Statistically Inferring Protein−Protein Associations with Affinity Isolation LC−MS/MS Assays

Affinity isolation of protein complexes followed by protein identification by LC−MS/MS is an increasingly
popular approach for mapping protein interactions. However, systematic and random assay errors
from multiple sources must be considered to confidently infer authentic protein−protein interactions.
To address this issue, we developed a general, robust statistical method for inferring authentic
interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test
(LRT) coupled with Bayes' Odds estimation. We then applied our LRT-Bayes' algorithm experimentally
using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in
conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins
from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes.
The algorithm can discriminate against a background of prey proteins that are detected in association
with a large number of baits as an artifact of the measurement. We conclude that the experimental
protocol including the LRT-Bayes' algorithm produces results with high confidence but moderate
sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling
assumptions, estimating parameters, and evaluating the significance of results in protein association
studies.
Keywords: protein−protein interaction • affinity isolation • LC−MS/MS • likelihood ratio test • Bayes' Odds