Toxicity inhibition strategy of microplastics to aquatic organisms through molecular docking, molecular dynamics simulation and molecular modification
Bibliographic record
Abstract
In the present study, the combined toxic effect of microplastics and their additives (five) on aquatic organisms (zebrafish) was studied using full factorial design method, molecular docking, and molecular dynamics (MD) simulation technology. The aquatic toxicity control programmer was designed to improve the optimal combination of plasticizer and microplastics based on the design of environment-friendly phthalic acid ester (PAE) derivatives. First, a total of 64 groups of microplastic-additives were designed using the full factorial design method. Next, the microplastic-additives and aquatic receptor protein were docked together, and the binding energy of these complexes was calculated using the MD simulation method. The results revealed that the aquatic toxicity effects of different microplastic-additive combinations were variable; therefore, the optimal combination of microplastics exhibiting the lowest aquatic toxicity effect could be screened out. Base on the analyzing the bonding effect and surrounded amino acid residues between the microplastic additives and receptor protein, the main driving forces for the binding of the microplastic-additive and the protein were hydrophobic force, hydrogen bonding force and electrostatic force. The main effects and the second-order interaction of the microplastic-additives combination were analyzed using the fixed-effect model. The main additives that affect the aquatic toxicity of the microplastics can be known. In addition, based on the MD simulation of the molecular replacement of PAE derivatives, the optimal level of component combination of low aquatic toxicity effect of microplastics was constructed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".