Yuting Xiao

Multidomain proteins are mosaics of domains, protein modules that are associated with a specific structure or function and are found in diverse combinations. This modular organization facilitates the evolution of novel protein functions, but the principles that govern the relationship between the domain content of a protein and its function is poorly understood. In particular, do domains always contribute the same function, or does the functional contribution of a domain depend on the neighboring domains in the protein? To answer this question, we used vector embeddings, which account for local contextual signals, to model the protein domain content of multidomain proteins. We observe that multidomain architectures that are semantically similar share more functional attributes than multidomain architectures selected based on domain content similarity, alone, suggesting that context is important for understanding the relationship between domain content and protein function. Surprisingly, vector semantics also identified multidomain architecture pairs with significantly high functional similarity, despite having no domains in common at all, suggesting that vector semantics may be discovering domain “synonyms”. Taken together, our results underscore the importance of contextual models for understanding the interplay between domain architecture evolution and functional innovation in multidomain proteins.

Reconstruction of the ancestral protein repertoire offers valuable insights into the tempo and mode of protein content evolution, but can be highly sensitive to model choice. We used a phylogenetic Birth-Death-Gain model to investigate the evolution of the metazoan protein domain repertoire. Domains, protein modules with a distinct structure and function, represent the basic components of protein repertoire. Given a species tree and a census of protein domain families in present-day species, we estimated the most likely rates of domain family origination, duplication, and loss. Rates were allowed to vary across species lineages and domain families, decoupling these factors. Statistical hierarchical clustering of family-specific rates reveals groups of domains evolving in concert. Moreover, we observe a strong and significant association between family rate and family function. Interestingly, families with functions associated with metazoan innovations tend to have the fastest rates. We further inferred the expected ancestral domain content and the history of domain family gains, losses, expansions, and contractions in each species lineage. Our analysis reveals an ongoing process of domain family replacement and resizing, consistent with extensive remodeling of the protein domain repertoire. This stands in contrast to recent reports of widespread loss during metazoan evolution, which were obtained with more constrained models. The use of a powerful, probabilistic Birth-Death-Gain model reveals an unexpected level of genomic plasticity.

Multidomain proteins are mosaics of domains , protein modules that are associated with a specific structure or function and are found in diverse combinations. This modular organization facilitates the evolution of novel protein functions, but the principles that govern the relationship between the domain content of a protein and its function is poorly understood. In particular, do domains always contribute the same function, or does the functional contribution of a domain depend on the neighboring domains in the protein? To answer this question, we used vector embeddings, which account for local contextual signals, to model the protein domain content of multidomain proteins. We observe that multidomain architectures that are semantically similar share more functional attributes than multidomain architectures selected based on domain content similarity, alone, suggesting that context is important for understanding the relationship between domain content and protein function. Surprisingly, vector semantics also identified multidomain architecture pairs with significantly high functional similarity, despite having no domains in common at all, suggesting that vector semantics may be discovering domain “synonyms”. Taken together, our results underscore the importance of contextual models for understanding the interplay between domain architecture evolution and functional innovation in multidomain proteins.

Domains, sequence fragments that encode protein modules with a distinct structure and function, are the basic building blocks of proteins. The set of domains encoded in the genome serves as the functional toolkit of the species. Here, we use a phylogenetic Birth-Death-Gain model to investigate the evolution of this protein toolkit in metazoa. Given a species tree and the set of protein domain families in each present-day species, this approach estimates the most likely rates of domain origination, duplication and loss. Statistical hierarchical clustering of domain family rates reveals sets of domains with similar rate profiles, consistent with groups of domains evolving in concert. Moreover, we find that domains with similar functions tend to have similar rate profiles. Interestingly, domains with functions associated with metazoan innovations, including immune response, cell adhesion, tissue repair, and signal transduction, tend to have the fastest rates. We further infer the expected ancestral domain content and the history of domain family gains, losses, expansions, and contractions on each branch of the species tree. In contrast to recent reports of widespread loss during metazoan evolution, we observe little evidence of genome streamlining. Rather, our analysis reveals an ongoing process of domain family replacement and resizing, consistent with extensive remodeling of the protein domain repertoire. The use of a powerful, probabilistic Birth-Death-Gain model reveals an unexpected level of genomic plasticity and a striking harmony between the evolution of domain usage in metazoan proteins and organismal innovation.