Engineered viruses to select genes encoding secreted and membrane-bound proteins in mammalian cells
Bibliographic record
Abstract
We have developed a functional genomics tool to identify the subset of cDNAs encoding secreted and membrane-bound proteins within a library (the 'secretome'). A Sindbis virus replicon was engineered such that the envelope protein precursor no longer enters the secretory pathway. cDNA fragments were fused to the mutant precursor and expression screened for their ability to restore membrane localization of envelope proteins. In this way, recombinant replicons were released within infectious viral particles only if the cDNA fragment they contain encodes a secretory signal. By using engineered viral replicons to selectively export cDNAs of interest in the culture medium, the methodology reported here efficiently filters genetic information in mammalian cells without the need to select individual clones. This adaptation of the 'signal trap' strategy is highly sensitive (1/200 000) and efficient. Indeed, of the 2546 inserts that were retrieved after screening various libraries, more than 97% contained a putative signal peptide. These 2473 clones encoded 419 unique cDNAs, of which 77% were previously annotated. Of the 94 cDNAs encoding proteins of unknown function, 24% either had no match in databases or contained a secretory signal that could not be predicted from electronic data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.008 | 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; both teacher heads agree on what is shown here.
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".