Computational Characterization of Small Molecules Binding to the Human XPF Active Site and Virtual Screening to Identify Potential New DNA Repair Inhibitors Targeting the ERCC1-XPF Endonuclease
Bibliographic record
Abstract
The DNA excision repair protein ERCC-1-DNA repair endonuclease XPF (ERCC1-XPF) is a heterodimeric endonuclease essential for the nucleotide excision repair (NER) DNA repair pathway. Although its activity is required to maintain genome integrity in healthy cells, ERCC1-XPF can counteract the effect of DNA-damaging therapies such as platinum-based chemotherapy in cancer cells. Therefore, a promising approach to enhance the effect of these therapies is to combine their use with small molecules, which can inhibit the repair mechanisms in cancer cells. Currently, there are no structures available for the catalytic site of the human ERCC1-XPF, which performs the metal-mediated cleavage of a DNA damaged strand at 5′. We adopted a homology modeling strategy to build a structural model of the human XPF nuclease domain which contained the active site and to extract dominant conformations of the domain using molecular dynamics simulations followed by clustering of the trajectory. We investigated the binding modes of known small molecule inhibitors targeting the active site to build a pharmacophore model. We then performed a virtual screening of the ZINC Is Not Commercial 15 (ZINC15) database to identify new ERCC1-XPF endonuclease inhibitors. Our work provides structural insights regarding the binding mode of small molecules targeting the ERCC1-XPF active site that can be used to rationally optimize such compounds. We also propose a set of new potential DNA repair inhibitors to be considered for combination cancer therapy strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".