Unnatural amino acids improve affinity and modulate immunogenicity: Developing peptides to treat MHC type II autoimmune disorders
Bibliographic record
Abstract
Abstract Many autoimmune diseases, including multiple sclerosis (MS), rheumatoid arthritis (RA), and celiac disease (CD), arise from improper immune system recognition of self or benign peptides as threats. No autoimmune disease currently has a cure. Many treatments suppress the entire immune system to decrease symptom severity. The core molecular interaction underlying these diseases involves specific alleles of the human leukocyte antigen (HLA) receptor hosting the immunodominant peptides associated with the disease (i.e., myelin basic protein, Type II collagen, or α‐gliadin) in their binding groove. Once bound, circulating T‐cells can recognize the HLA‐antigen complex and initiate the complex cascade that forms an adaptive immune response. This initial HLA‐antigen interaction is a promising target for therapeutic intervention. Two general strategies have been pursued: altered peptide ligands (APLs) that attempt to recruit a different class of T‐cell to induce an anti‐inflammatory response to balance the pro‐inflammatory response associated with the antigen; and HLA‐blockers (HLABs), peptides that quantitatively displace the antigen to inhibit the immune response. Both approaches would benefit from improved HLA‐drug binding, but as the HLA receptors are highly promiscuous, the binding sites are not specific for any natural amino acid. Unnatural amino acids, either designed or screened through high‐throughput assays, may provide a solution. This review summarizes the nascent field of using noncanonical residues to treat MS, RA and CD, focusing on the importance of specific molecular interactions, and provides some examples of the synthesis of these unnatural residues.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".