TSPDB: A curated resource of tailspike proteins with potential applications in phage research
Bibliographic record
Abstract
Abstract Phages are ubiquitous viruses that drive bacterial evolution through infection and replication within host bacteria. Phage tailspike proteins (TSPs) are key components of phage tail structures, exhibiting polysaccharide depolymerase activity and host specificity. Despite their potential as novel antimicrobials, few TSPs have been fully characterized due to laborious detection techniques. To address this, we present TSPDB, a curated resource for rapid detection of TSPs in genomics and metagenomics sequence data. We mined public databases, obtaining 17,211 TSP sequences, which were filtered to exclude duplicates and partial sequences, resulting in 8,099 unique TSP sequences. TSPDB contains TSPs from over 400 bacterial genera, with significant diversity among them as revealed by the phylogenetic analysis. The top 13 genera represented were Gram-positive, with Bacillus, Streptococcus , and Clostridium being the most common. Of note, Phage TSPs in Gram-positive bacteria were on average 1 Kbp larger than those in Gram-negative bacteria. TSPDB has been applied in a recent study to screen phage genomes, demonstrating its potential for functional annotation. TSPDB serves as a comprehensive repository and a resource for researchers in phage biology, particularly in phage associated therapy and antimicrobial or biocontrol applications. TSPDB is compatible with bioinformatics tools for in silico detection of TSPs in genomics and metagenomic data, and is freely accessible on GitHub and Figshare, providing a valuable resource for the scientific community.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.031 |
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".