Giant genes are rare but implicated in cell wall degradation by predatory bacteria
Bibliographic record
Abstract
Abstract Across the tree of life, gene lengths vary, but most are no more than a few thousand base pairs in length. The largest protein often reported is the ∼40,000 aa eukaryotic Titin. Even larger proteins may occur in the rapidly expanding set of metagenome-derived sequences, but their existence may be obscured by assembly fragmentation. Here, we leverage genome curation to complete metagenome-derived sequences that encode predicted proteins of up to 85,804 aa. Overall, the findings illuminate a huge knowledge gap related to giant proteins. Although predicted proteins of >30,000 aa occur in bacterial phyla such as Firmicutes and Actinobacteria , they are most common in ca. Omnitrophota, ultra small bacteria that adopt predatory lifestyles. All full length giant genes encode numerous transmembrane regions and most encode divergent secA DEAD helicase domains. In silico structural prediction of protein subregions was required to identify domains in unannotated protein segments, and revealed putative domains implicated in attachment and carbohydrate degradation. Many giant genes in new complete and near-complete Omnitrophota genomes occur in close proximity to genes homologous to type II secretion systems as well as carbohydrate import systems. This, in combination with the domain content, suggests that many bacterial giant proteins enable prey adhesion and cell wall digestion during bacterial predation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".