Utilising DNA Modifying Enzymes for Method Development in Molecular Biology
Bibliographic record
Abstract
Method development plays a critical role in advancing molecular biology by enabling the detection, visualization, and interpretation of complex cellular processes. This dissertation focused on the development and optimization of methods based on DNA modifying enzymes to investigate DNA damage and protein–protein interactions—key mechanisms in genomic integrity, stress response, and gene regulation. The first part of the work involved the development of Polymerase-Assisted DNA Damage Analysis (PADDA), a method combining the comet assay with enzymatic labelling to distinctively detect DNA single-strand breaks (SSBs) and double-strand breaks (DSBs) with fluorescence microscopy. For a genome-wide detection of SSBs, a novel sequencing-based method—Sequence-Templated Erroneous End-Labelling sequencing (STEEL-seq) was developed. The method is based on an engineered, artificial DNA polymerase, Sloppymerase. Its highly error-prone activity allows for DNA synthesis in absence of a specific nucleotide (e.g. dATP), creating unique patterns of mismatches directly downstream of an SSB. These mismatches can be detected after DNA sequencing analysis and give information about bona fide SSBs. The method was validated using multiple sequencing platforms, revealing enrichment of SSBs at promoter regions of actively transcribed genes. The final part of the work covers a new antibody-based proximity assay for the detection of endogenous protein-protein interactions - Enzyme-Activated Proximity of Oligonucleotides Sensing (EPOS). Across multiple cellular models, EPOS could produce robust results for the detection of PPIs with higher resolution, improved dynamic range and increased sensitivity compared with in situ proximity ligation assay. Collectively, the methods developed during this project demonstrate the transformative potential of enzymatic tools in molecular biology. By enabling more precise and accessible analysis of DNA damage and protein interactions, these approaches provide valuable platforms for future research in genomics, cell biology, and biomedical science.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".