN 结构与蛋白质丰度数据之间匹配的似然显著性。广泛的数值实验证明了 ActivePPI 的优越性能，并实现了网络活性评估、途径活性评估和最佳网络架构调整任务。简而言之，ActivePPI 是一种用于评估 PPI 网络的多功能工具，可以揭示蛋白质相互作用在关键细胞生物学过程中的功能意义，并为生理现象提供更深入的见解。
所有的源代码和数据都可以在 https://github.com/zpliulab/ActivePPI 上免费获取。
