Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing
Bibliographic record
Abstract
Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".