Chemoselective Coupling Preserves the Substrate Integrity of Surface-Immobilized Oligonucleotides for Emulsion PCR-Based Gene Library Construction
Bibliographic record
Abstract
Combinatorial bead libraries figure prominently in next-generation sequencing and are also important tools for in vitro evolution. The most common methodology for generating such bead libraries, emulsion PCR (emPCR), enzymatically extends bead-immobilized oligonucleotide PCR primers in emulsion droplets containing a single progenitor library member. Primers are almost always immobilized on beads via noncovalent biotin–streptavidin binding. Here, we describe covalent bead functionalization with primers (∼10 6 primers/2.8-μm-diameter bead) via either azide–alkyne click chemistry or Michael addition. The primers are viable polymerase substrates (4–7% bead-immobilized enzymatic extension product yield from one thermal cycle). Carbodiimide-activated carboxylic acid beads only react with oligonucleotides under conditions that promote nonspecific interactions (low salt, low pH, no detergent), comparably immobilizing primers on beads, but yielding no detectable enzymatic extension product. Click-functionalized beads perform satisfactorily in emPCR of a site-saturation mutagenesis library, generating monoclonal templated beads (10 4 –10 5 copies/bead, 1.4-kb amplicons). This simpler, chemical approach to primer immobilization may spur more economical library preparation for high-throughput sequencing and enable more complex surface elaboration for in vitro evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".