Identifying coevolving partners from paralogous gene families.

Many methods have been developed to detect coevolution from aligned sequences. However, all the existing methods require a one-to-one mapping of candidate coevolving partners (nucleotides, amino acids) a priori. When two families of sequences have distinct duplication and loss histories, finding the one-to-one mapping of coevolving partners can be computationally involved. We propose an algorithm to identify the coevolving partners from two families of sequences with distinct phylogenetic trees. The algorithm maps each gene tree to a reference species tree, and builds a joint state of sequence composition and assignments of coevolving partners for each species tree node. By applying dynamic programming on the joint states, the optimal assignments can be identified. Time complexity is quadratic to the size of the species tree, and space complexity is exponential to the maximum number of gene tree nodes mapped to the same species tree node. Analysis on both simulated data and Pfam protein domain sequences demonstrates that the paralog coevolution algorithm picks up the coevolving partners with 60% 88% accuracy. This algorithm extends phylogeny-based coevolutionary models and make them applicable to a wide range of problems such as predicting protein-protein, protein-DNA and DNA-RNA interactions of two distinct families of sequences.