Transfer RNA Binding to Human Serum Albumin: A Model for Protein–RNA Interaction
Bibliographic record
Abstract
Protein-RNA complexation is essential in cell biological functions. Transfer RNAs are bound to aminoacyl-tRNA synthetases for the translation of the genetic code during protein synthesis, while ribonucleoproteins bind RNA in posttranscriptional regulation of gene expression. A recent report showed the interacton of human serum albumin (HSA) with DNA duplex, in which two binding sites with strong and weak association constants were detected. We now examine the interaction of tRNA with human serum albumin (HSA) in aqueous solution at physiological conditions, using a constant RNA concentration of 12.5 mM (phosphate) and various HSA contents of 0.04 to 0.6 mM. Affinity capillary electrophoresis and FTIR spectroscopic methods were used to determine the protein binding mode, the association constant, sequence preference, and the biopolymer secondary structural changes in the HSA-RNA complexes. Spectroscopic evidence showed two types of HSA-RNA complexes with an overall binding constant of K = 1.45 x 10(4) M(-1). The major binding sites were located on the G-C bases and the backbone PO2 group. The protein-RNA interaction stabilizes the HSA secondary structure, and no major alterations of A-RNA structure or protein conformation occurred.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".