Method to generate highly stable D-amino acid analogs of bioactive helical peptides using a mirror image of the entire PDB
Bibliographic record
Abstract
Significance Using D-amino acids as the building blocks for bioactive peptides can dramatically increase their potency. However, simply swapping regular levorotary amino acids for dextrorotary (D)-amino acids alters the peptide surface topology and function is lost. Current methods to overcome this are not generally applicable and exclude the majority of therapeutic targets. By creating a mirror image of all 111,867 protein structures in the Protein Data Bank (PDB), we convert this repository into a D-peptide database with 2.8 million D-peptide structures. This D-PDB can be searched to find therapeutically active topologies, demonstrated here by the discovery of D-peptide GLP1R and PTH1R agonists. Evaluation of D-PDB coverage suggests that it holds candidates for most therapeutic targets and, thus, potentially contains hundreds of potent drug leads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".