Cyanobacteriochrome-like GAF folds in phages revealed via AlphaFold proteomic modelling
Bibliographic record
Abstract
Accurate protein structure prediction followed by structural homology detection enable the functional annotation of otherwise obscure viral protein-coding genes. Here we employ AlphaFold proteomic modelling and structural homology searches on the genome of CrV-01T, a representative freshwater cyanophage, to reveal previously unknown structural homologs. One of these cryptic viral proteins is found to be a cyanobacteriochrome-like GAF fold (CGF) protein. Cyanobacteriochromes (CBCRs) are known to regulate phototaxis, cyclic nucleotide metabolism and optimization of light harvesting in cyanobacteria. Phylogenetic analyses indicate that the CGF protein of CrV-01T was probably acquired from a cyanobacterial host. We then use experimentally determined CBCR structures to query the Big Fantastic Virus Database and discover that CGFs are present among many different bacteriophages. The GAF domain sequence, which is a hallmark of CBCRs, can still be detected in some of these divergent viral proteins. Remarkably, viral CGF proteins harbor an N-terminal extension that in most cases is predicted to contain a transmembrane α-helix, indicating that they may bind the host membrane after being synthesized in the virocell. The presence of CGF protein-coding genes in cyanophage genomes suggests novel ways in which viruses may manipulate the metabolism of cyanobacteria, the most abundant oxygenic phototrophs on Earth. Overall, the findings reported here emphasize the importance of applying structural homology detection methods when annotating viral genomes and highlight the potential of AlphaFold for exploring the dark matter of the aquatic virosphere.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".