Functional clustering of immunoglobulin superfamily proteins with protein-protein interaction information calibrated hidden Markov model sequence profiles

Abstract

Secreted and cell-surface-localized members of the immunoglobulin superfamily (IgSF) play central roles in regulating adaptive and innate immune responses and are prime targets for the development of protein-based therapeutics. An essential activity of the ectodomains of these proteins is the specific recognition of cognate ligands, which are often other members of the IgSF. In this work, we provide functional insight for this important class of proteins through the development of a clustering algorithm that groups together extracellular domains of the IgSF with similar binding preferences. Information from hidden Markov model-based sequence profiles and domain architecture is calibrated against manually curated protein interaction data to define functional families of IgSF proteins. The method is able to assign 82% of the 477 extracellular IgSF protein to a functional family, while the rest are either single proteins with unique function or proteins that could not be assigned with the current technology. The functional clustering of IgSF proteins generates hypotheses regarding the identification of new cognate receptor-ligand pairs and reduces the pool of possible interacting partners to a manageable level for experimental validation.

Keywords: GO; Gene Ontology; IgSF; MSA; functional prediction; immunoglobulin superfamily; multiple sequence alignment; protein–protein interaction.