Mining folded proteomes in the era of accurate structure prediction

Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques and a wealth of experimentally determined structures, previously curated sequence databases have become a rich source of biological information. Here, we use bicinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins.

Automated summary

Protein structure plays a fundamental role in the function and processes of biological systems. Fold recognition algorithms provide a powerful tool to identify structural and functional similarities between distantly related homologs. With advances in machine learning techniques and a wealth of experimentally determined structures, previously curated sequence databases have become an important source of biological information. In this study, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database and identify novel protein family members, infer function, and group predicted protein structures. We identify novel, previously unknown members of various pore-forming protein families as an example of the utility of this approach.