Biological implications of SNPs in signal peptide domains of human proteins
Bibliographic record
Abstract
Proteins destined for secretion or membrane compartments possess signal peptides for insertion into the membrane. The signal peptide is therefore critical for localization and function of cell surface receptors and ligands that mediate cell-cell communication. About 4% of all human proteins listed in UniProt database have signal peptide domains in their N terminals. A comprehensive literature survey was performed to retrieve functional and disease associated genetic variants in the signal peptide domains of human proteins. In 21 human proteins we have identified 26 disease associated mutations within their signal peptide domains, 14 mutations of which have been experimentally shown to impair the signal peptide function and thus influence protein transportation. We took advantage of SignalP 3.0 predictions to characterize the signal peptide prediction score differences between the mutant and the wild-type alleles of each mutation, as well as 189 previously uncharacterized single nucleotide polymorphisms (SNPs) found to be located in the signal peptide domains of 165 human proteins. Comparisons of signal peptide prediction outcomes of mutations and SNPs, have implicated SNPs potentially impacting the signal peptide function, and thus the cellular localization of the human proteins. The majority of the top candidate proteins represented membrane and secreted proteins that are associated with molecular transport, cell signaling and cell to cell interaction processes of the cell. This is the first study that systematically characterizes genetic variation occurring in the signal peptides of all human proteins. This study represents a useful strategy for prioritization of SNPs occurring within the signal peptide domains of human proteins. Functional evaluation of candidates identified herein may reveal effects on major cellular processes including immune cell function, cell recognition and adhesion, and signal transduction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".