Phylogenetic tree-based amino acid sequence generation for proteomics data analysis of unknown species
Bibliographic record
Abstract
In bottom-up proteomics, selecting an appropriate protein amino acid sequence database is vital for reliable peptide identification. However, this approach excludes species with unsequenced genomes, limiting the comprehensiveness. This is a major challenge in current microbiota proteomics, a rapidly developing field, which involves simultaneously assigning proteins to species in a sample and analyzing them using databases of protein amino acid sequences with known genomes. We aimed to develop a method to extend the database species diversity by generating protein amino acid sequences of unknown species using phylogenetic relationships among known species. To evaluate this approach, we generated the Helicobacter pylori F16 strain sequence based on the phylogenetic relationships of 29 closely related strains (excluding F16). Consequently, the percentages of peptides that matched the peptides obtained from the reference F16 strain increased by 5%, based on sequence generation. Proteomics data analyses were performed on the F16 strain using the generated sequence database to validate peptide identification. Peptide spectral match decreased when the database was expanded using sequence generation owing to a decrease in sensitivity primarily caused by an increase in decoy hits. The decrease in identification sensitivity caused by large-scale databases could be improved by introducing a novel score, Ion Cover Score, based on spectral matching. The sequence generation method used in the present study and the introduction of scores based on spectral matching could accelerate proteomics development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".