Computational Immunogenetic Analysis of Botulinum Toxin A Immunogenicity and HLA Gene Haplotypes: New Insights
Bibliographic record
Abstract
Botulinum toxin A (BoNT-A) is widely used in both therapeutic and aesthetic settings; however, the formation of neutralizing antibodies (NAbs) remains a critical concern, leading to treatment failure. Immunogenic responses are known to vary between individuals due to HLA polymorphisms. Although some claim that neurotoxin-associated proteins (NAPs) shield BoNT-A from immune detection or are themselves immunogenic, there is limited molecular evidence supporting either view. This study applies computational immunogenetics to explore BoNT-A immunogenicity, focusing on HLA binding and the influence of accessory proteins. Epitope mapping, molecular docking, and HLA binding predictions were used to evaluate interactions between BoNT-A epitopes and selected class II HLA alleles (HLA-DQA1*01:02, HLA-DQA1*03:03, HLA-DQB1*06:04, HLA-DQB1*03:01, and HLA-DRB1*15:01). To assess the potential immunomodulatory role of NAPs, molecular dynamics (MD) simulations, solvent-accessible surface area (SASA) analysis, and electrostatic potential mapping were also conducted. Key epitopes-L11, N25, and C10-showed strong binding affinities to HLA-DQA1*01:02, HLA-DQB1*06:04, and HLA-DQA1*03:03, indicating a potential immunodominant role. NAPs did not obstruct these epitopes but slightly increased their exposure and appeared to stabilize the toxin structure. Electrostatic mapping and binding free energy calculations suggested no significant immunogenic shift in the presence of NAPs. BoNT-A immunogenicity appears to be influenced by HLA allele variability, reinforcing the value of patient-specific genetic profiling. The presumed immunogenic role of NAPs remains unsubstantiated at the molecular level, underscoring the need for evidence-based evaluation over commercial rhetoric. While these findings provide valuable molecular insight, it is important to acknowledge that they are derived entirely from in silico analyses. As such, experimental validation remains essential to confirm the immunological relevance of these predicted interactions. Nonetheless, this computational framework offers a rational basis for guiding future clinical research and the development of HLA-informed BoNT-A therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".