Protein Interaction Inference as a MAX-SAT Problem

Discovering interacting proteins is essential for understanding protein functions. However, high throughput interaction data are inherently noisy and only cover a small portion of the whole interactome. Domains, the building block of proteins, are believed to be responsible for the interactions among proteins. An abstract representation of interactome is achieved at domain level and this representation also facilitates the discovery of unobserved proteinprotein interactions. Many domain-based approaches have been proposed to predict protein-protein interactions and promising results have been obtained. Existing methods generally assume that domain interactions are independent of each other for the convenience of computational modeling. In this paper, a new framework of learning is proposed. The framework makes no assumption about domain interactions and consider protein interactions resulting from multiple domain interactions which may be dependent of each other. With a conjunctive normal form representation of the relationship between protein interactions and domain interactions, the problem of interaction inference is modeled as a constraint satis?ability problem and solved via linear programming. Experimental results on a combined yeast data set have demonstrated the robustness of and the accuracy of the proposed algorithm.