B-P.09: UniProt Pan Proteomes: a scalable resource for comparative proteome analyses and exploring species diversity
Authors
Keywords
UniProt, pan proteomes,
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The rapid growth of sequence data requires scalable frameworks that capture species-level diversity beyond single reference proteomes. We present UniProt Pan Proteomes (PP), a resource integrating conserved (core-like) and variable (accessory) proteins to support comparative proteomics, pathogenicity and resistance studies, gene essentiality analyses, and population-aware target discovery.
For each species with >=3 eligible proteomes, sequences are clustered with MMseqs2 (90% identity, 50% coverage) to generate a non-redundant PP. The best annotated protein per cluster is selected using a hierarchical strategy prioritising reference proteomes and reviewed entries. Each species's PP dataset includes three outputs: FASTA entries with protein frequency; PP matrix, akin to the pan-genome (PG) presence-absence matrix; a statistics file summarising proteome composition, clustering, and frequencies.
The upcoming 2026_02 release includes ~3,200 species, created from ~70,000 proteomes, and ~270 million clustered proteins. Preliminary benchmarking in Escherichia coli shows strong PP and PG concordance in core-like subsets (96% bidirectional matching), while full-set PP-PG comparisons retain expected accessory diversity. Inter-species PP comparisons (e.g. E. coli vs. Shigella) identify conserved and divergent sequences. The Streptococcus pneumoniae PP, built from ~156 proteomes, spans ~40 pneumococcal lineages showcasing robust coverage of species diversity. It comprises 6,411 protein clusters consistent with recent PG studies. To demonstrate translational use, we mapped 64 S. pneumoniae vaccine antigens to within- and across-species PP clusters to identify homologues with functional annotations for target prioritisation. UniProt PP facilitates cross- species comparative analyses, supporting diverse research use cases. We invite community feedback to shape future releases.
Co-authors: Giuseppe Insana, John Lees, Stephanie Lo, Maria Martin
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