A subset of calcium‐binding S100 proteins show preferential heterodimerization
Bibliographic record
Abstract
The assembly of proteins into dimers and oligomers is a necessary step for the proper function of transcription factors, muscle proteins, and proteases. In uncontrolled states, oligomerization can also contribute to illnesses such as Alzheimer's disease. The S100 protein family is a group of dimeric proteins that have important roles in enzyme regulation, cell membrane repair, and cell growth. Most S100 proteins have been examined in their homodimeric state, yet some of these important proteins are found in similar tissues implying that heterodimeric molecules can also be formed from the combination of two different S100 members. In this work, we have established co-expression methods in order to identify and quantify the distribution of homo- and heterodimers for four specific pairs of S100 proteins in their calcium-free states. The split GFP trap methodology was used in combination with other GFP variants to simultaneously quantify homo- and heterodimeric S100 proteins in vitro and in living cells. For the specific S100 proteins examined, NMR, mass spectrometry, and GFP trap experiments consistently show that S100A1:S100B, S100A1:S100P, and S100A11:S100B heterodimers are the predominant species formed compared to their corresponding homodimers. We expect the tools developed here will help establish the roles of S100 heterodimeric proteins and identify how heterodimerization might alter the specificity for S100 protein action in cells.
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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) | 0.003 | 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".