An improved hypergeometric probability method for identification of functionally linked proteins using phylogenetic profiles

Predicting functions of proteins and alternatively spliced isoforms encoded in a genome is one of the important applications ofbioinformatics in the post-genome era. Due to the practical limitation of experimental characterization of all proteins encoded in agenome using biochemical studies, bioinformatics methods provide powerful tools for function annotation and prediction. Thesemethods also help minimize the growing sequence-to-function gap. Phylogenetic profiling is a bioinformatics approach to identifythe influence of a trait across species and can be employed to infer the evolutionary history of proteins encoded in genomes. Herewe propose an improved phylogenetic profile-based method which considers the co-evolution of the reference genome to derivethe basic similarity measure, the background phylogeny of target genomes for profile generation and assigning weights to targetgenomes. The ordering of genomes and the runs of consecutive matches between the proteins were used to define phylogeneticrelationships in the approach. We used Escherichia coli K12 genome as the reference genome and its 4195 proteins were used in thecurrent analysis. We compared our approach with two existing methods and our initial results show that the predictions haveoutperformed two of the existing approaches. In addition, we have validated our method using a targeted protein-proteininteraction network derived from protein-protein interaction database STRING. Our preliminary results indicates thatimprovement in function prediction can be attained by using coevolution-based similarity measures and the runs on to the samescale instead of computing them in different scales. Our method can be applied at the whole-genome level for annotatinghypothetical proteins from prokaryotic genomes.