Neuronal palmitoyl acyl transferases exhibit distinct substrate specificity
Bibliographic record
Abstract
Palmitoylation, a post-translational modification of cysteine residues with the lipid palmitate, has recently emerged as an important mechanism for regulating protein trafficking and function. With the identification of 23 DHHC mammalian palmitoyl acyl transferases (PATs), a key question was the nature of substrate-enzyme specificity for these PATs. Using the acyl-biotin exchange palmitoylation assay, we compared the substrate specificity of four neuronal PATs, namely DHHC-3, DHHC-8, HIP14L (DHHC-13), and HIP14 (DHHC-17). Exogenous expression of enzymes and substrates in COS cells reveals that HIP14L and HIP14 modulate huntingtin palmitoylation, DHHC-8 modulates paralemmin-1 palmitoylation, and DHHC-3 shows the least substrate specificity. These in vitro data were validated by lentiviral siRNA-mediated knockdown of endogenous HIP14 and DHHC-3 in cultured rat cortical neurons. PATs require the presence of palmitoylated cysteines in order to interact with their substrates. To understand the elements that influence enzyme/substrate specificity further, we fused the HIP14 ankryin repeat domain to the N terminus of DHHC-3, which is not a PAT for huntingtin. This modification enabled DHHC-3 to behave similarly to HIP14 by modulating palmitoylation and trafficking of huntingtin. Taken together, this study indicates that individual PATs have specific substrate preference, determined by regulatory domains outside the DHHC domain of the enzymes.
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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) | 0.000 | 0.000 |
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".