<i>In Silico</i> Optimization of GAD65 <sub>114–122</sub> Autoantigen for Potential Type 1 Diabetes Antigen-Specific Immunotherapy
Bibliographic record
Abstract
Type 1 diabetes (T1D) is mediated by autoreactive T cells targeting pancreatic β-cell antigens, with CD8+ T cells specific for islet autoantigens playing a central role. CD8+ T cell reactivity to glutamic acid decarboxylase 65 (GAD65) in HLA-A*02:01 individuals has been reported to focus on the immunogenic region around residues 114–122 (VMNILLQYV). Here, we design GAD65 114–122 mimotopes with enhanced human leukocyte antigen (HLA) binding as potential T1D vaccine candidates. Using all-atom molecular dynamics (MD) simulation and free energy perturbation (FEP), we evaluate single, double, and swap mutations on HLA-A*02:01–GAD65 114–122 binding. Our results identify positions 3 and 7 as key sites for affinity enhancement. Position 3 favors negatively charged residues aspartic acid (N3D) and glutamic acid (N3E) over native asparagine (ASN), suggesting favorable electrostatic interactions, while position 7 prefers hydrophobic residues methionine (Q7M) and isoleucine (Q7I) over native glutamine (GLN), enhancing binding stability. Double mutations at both positions 3 and 7 display an overall additive or even synergistic effect, with N3D_Q7M, N3D_Q7I, N3E_Q7M, and N3E_Q7I double mutants identified as strong candidates for further experimental validation of T cell activation. This work highlights key insights for optimizing antigen-based vaccine design and optimization for T1D.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".