Predicting direct protein interactions from affinity purification mass spectrometry data

Adrian R Vetta,Mathieu Blanchette,Ethan Dh Kim,Ashish Sabharwal

doi:10.1186/1748-7188-5-34

Adrian R Vetta, Mathieu Blanchette + Show 2 more

Open Access

https://doi.org/10.1186/1748-7188-5-34

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Abstract

BackgroundAffinity 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.ResultsWe 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.ConclusionsAs 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.

Highlights

Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo
Mathematical modelling of AP-MS data We first describe a simple model of the AP-MS PPI data that shall be used throughout this paper
We model the physical process through which PPIs are identified in an AP-MS experiment as follows

Summary

Introduction

Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. Inferring the direct, physical PPI network from highthroughput data remains a challenge in systems biology. The leading technologies for identifying PPIs are Yeast 2-Hybrid (Y2H) [1,2] and Affinity Purification followed confidence level of an interaction by considering alternative paths between two protein molecules. In this context, authors say that a true interaction between bait b and prey p is a true positive if, at some point in the set of cells considered, there exists a complex that contains both b and p. We note that as the sensitivity of the APMS methods improves and the stability of the complexes that can be detected decreases, the transience of detectable interactions will increase, to a point where, eventually, every protein may be shown to marginally interact with every other protein

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