Gcn5-related N-acetyltransferases (GNATs) are considered a "megafamily" that ranks among the most structurally and sequentially diverse superfamilies in the CATH (Class, Architecture, Topology, Homology) database. In this vast superfamily, several types of protein functions have been explored throughout evolution, yet the evolutionary pathways that led to such diversity remain poorly understood. To investigate these concepts further, we selected a functionally distinct GNAT subgroup called polyamine N-acetyltransferases (PAATs). These enzymes acetylate polyamines that are crucial for cellular homeostasis. While PAATs from different domains of life catalyze the same reaction, their residue conservation patterns, oligomeric states, and presence of allosteric sites vary. Despite their biological importance, many putative PAATs remain uncharacterized, limiting our ability to infer evolutionary relationships, understand how functional properties emerged, and appreciate the extent of their structural diversity and substrate specificity. Here, we present a characterization of a large subset of PAAT enzymes, including their likely oligomeric states, functional site properties, and experimental functions.
J. Roca-Martínez, Hazel N. Leiva Martel, Jialin Yin et al.· Structure· 0 citations
De novo protein design is advancing rapidly1,2. This is being driven by AI to generate protein backbones, sequences, and structural models3–7. As a result, de novo designed proteins are becoming larger and more complex8–10, and increasingly explore new protein structures11,12. By contrast, natural proteins have evolved structural and functional complexity by modular combination of recurring protein domains13. Approximately 25% of these natural domains are mostly α-helical structures14. Here we show how these can be expanded using rational computational design. Following the domain classification scheme CATH15, we build complex all-α de novo proteins hierarchically using sequence-to-structure relationships for helix-helix interactions, systematic rules to connect helices, computational tools to design loops, and in silico evaluation. The pipeline starts with a target architecture of free-standing helices. These are connected into a topology by considering local arrangements of helical bundles using understood sequence-to-structure relationships for helix packing. Single-chain sequences are completed using template- and AI-based methods. Finally, AlphaFold models are assessed to give small numbers of designs for experimental validation. We test 31 designs for 14 different architectures and 25 topologies. 75% of these express as stable, monomeric, water-soluble proteins; and >30% yield X-ray crystal structures matching the designs to atomic accuracy and with new-to-nature structures. Finally, several of the scaffolds are functionalised through one-shot designs to deliver ion, small-molecule and protein binders.
K. I. Albanese, Joel J. Chubb, L. Gutierrez-Rus et al.· bioRxiv· 0 citations