Spotlight on protein N-terminal acetylation
Bibliographic record
Abstract
N-terminal acetylation (Nt-acetylation) is a widespread protein modification among eukaryotes and prokaryotes alike. By appending an acetyl group to the N-terminal amino group, the charge, hydrophobicity, and size of the N-terminus is altered in an irreversible manner. This alteration has implications for the lifespan, folding characteristics and binding properties of the acetylated protein. The enzymatic machinery responsible for Nt-acetylation has been largely described, but significant knowledge gaps remain. In this review, we provide an overview of eukaryotic N-terminal acetyltransferases (NATs) and the impact of Nt-acetylation. We also discuss other functions of known NATs and outline methods for studying Nt-acetylation. A chemical modification of protein structure called N-terminal acetylation occurs normally in many circumstances, but is also implicated in diseases including cancers and developmental disorders. It adds an acetyl group, composed of a carbonyl group (C = O) linked to a methyl group (CH3), to the nitrogen atom found at one end of a protein chain. Thomas Arnesen at the University of Bergen in Norway and colleagues review the understanding of this modification and survey the enzymes that carry it out. Acetylation occurs on around 80% of human proteins and affects crucial aspects of their function. These include the stability that determines proteins’ lifetimes inside cells, the three-dimensional structures that proteins fold into, and the interactions between different proteins. Advances in analytical techniques are yielding new insights into the role of N-terminal acetylation in health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".