The Zn<sup>2+</sup> and Ca<sup>2+</sup>‐binding S100B and S100A1 proteins: beyond the myths
Bibliographic record
Abstract
ABSTRACT The S100 genes encode a conserved group of 21 vertebrate‐specific EF‐hand calcium‐binding proteins. Since their discovery in 1965, S100 proteins have remained enigmatic in terms of their cellular functions. In this review, we summarize the calcium‐ and zinc‐binding properties of the dimeric S100B and S100A1 proteins and highlight data that shed new light on the extracellular and intracellular regulation and functions of S100B. We point out that S100B and S100A1 homodimers are not functionally interchangeable and that in a S100A1/S100B heterodimer, S100A1 acts as a negative regulator for the ability of S100B to bind Zn2+. The Ca2+ and Zn2+‐dependent interactions of S100B with a wide array of proteins form the basis of its activities and have led to the derivation of some initial rules for S100B recognition of protein targets. However, recent findings have strongly suggested that these rules need to be revisited. Here, we describe a new consensus S100B binding motif present in intracellular and extracellular vertebrate‐specific proteins and propose a new model for stable interactions of S100B dimers with full‐length target proteins. A chaperone‐associated function for intracellular S100B in adaptive cellular stress responses is also discussed. This review may help guide future studies on the functions of S100 proteins in general.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".