<i>De novo</i>identification of binding sequences for antibody replacement molecules
Bibliographic record
Abstract
A new in silico method has been developed that automatically identifies peptide sequences that can bind to targets of known three-dimensional structure. The method is potentially faster and more economical than traditional methods of raising antibodies by means of hybridomas or biopanning technology. The current algorithm creates an initial peptide library that is either completely random or that is constrained by the user. This library represents only a small fraction of possible sequence space and the peptides are created with a specified torsional geometry. The library is used as input to any number of available molecular docking programs and the library is docked and scored. The final rank ordering is then used to create a new library by constraining that library to the sequence conservation pattern deduced from the top N-scoring peptides in the first round. Successive rounds of screening, scoring, and new library creation ultimately results in the system converging to a final solution set of peptides. To test the method, a family of novel peptides that can bind to, and inhibit the enzyme Deoxyribonuclease I has been discovered. The peptides inhibit the enzyme either alone or when placed into a protein backbone structure as has been confirmed experimentally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".