What this entity is
Canonical name: KEScience AlphaFold. 1
Known aliases: AlphaFold MSA-depth dataset; KBase Data Lakehouse AlphaFold snapshot. 1
Stable external identifier: No stable external identifier was reported in the source document. 1
KEScience AlphaFold is a dataset used to provide multiple-sequence-alignment (MSA) depths for gene-cluster representative sequences in the KBase Data Lakehouse analysis. 1 The analysis joined it with kbase-ke-pangenome, Bakta annotations, and interproscan domain annotations. 1
The BERDL Data Atlas inventories 241,070,489 AlphaFold predicted structures, establishing this dataset as a major structural reference layer in the data lakehouse. 2 This supports its use as a structural complement to pangenome and fitness data, while the atlas refines the earlier description by documenting a separate validated structural-fitness join through SwissProt best hits. 2
Key facts from alphafold_msa_annotation
The analysis contained 132,531,501 total gene clusters, of which 38,804,903 (29.3%) had a real UniProt accession and 38,051,842 (28.7%) bridged successfully to AlphaFold MSA depths. 1 The remaining 70.7% lacked UniRef100 identifiers or had UniParc-only identifiers without an AlphaFold entry. 1
Among the bridged clusters, core genes had a median MSA depth of 15,308, compared with 5,299 for auxiliary+singleton clusters and 5,527 for auxiliary non-singleton clusters. 1 The core median was 2.89× higher than auxiliary+singleton and 2.77× higher than auxiliary non-singleton clusters. 1
The 10th-percentile MSA depth was 334 for core genes versus 25–32 for accessory genes. 1 Core clusters included 415,733 (1.6%) with MSA depth below 10, while auxiliary non-singleton clusters included 245,002 (4.6%) and auxiliary+singleton clusters included 392,959 (5.5%) below that threshold. 1
Across 38,051,842 gene cluster–UniProt pairs, MSA depth and domain-hit count had Spearman ρ = 0.7563. 1 Mean domain hits increased from 0.59 for MSA depth below 10 to 10.83 for MSA depth at least 10,000, while mean distinct InterPro families increased from 0.059 to 4.601. 1
The report identified 415,603 distinct core clusters with MSA depth below 10 across 14,768 species clades. 1 Their mean and median MSA depths were 4.57 and 4.0, respectively, and 286,439 (68.9%) were hypothetical. 1 Only 137 (0.033%) were EC-annotated and 346 (0.083%) were KEGG-mapped. 1
These low-MSA-depth core clusters were interpreted as candidates for experimental structural characterisation because they are conserved by pangenome classification yet structurally isolated from characterised sequence space; this is a prioritisation hypothesis rather than direct experimental validation. 1
The dataset used a static version-6 KBase Data Lakehouse AlphaFold snapshot. 1 MSA depth was looked up for each cluster’s representative sequence, so within-cluster sequence diversity was not represented. 1
Validated structural-fitness integration
The Data Atlas refines the presumed join path: FitnessBrowser does not expose protein_id; the validated path joins gene-fitness records to SwissProt best hits on the composite orgId, locusId key, then joins sprotAccession to uniprot_accession in AlphaFold entries. 2 Of 79,180 genes with a SwissProt best hit, 78,753 were represented in AlphaFold, and 55,454 genes across 48 organisms had both fitness data and an AlphaFold model; the joined cohort contained 22,303 distinct AlphaFold models. 2
Within that cohort, 6,635 genes were essential, defined as min_fit ≤ −4; 8,271 were strong-defect, 10,950 moderate, 29,467 mild, and 131 had no defect, with genes tested under an average of 121–187 conditions per gene. 2 This supports the existing proposal to analyse low-MSA-depth core clusters alongside gene-essentiality and tnseq, but the cohort does not itself validate the structural-characterisation hypothesis. 2
The validated AlphaFold entries lack per-residue pLDDT and structural-feature data, so downstream structure–function analysis requires those features to be ingested or computed from PDB files. 2
Relations to other wiki pages
KEScience AlphaFold supplies structural-representation evidence for pangenome-integration, including the 2.9× core/accessory MSA-depth gradient and the low-MSA-depth core subset. 1 Its domain-depth relationship connects to multi-omics-integration and supports proposed joins with kescience-fitnessbrowser measurements. 1 The atlas supports this adjacency by validating a 55,454-gene FitnessBrowser–AlphaFold cohort. 2 The low-MSA-depth core clusters are proposed candidates for analysis alongside gene-essentiality and tnseq fitness measurements. 1
Source
- AlphaFold MSA Depth as a Lens on the Bacterial Annotation Gap — report describing AlphaFold MSA depth as a lens on the bacterial annotation gap. 1
- BERDL Data Atlas — Inventory, Topic Map, and Cross-Reference Synergies — atlas inventory and validation of the AlphaFold structural-fitness bridge. 2