Probing tRNA interaction with biogenic polyamines
Bibliographic record
Abstract
Biogenic polyamines are found to modulate protein synthesis at different levels. This effect may be explained by the ability of polyamines to bind and influence the secondary structure of tRNA, mRNA, and rRNA. We report the interaction between tRNA and the three biogenic polyamines putrescine, spermidine, spermine, and cobalt(III)hexamine at physiological conditions, using FTIR spectroscopy, capillary electrophoresis, and molecular modeling. The results indicated that tRNA was stabilized at low biogenic polyamine concentration, as a consequence of polyamine interaction with the backbone phosphate group. The main tRNA reactive sites for biogenic polyamine at low concentration were guanine-N7/O6, uracil-O2/O4, adenine-N3, and 2'OH of the ribose. At high polyamine concentration, the interaction involves guanine-N7/O6, adenine-N7, uracil-O2 reactive sites, and the backbone phosphate group. The participation of the polycation primary amino group, in the interaction and the presence of the hydrophobic contact, are also shown. The binding affinity of biogenic polyamine to tRNA molecule was in the order of spermine > spermidine > putrescine with K(Spm) = 8.7 × 10(5) M(-1), K(Spd) = 6.1 × 10(5) M(-1), and K(Put) = 1.0 × 10(5) M(-1), which correlates with their positively charged amino group content. Hill analysis showed positive cooperativity for the biogenic polyamines and negative cooperativity for cobalt-hexamine. Cobalt(III)hexamine contains high- and low-affinity sites in tRNA with K(1) = 3.2 × 10(5) M(-1) and K(2) = 1.7 × 10(5) M(-1), that have been attributed to the interactions with guanine-N7 sites and the backbone PO(2) group, respectively. This mechanism of tRNA binding could explain the condensation phenomenon observed at high Co(III) content, as previously shown in the Co(III)-DNA complexes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".