A New Immunogenic Structure of Polyepitopic Fusion against Leishmania major: In Silico Study
Bibliographic record
Abstract
Background: The lack of complete protection against leishmaniasis and the challenges of anti-leishmaniasis drug treatment have made the treatment process more difficult. This study aimed to develop a new strategy for preparing a vaccine against cutaneous leishmaniasis using some of the antigenic proteins of the Leishmania parasite. Methods: This study was carried out in 2022 at Shahid Chamran University of Ahvaz, Ahvaz, Iran. After preparing suitable epitopes of the Leishmania parasite and examining their antiparasitic properties, the process of making a fusion vaccine was performed and with the help of various bioinformatics tools, physicochemical and structural properties as well as immunological and simulation properties were studied and finally optimized. Construction and cloning were performed in the E.coli K12 system and finally, the docking process was performed with Toll-like receptors (TLRs), major histocompatibility complex I (MHC-I), and MHC-II receptors. With the help of selected epitopes of the Leishmania parasite, which had a high percentage of population coverage, a stable, antigenic, and non-allergenic chimeric vaccine was predicted. Results: The results of the structural analysis of the TLR5\vaccine complex and simulation of its molecular dynamics showed a sufficiently stable binding. It also showed good potential for stimulation and production of active B cells and memory, as well as the potential for CD8+ T, CD4+ T cell production and development of Th2 and Th1-induced immune responses. Conclusion: Computational results showed that the designed immunogenic structure has the potential to adequately stimulate cellular and humoral immune responses against Leishmania parasitic disease. As a result of evaluating the effectiveness of the candidate vaccine through in vivo and in vitro immunological tests, it can be suggested as a vaccine against Leishmania major.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".