Virtual Screening and Biological Evaluation of Inhibitors Targeting the XPA-ERCC1 Interaction
Bibliographic record
Abstract
BACKGROUND: Nucleotide excision repair (NER) removes many types of DNA lesions including those induced by UV radiation and platinum-based therapy. Resistance to platinum-based therapy correlates with high expression of ERCC1, a major element of the NER machinery. The interaction between ERCC1 and XPA is essential for a successful NER function. Therefore, one way to regulate NER is by inhibiting the activity of ERCC1 and XPA. METHODOLOGY/PRINCIPAL FINDINGS: Here we continued our earlier efforts aimed at the identification and characterization of novel inhibitors of the ERCC1-XPA interaction. We used a refined virtual screening approach combined with a biochemical and biological evaluation of the compounds for their ability to interact with ERCC1 and to sensitize cells to UV radiation. Our findings reveal a new validated ERCC1-XPA inhibitor that significantly sensitized colon cancer cells to UV radiation indicating a strong inhibition of the ERCC1-XPA interaction. CONCLUSIONS: NER is a major factor in acquiring resistance to platinum-based therapy. Regulating the NER pathway has the potential of improving the efficacy of platinum treatments. One approach that we followed is to inhibit the essential interaction between the two NER elements, ERCC1 and XPA. Here, we performed virtual screening against the ERCC1-XPA interaction and identified novel inhibitors that block the XPA-ERCC1 binding. The identified inhibitors significantly sensitized colon cancer cells to UV radiation indicating a strong inhibition of the ERCC1-XPA interaction.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".