Ionic Residues of Human Serum Transferrin Affect Binding to the Transferrin Receptor and Iron Release
Bibliographic record
Abstract
Efficient delivery of iron is critically dependent on the binding of diferric human serum transferrin (hTF) to its specific receptor (TFR) on the surface of actively dividing cells. Internalization of the complex into an endosome precedes iron removal. The return of hTF to the blood to continue the iron delivery cycle relies on the maintenance of the interaction between apohTF and the TFR after exposure to endosomal pH (≤6.0). Identification of the specific residues accounting for the pH-sensitive nanomolar affinity with which hTF binds to TFR throughout the cycle is important to fully understand the iron delivery process. Alanine substitution of 11 charged hTF residues identified by available structures and modeling studies allowed evaluation of the role of each in (1) binding of hTF to the TFR and (2) TFR-mediated iron release. Six hTF mutants (R50A, R352A, D356A, E357A, E367A, and K511A) competed poorly with biotinylated diferric hTF for binding to TFR. In particular, we show that Asp356 in the C-lobe of hTF is essential to the formation of a stable hTF-TFR complex: mutation of Asp356 in the monoferric C-lobe hTF background prevented the formation of the stoichiometric 2:2 (hTF:TFR monomer) complex. Moreover, mutation of three residues (Asp356, Glu367, and Lys511), whether in the diferric or monoferric C-lobe hTF, significantly affected iron release when in complex with the TFR. Thus, mutagenesis of charged hTF residues has allowed identification of a number of residues that are critical to formation of and release of iron from the hTF-TFR complex.
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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) | 0.002 | 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".