Molecular dynamics simulation and free energy analysis of the interaction of platinum-based anti-cancer drugs with DNA
Bibliographic record
Abstract
Cisplatin and oxaliplatin are two widely-used anti-cancer drugs which covalently bind to a same location in DNA strands. Platinum agents make intrastrand and interstrand cross-links with the N7 atoms of guanine nucleotides which prevent DNA from polymerization by causing a distortion in the double helix. Molecular dynamics simulations and free energy calculations were carried out to investigate the binding of two platinum-based anti-cancer drugs with DNA. We compared the binding of these drugs which differ in their carrier ligands, and hence their potential interactions with DNA. When a platinum agent binds to nucleotides, it causes a high amount of deformation in DNA structure. To find the extent of deformation, torsion angles and base pair and groove parameters of DNA were considered. These parameters were compared with normal B-DNA which was considered as the undamaged DNA. The formation of hydrogen bonds between drugs and DNA nucleotides was examined in solution. It was shown that oxaliplatin forms more hydrogen bonds than cisplatin. Our results confirm that the structure of the platinated DNA rearranges significantly and cisplatin tries to deform DNA more than oxaliplatin. The binding free energies were also investigated to understand the affinities, types and the contributions of interactions between drugs and DNA. It was concluded that oxaliplatin tendency for binding to DNA is more than cisplatin in solvent environment. The binding free energy was calculated based on the MM/PBSA and MM/GBSA methods and the results of QM/MM calculations verified them.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".