Molecular Dynamics Simulations on Nucleic Acid Binding Polymers Designed To Arrest Thrombosis
Bibliographic record
Abstract
Cancer-associated thrombosis is managed by the administration of anticoagulants and antithrombotic agents that have a high risk of inducing hemorrhagic complications. To develop safer strategies for antithrombotic therapy, in vivo activators of the intrinsic pathway, namely, cell-free nucleic acids (DNA and RNA) have been targeted with cationic, polyamine-based polymers. The cytotoxicity of the highly cationic polymers is a major drawback for their practical use, and biocompatible alternatives are in high demand. In this study, we carried out all-atom molecular dynamics simulations to systematically examine the DNA binding of polyamine-poly(ethylene glycol) (PEG) diblock polymers designed from biocompatible building blocks to inhibit the procoagulant activity of DNA. The differences in cationic charge, PEG chain length, and initial conformations of the polymers resulted in marked differences in their binding to DNA. We found that having an exposed cationic polyamine group is essential to polymer-DNA binding and a certain level of electrostatic interactions is necessary to maintain the bound state. Intrachain associations between the polyamine groups and PEG chains in some cases have led to a collapsed state of the polymer that precludes binding to DNA. This self-association is mainly due to a strong hydrogen bond between polymer polyamine and PEG groups and partly due to a partially charged semibranched polyamine group architecture. As polymer "masking" of DNA is thought to arrest DNA's prothrombotic activity, our findings highlight the desired structural features of the polymers for stronger DNA binding and provide insights into the design of novel antithrombotic agents.
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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 | 0.002 |
| 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 | 0.002 |
| 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".