Abstract

Multidomain proteins are characterized by a mosaic of sequence fragments that encode structural or functional modules, called domains. Multidomain families evolve via domain shuffling: insertion, duplication, and deletion of domains, as well as fusion and fission of adjacent coding sequences. Multidomain families play a central role in cell signaling and adhesion. Preferential expansions in the size and complexity of multidomain families are associated with key evolutionary transitions, including the emergence of the Metazoa and the evolution of vertebrates. As a consequence of their functional repertoire, multidomain families are of central importance in immune response, tissue repair, neural development, and cancer. The modularity of multidomain families is linked to their functional roles in cell signaling and adhesion and was a driving force in the evolution of multicellularity in animals. It has been proposed that modularity confers functional plasticity: changes in modular structure, through acquisition, loss, or replacement of a domain, potentially result in rapid rewiring of cellular networks.

Understanding the connection between protein evolution and network evolution requires methods to reconstruct multidomain evolution. Here, I present algorithms to infer the history of domain duplications, losses, and insertions in a multidomain family, as well as the timing of these events and the ancestral domain architectures. Key features of my approach include explicit representation of domain shuffling events and a model that captures sequence variation within domain families. I demonstrate the utility of my methods with in-depth analyses of well-studied multidomain families, as well as a genome-scale analysis of domain families in human. My results suggest that a remarkably greater amount of domain shuffling may have occurred than predicted by simpler models that do not consider sequence variation. Further, they underscore the importance of accurate evolutionary reconstruction for homology-based function prediction and evolutionary systems biology.

• http://www.cmu.edu/bio/faculty/durand.html
• http://www-2.cs.cmu.edu/~durand/Lab/