Belief propagation estimation of protein and domain interactions using the sum-product algorithm

Authors:
Faruck Morcos;Marcin Sikora;Mark S. Alber;Dale Kaiser;Jesús A. Izaguirre
Affiliations:
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Life Technologies, Foster City, CA and Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN;Department of Mathematics, University of Notre Dame, Notre Dame, IN;Departments of Biochemistry and Developmental Biology, Kaiser Laboratory, School of Medicine, Stanford University, Stanford, CA;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN
Venue:
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Year:
2010

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Abstract

In this paper, a novel framework is presented to estimate protein-protein interactions (PPIs) and domain-domain interactions (DDIs) based on a belief propagation estimation method that efficiently computes interaction probabilities. Experimental interactions, domain architecture, and gene ontology (GO) annotations are used to create a factor graph representation of the joint probability distribution of pairwise protein and domain interactions. Bound structures are used as a priori evidence of domain interactions. These structures come from experiments documented in iPfam. The probability distribution contained in the factor graph is then efficiently marginalized with a message passing algorithm called the sum-product algorithm (SPA). This method is compared against two other approaches: maximum-likelihood estimation (MLE) and maximum specificity set cover (MSSC). SPA performs better for simulated scenarios and for inferring high-quality PPI data of Saccharomyces cerevisiae. This framework can be used to predict potential protein and domain interactions at a genome wide scale and for any organism with identified protein-domain architectures.