Figure 1
Example illustrating the intuition behind our approach. In this simple example, there are five proteins (elongated rectangles) with four interactions between them (black lines); proteins contain occurrences of sequence motifs (colored small elements within the protein rectangles). Pairs of motifs on two proteins may bind to each other and hence mediate a protein-protein interaction if they have high affinity. The observed interactions are best explained via high affinity for the motif pair a,d, explaining the interactions P1-P3 and P1-P4, and high affinity for the pair b,e, explaining the interactions P1-P5 and P2-P5. We can now estimate the confidence in a prediction 'P i binds to P j at motif M' by (computationally) 'disabling' the ability of M to mediate this interaction. For example, the prediction that P1-P4 bind at motif d has high confidence, because d is the only motif that can explain the interaction. Conversely, the prediction that P1-P3 bind at motif d has lower confidence, because the motif pair a,c can provide an alternative explanation to the interaction. The prediction that P2-P5 bind at motif e also has high confidence: although interaction via binding at b,c would explain the interaction, making b,c a high-affinity motif pair would contradict the fact that P2 and P3 do not interact.