PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery
Bibliographic record
Abstract
Abstract As biology becomes increasingly data-driven, so too does the field of phage lysins, enzymes that degrade bacterial cell walls and hold promise as alternatives to traditional antibiotics. Five years ago, we introduced PhaLP, a centralized resource for Pha ge L ytic P rotein sequences and associated metadata to support global research efforts. Here, we present PhaLP 2.0, a significantly enhanced database designed to overcome key challenges in the computational study of lysins by integrating newly identified lysins obtained from thousands of metagenomes. To expand the known diversity of lysins beyond those from cultured phages, we developed SUBLYME, a protein embedding-based machine learning S oftware designed to U ncover and classify B acteriophage Ly sins in Me tagenomic datasets. Using embeddings derived from the prior well-curated protein sequences of the original PhaLP database, we trained support vector machines to distinguish lysins from non-lysins in viromes and classify them as either endolysins or virion-associated lysins. The models achieved an average F1-score of 98% on held-out lysin clusters. SUBLYME enabled the discovery of 743,000 new lysin sequences from EnVhogDB, a virome-derived protein database, increasing the number of known lysin clusters by a factor of 40, from 1,000 to 40,000. PhaLP 2.0 entries were annotated by integrating Pfam functional predictions to the refined delineations obtained with SPAED, an algorithm that leverages the predicted aligned error matrix from AlphaFold predictions to identify domain boundaries. Both SUBLYME and the PhaLP 2.0 database are accessible online at https://github.com/Rousseau-Team/sublyme and http://phalp.ugent.be , respectively. Together, these advances establish PhaLP 2.0 as a comprehensive and scalable portal for the discovery, classification, and sequence analysis of phage lysins, paving the way for future antibacterial applications and evolutionary insights. Abstract Figure
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".