NEXT-GENERATION SEQUENCING AND MOTIF GRAFTING APPLICATIONS IN SYNTHETIC ANTIBODY DISCOVERY
Bibliographic record
Abstract
The overall objective of this PhD project was to develop and validate methods for advancing the applications of two techniques, next-generation sequencing (NGS) and motif grafting, in synthetic antibody discovery.In the first part of this project, we developed an NGS-assisted antibody discovery platform by integrating phage-displayed single-framework synthetic antigenbinding fragment (Fab) libraries with Ion Torrent sequencing.We constructed a new singleframework synthetic Fab library containing 8.5 billion unique Fab clones, and validated its functionality by generating high affinity Fabs against Notch and Jagged receptors.We developed a rapid and simple method to link and sequence all diversified complementarity-determining regions (CDRs) in phage Fab pools without losing the CDR pairing information.We identified and reconstructed low-frequency rare Fab clones from NGS information in a reliable and highthroughput manner.In some cases, reconstructed rare clones (frequency ~0.1%) showed higher affinity and better specificity than high-frequency top clones isolated by Sanger sequencing, highlighting the importance of NGS in synthetic antibody discovery.In the second part of this project, we employed motif grafting to semi-rationally design phage-displayed synthetic Fab libraries that are biased towards interacting with a specific site on a receptor.We used structural information on the epidermal growth factor receptor (EGFR) homo-dimerization interaction to design a structure-guided Fab library that was biased towards interacting with domain II of EGFR.We used this structure-guided Fab library to obtain Fabs against the EGFR extracellular domain.For comparison, we used a naïve synthetic Fab library to generate an anti-EGFR Fab whose binding overlapped with the Fab isolated from the structure-guided Fab library.Both Fabs possessed low-nM binding values for recombinant and cell-surface EGFR and inhibited EGFmediated EGFR activation.Epitope mapping showed that domain II is partially responsible for the interaction of Fabs with EGFR.Further, both Fabs target unique epitopes that are different from previously validated epitopes on EGFR.In total, this PhD project resulted in novel methods for discovering synthetic antibodies using NGS and motif grafting techniques, three functional Fab libraries and numerous high-affinity Fabs against Notch, Jagged and EGF receptors.VDJ segments Variable (V), diversity (D) and joining (J) germline gene segments VEGFR-1 Vascular endothelial growth factor receptor 1 V H Variable heavy domain V L Variable light domain WT Wild type
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".