A Combinatorial Approach toward Analyzing Functional Elements of the <i>Escherichia coli</i> Hemolysin Signal Sequence
Bibliographic record
Abstract
Secretion of hemolysin is directed by a signal sequence located within its C-terminal 60 amino acids. Deletion analyses have indicated that the extreme end of this C-terminus is critical for transport; however, it is not known if this region contains structural features necessary for function. In this study, we have used a combinatorial approach to generate two contiguous 8-residue random libraries (Cterm1 and Cterm2) in the signal sequence to investigate the functional specificity of the last 16 residues. The large number of variants generated had provided us with a rich data set to determine if a restricted subset of sequences was actually required for function in the extreme C-terminus. We observed that over 90% of the random sequences in the Cterm1 region were secreted at close to wild-type level, while the Cterm2 region was more restricted with only 50% of the random sequences supporting wild-type-like transport. It appeared that, in the Cterm2 region, the relative lack of positive charge is favored for function. These findings, along with previous results, allow us to propose a model for recognition and transport of hemolysin that emphasizes secondary structure and general biophysical properties over primary sequence. This model may have implications for understanding the broad substrate specificity common among ATP-binding cassette transporters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".