Aggregating Homologous Protein Families in Evolutionary Reconstructions of Herpesviruses

Protein families can be used to reconstruct evolutionary histories of organisms. The accuracy of protein assignment to such families is critical for the success of such studies. Here we investigate the automatic aggregation of motif-defined homologous protein families for further reconstruction of their evolutionary histories. We propose a method that utilises only parameters that can be adjusted by using the data. The building blocks of the method include: (a) a majority rule for combining protein homologous neighbourhood lists into that for a family, and (b) a robust clustering procedure whose only parameter, the similarity shift, can be estimated from information on proteins with known function. The method is applied to a herpesvirus protein dataset leading to insights into the composition of ancestors of herpesvirus superfamilies. Comparison of the computational reconstructions with more comprehensive analyses also show how alignment-based between-protein similarity scoring can be improved by using data on gene arrangements