CAcnesDB: a database of Cutibacterium acnes with integrated functional insights derived from multi-modal genome annotation.

Abstract

Cutibacterium acnes is a predominant member of the human skin microbiome, generally as a beneficial or a benign inhabitant, which under specific conditions transforms into a pathogen, causing acne and associated inflammatory responses. While there is a wealth of data on the microbiology and genomics of the microbe, there is a lack of comprehensive annotation of the proteins coded by the genome, slowing progress in understanding its unique metabolic versatility and survival in host hypoxic sebaceous niches. We configured an integrated pipeline for functional annotation of the C. acnes genome, combining gene calling, domain and ontology mapping, KEGG pathways assignment, proteome-scale structural modeling, predicting pockets and associating the small-molecule metabolites based on binding site similarity to facilitate structure-to-function annotation. Structural predictions using ColabFold covered nearly the full proteome, with 83.7% of models showing high confidence. To infer protein function, ligand-binding sites in the C. acnes modeled proteome were predicted using a consensus of three independent algorithms, PocketDepth, SiteHound, and Fpocket. Small molecule ligands were mapped to the resulting pockets through structural comparison of the pockets with known small-molecule binding sites in PDB where there was direct or indirect evidence for such binding, resulting in the C. acnes predicted ligandome (CApL) comprising 693 protein-ligand pairs across 534 proteins. CAcnesDB is a comprehensive database of the C. acnes genome, hosting functional annotation from multiple levels of input and analyses, covering 2297 protein-coding genes.

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