Human Non-synonymous Single Nucleotide Polymorphisms Can Influence Ubiquitin-mediated Protein Degradation
Bibliographic record
Abstract
Ubiquitin-mediated proteolysis plays a critical role in the degradation of proteins important in the cellular processes, such as cell cycle/division, differentiation and development, DNA repair, transcriptional regulation, and signaling. It is carried out by a complex cascade of enzymes that contain a high degree of specificity to motifs found in many proteins with rapid turnover. For example, the PEST motifs are hydrophilic stretches of amino acids that serve as signals for proteolytic degradation. In this study, we propose that amino acid altering non-synonymous single nucleotide polymorphisms (nsSNP) result in the abolishment or creation of putative PEST motifs, and thus lead to abnormal stabilization or degradation of the proteins. Using a web-based algorithm, PESTFind, we analyzed a total of 253 nsSNPs from proteins involved in cell cycle (n = 24), DNA repair (n = 128), and TGFbeta signaling pathway (n = 101). Fifteen nsSNPs were located within putative PEST sequences, and 9/15 (60%) either created or abolished these PEST motifs. PEST motifs were abolished in the presence of nsSNPs in CCND3, PMS2, POLE4, SITPEC, and PPARG and putative PEST motifs were created in NEIL2, BIRC4, MLL2, and PPP1R15A. Although experimental analyses are required to confirm these results, they suggest that nsSNPs can induce changes in ubiquitin-mediated protein degradation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".