Vaccinomics-driven proteome-wide screening of <i>Haemophilus influenzae</i> for the prediction of common putative vaccine candidates
Bibliographic record
Abstract
Haemophilus influenzae colonizes the respiratory tract and is associated with life-threatening invasive infections. The recent rise in its global prevalence, even in the presence of multiple vaccines, indicates an urgent need to develop effective cross-strain vaccine strategies. Our work focused on identifying the universally conserved antigenic regions of H. influenzae that can be used to develop new vaccines. A variety of bioinformatics tools were applied for the comprehensive geno-proteomic analysis of H. influenzae type a strain, as reference serotype, through which subcellular localization, essentiality, virulence, and non-host homology were determined. B and T cell epitope mapping of the 3D protein structures were performed. Thereafter, molecular docking with HLA_DRB1*0101 and comparative genome analysis established the candidature of the identified regions. Based on the established vaccinomics criteria, five target proteins were predicted as novel vaccine candidates. Among these, nine epitopic regions that could regulate lymphocyte activity through strong protein–protein interactions were identified. Comparative genomic analysis revealed that the identified regions were highly conserved among the different strains of H. influenzae. Based on multiple immunogenic factors, five prioritized proteins and their predicted epitopes were identified as ideal common putative vaccine candidates against typeable strains.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Proteome wide screening for vaccine candidates; the object is bacterial immunogenicity.
The work predicts vaccine candidates for Haemophilus influenzae rather than studying research practice.
In silico vaccine-candidate discovery for H. influenzae is biomedical domain science using methods, not studying methods.
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) | 0.001 | 0.000 |
| 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".