Background: Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest.
Results: We first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm.Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset.
Conclusions: As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.