Silent Encoding of Chemical Post-Translational Modifications in Phage-Displayed Libraries
Bibliographic record
Abstract
In vitro selection of chemically modified peptide libraries presented on phage, while a powerful technology, is limited to one chemical post-translational modification (cPTM) per library. We use unique combinations of redundant codons to encode cPTMs with "silent barcodes" to trace multiple modifications within a mixed modified library. As a proof of concept, we produced phage-displayed peptide libraries Ser-[X]4-Gly-Gly-Gly, with Gly and Ser encoded using unique combinations of codons (TCA-[X]4-GGAGGAGGA, AGT-[X]4-GGTGGTGGT, etc., where [X]4 denotes a random NNK library). After separate chemical modification and pooling, mixed-modified libraries can be panned and deep-sequenced to identify the enriched peptide sequence and the accompanying cPTM simultaneously. We panned libraries bearing combinations of modifications (sulfonamide, biotin, mannose) against matched targets (carbonic anhydrase, streptavidin, concanavalin A) to identify desired ligands. Synthesis and validation of sequences identified by deep sequencing revealed that specific cPTMs are significantly enriched in panning against the specific targets. Panning on carbonic anhydrase yielded a potent ligand, sulfonamide-WIVP, with Kd = 6.7 ± 2.1 nM, a 20-fold improvement compared with the control ligand sulfonamide-GGGG. Silent encoding of multiple cPTMs can be readily incorporated into other in vitro display technologies such as bacteriophage T7 or mRNA display.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".