We describe a novel probabilistic approach to estimating errors in two-hybrid (2H)
experiments. Such experiments are frequently used to elucidate protein-protein
interaction networks in a high-throughput fashion; however, a significant challenge
with these is their relatively high error rate, specifically, a high false-positive rate.
We describe a comprehensive error model for 2H data, accounting for both random
and systematic errors. The latter arise from limitations of the 2H experimental
protocol: in theory, the reporting mechanism of a 2H experiment should be acti-
vated if and only if the two proteins being tested truly interact; in practice, even in
the absence of a true interaction, it may be activated by some proteins – either by
themselves or through promiscuous interaction with other proteins. We describe
a probabilistic relational model that explicitly models the above phenomenon and
use Markov Chain Monte Carlo (MCMC) algorithms to compute both the proba-
bility of an observed 2H interaction being true as well as the probability of indi-
vidual proteins being self-activating/promiscuous. This is the first approach that
explicitly models systematic errors in protein-protein interaction data; in contrast,
previous work on this topic has modeled errors as being independent and random.
By explicitly modeling the sources of noise in 2H systems, we find that we are
better able to make use of the available experimental data. In comparison with
Bader et al.’s method for estimating confidence in 2H predicted interactions, the
proposed method performed 5-10% better overall, and in particular regimes im-
proved prediction accuracy by as much as 76%.