Fingerprinting of Proteins that Mediate Quagga Mussel Adhesion using a De Novo Assembled Foot Transcriptome
Bibliographic record
Abstract
The European freshwater mollusk Dreissena bugensis (quagga mussel), an invasive species to North America, adheres to surfaces underwater via the byssus: a non-living protein 'anchor'. In spite of its importance as a biofouling species, the sequence of the majority of byssal proteins responsible for adhesion are not known, and little genomic data is available. To determine protein sequence information, we utilized next-generation RNA sequencing and de novo assembly to construct a cDNA library of the quagga mussel foot transcriptome, which contains over 200,000 transcripts. Quagga mussel byssal proteins were extracted from freshly induced secretions and analyzed using LC-MS/MS; peptide spectra were matched to the transcriptome to fingerprint the entire protein primary sequences. We present the full sequences of fourteen novel quagga mussel byssal proteins, named Dreissena bugensis foot proteins 4 to 17 (Dbfp4-Dbfp17), and new sequence data for two previously observed byssal proteins Dbfp1 and Dbfp2. Theoretical masses of the newly discovered proteins range from 4.3 kDa to 21.6 kDa. These protein sequences are unique but contain features similar to glue proteins from other species, including a high degree of polymorphism, proteins with repeated peptide motifs, disordered protein structure, and block structures.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".