ActivePPI: quantifying protein-protein interaction network activity with Markov random fields

Motivation 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.Results 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.Availability and implementation All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

Automated summary

In this study, a framework called ActivePPI is proposed to evaluate the activity of PPI networks in different cellular conditions. ActivePPI measures the consistency between network architecture and protein measurement data by estimating the probability density of protein mass spectrometry abundance and modeling PPIN using a Markov-random-field-based method. The likelihood significance of the match between PPIN structure and protein abundance data is quantified using a nonparametric permutation test to derive empirical P-values. Extensive numerical experiments demonstrate the superior performance of ActivePPI in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. In summary, ActivePPI is a versatile tool for evaluating PPI networks that uncovers the functional significance of protein interactions in crucial cellular biological processes and offers further insights into physiological phenomena.