The role of metals in mammalian olfaction of low molecular weight organosulfur compounds
Bibliographic record
Abstract
Covering: up to the end of 2017While suggestions concerning the possible role of metals in olfaction and taste date back 50 years, only recently has it been possible to confirm these proposals with experiments involving individual olfactory receptors (ORs). A detailed discussion of recent experimental results demonstrating the key role of metals in enhancing the response of human and other vertebrate ORs to specific odorants is presented against the backdrop of our knowledge of how the sense of smell functions both at the molecular and whole animal levels. This review emphasizes the role of metals in the detection of low molecular weight thiols, sulfides, and other organosulfur compounds, including those found in strong-smelling animal excretions and plant volatiles, and those used in gas odorization. Alternative theories of olfaction are described, with evidence favoring the modified "shape" theory. The use of quantum mechanical/molecular modeling (QM/MM), site-directed mutagenesis and saturation-transfer-difference (STD) NMR is discussed, providing support for biological studies of mouse and human receptors, MOR244-3 and OR OR2T11, respectively. Copper is bound at the active site of MOR244-3 by cysteine and histidine, while cysteine, histidine and methionine are involved with OR2T11. The binding pockets of these two receptors are found in different locations in the three-dimensional seven transmembrane models. Another recently deorphaned human olfactory receptor, OR2M3, highly selective for a thiol from onions, and a broadly-tuned thiol receptor, OR1A1, are also discussed. Other topics covered include the effects of nanoparticles and heavy metal toxicants on vertebrate and fish ORs, intranasal zinc products and the loss of smell (anosmia).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".