Unstructured Conformations Are a Substrate Requirement for the Sir2 Family of NAD-dependent Protein Deacetylases
Bibliographic record
Abstract
The regulation of protein function is often achieved through post-translational modifications including phosphorylation, methylation, ubiquitination, and acetylation. The role of acetylation has been most extensively studied in the context of histones, but it is becoming increasingly evident that this modification now includes other proteins. The Sir2 family of NAD-dependent deacetylases was initially recognized as mediating gene silencing through histone deacetylation, but several family members display non-nuclear sub-cellular localization and deacetylate non-histone protein substrates. Although many structural and enzymatic studies of Sir2 proteins have been reported, how substrate recognition is achieved by this family of enzymes is unknown. Here we use in vitro deacetylase assays and a variety of potential substrates to examine the substrate specificity of yeast homologue Hst2. We show that Hst2 is specific for acetyl-lysine within proteins; it does not deacetylate small polycations such as acetyl-spermine or acetylated amino ter-mini of proteins. Furthermore we have found that Hst2 displays conformational rather than sequence specificity, preferentially deacetylating acetyl-lysine within unstructured regions of proteins. Our results suggest that this conformational requirement may be a general feature for substrate recognition in the Sir2 family.
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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) | 0.001 | 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".