Combining In Vitro and In Silico New Approach Methods to Investigate Type 3 Iodothyronine Deiodinase Chemical Inhibition Across Species
Bibliographic record
Abstract
New approach methodologies (NAMs) are being developed to reduce and replace vertebrate animal testing in support of ecotoxicology and risk assessment. The US Environmental Protection Agency's Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) bioinformatic tool was used to evaluate amino acid sequence conservation of the type 3 iodothyronine deiodinase (DIO3) enzyme across species to demonstrate NAM applications for understanding effects of chemical interactions with a specific protein target. Existing literature was used to identify critical amino acids for thyroid hormone binding and interaction with a reducing cofactor. The SeqAPASS tool identifies whether known critical amino acids involved in ligand binding are exact, partial, or not matches across species compared with a template species based on molecular weight and side chain classification. This evaluation guided the design of variant proteins representing critical amino acid substitutions found in various species. Site-directed mutagenesis of the wild-type (WT) human DIO3 gene sequence was used to create six variant proteins expressed in cell culture, which were then tested in vitro for chemical inhibition. Significant differences in in vitro median inhibitory concentration results were observed among variants for potential competitive inhibitors. A molecular model representing the WT human DIO3 was constructed using Molecular Operating Environment (MOE) software and mutated in silico to create the six variants. The MOE Site Finder tool identified the proposed catalytic and cofactor sites and potential alternative binding sites. Virtual docking did not provide affinity scores with sufficient resolution to rank the potency of the chemical inhibitors. Chemical characteristics, function and location of substituted amino acids, and complexities of the protein target are important considerations in developing NAMs to evaluate chemical susceptibility across species. Environ Toxicol Chem 2023;42:1032-1048. © 2023 University of Wisconsin-Madison. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".