Overexpression and Characterization of the C-Terminal Domain of Human SIVA1: A Proapoptotic Factor and Cytoskeleton Binding Protein
Bibliographic record
Abstract
Siva1 protein interacts with tumor protein p53 and with the member of the tumor necrosis factor receptor superfamily, stathmin, among others. These proteins are related to several pathways involved in cancer and are therefore strong candidate targets for drug design. This study aimed to characterize the biophysical properties of Siva 1 C- terminal domain to contribute to the discovery of new target directed drugs. Siva1 protein interacts with tumor protein p53 and with the member of the tumor necrosis factor receptor superfamily, stathmin, among others. These proteins are related to several pathways involved in cancer and are therefore strong candidate targets for drug design. This study aimed to characterize the biophysical properties of Siva 1 C- terminal domain to contribute to the discovery of new target directed drugs. The C-terminus Siva1 domain (residues 84-175) was fused to glutathione Stransferase (GST) and expressed in an E coli system and the recombinant GST-Siva C-terminus was purified by GSTTagged Protein affinity and gel filtration chromatography. We tested the biological activity of the purified Siva Cterminus domain in a Jurkat extract cell line and found that the protein interacted with natural binders. Biophysical and biochemical assays have demonstrated monodispersion of the protein in solution with a predominant unfolded and elongated shape. However, at high concentrations, the protein showed a tendency to form soluble aggregates. These results are expected to lead to further progress in the understanding of Siva1 properties and target-directed drug design.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".