Reconnoitering Mycobacterium tuberculosis lipoproteins to design subunit vaccine by immunoinformatics approach
Bibliographic record
Abstract
Background: Tuberculosis is an aerosol transmitted disease of human beings caused by Mycobacterium tuberculosis (Mtb). The only available vaccine for Mtb is Bacillus Calmette-Guérin (BCG). Currently no alternative or booster is available for BCG. The objective of this predictive approach was based on binding of MHC-I and MHC-II and B cell epitopes of Mtb for mouse host.Methods: Immunoinformatics approach was used to design subunit vaccine (SV) by joining 8 MHC-I bindings, 6 MHC-II bindings, and 8 B-Cell epitopes with AAV, GPGPG, and KK amino acid linkers, respectively. The efficacy of the SV was enhanced through Mtb protein Rv3763 (LpqH, PDB ID= 4ZJM) as an adjuvant at the N-terminal of SV. The in silico analyses evaluated the SV to predict allergenicity, antigenicity, and physico-chemical properties.Results: Predictions revealed that SV is non-allergic and highly antigenic. The physico-chemical analysis showed that the SV was stable and basic in nature. The three-dimensional structure of SV was stable with a high binding affinity against the mouse TLR2 receptor. In silico cloning suggested the effective transformation of SV into the eukaryotic expression vector.Conclusion: This study permits preclinical validation of the designed SV in mouse host to confirm its immunogenic potential and efficacy, which will help in controlling tuberculosis.Keywords: Immunoinformatics; Docking; Subunit vaccine; Lipoprotein; Tuberculosis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".