Biology subjects

Axel, G.

Publications and source records attributed to Axel, G..

2 recordsLinked to original sources

bioRxiv2026

N-Zinclopedia: A database of predicted transition metal-binding sites and metalloenzymes

Metal ions are among the most common enzymatic cofactors, yet metal utilization remains poorly mapped. Here, we present N-Zinclopedia, an atlas of predicted transition metal-binding sites and candidate metalloenzymes across protein fold space. N-Zinclopedia catalogs protein sites compatible with transition metal coordination, identified using ZincSight screening of protein structure model databases. We focused on previously uncharacterized domains and proteins, screening for transition metal-binding sites within over a million of these, spanning both poorly annotated and canonical folds. We also classified the predicted sites, using MAHOMES-II, as candidate catalytic or non-catalytic metal-binding sites. Experimental characterization of a previously unknown zinc binding site confirms that ZincSight can determine previously un-annotated zinc binding. N-Zinclopedia significantly expands the known landscape of metal utilization, providing numerous predictions that await experimental examination. N-Zinclopedia is available at: https://bentallab.github.io/nzinclopedia.

bioinformatics↗

bioRxiv2025

Contrastive learning unites sequence and structure in a global representation of protein space

Amino acid sequence dictates the three-dimensional structure and biological function of proteins. Yet, despite decades of research, our understanding of the interplay between sequence and structure is incomplete. To meet this challenge, we introduce Contrastive Learning Sequence-Structure (CLSS), an AI-based contrastive learning model trained to co-embed sequence and structure information in a self-supervised manner. We trained CLSS on large and diverse sets of protein building blocks called domains. CLSS represents both sequences and structures as vectors in the same high-dimensional space, where distance relates to sequence-structure similarity. Thus, CLSS provides a natural way to represent the protein universe, reflecting evolutionary relationships, as well as structural changes. We find that CLSS refines expert knowledge about the global organization of protein space, and highlights transitional forms that resist hierarchical classification. CLSS reveals linkage between domains of seemingly separate lineages, thereby significantly improving our understanding of evolutionary design.

bioinformatics↗