1C1400 New colorimetric sandwich assay for detection of pathogens by using antimicrobial peptides as detection probes(Proteins: Measurement, Analysis, Engineering,Oral Presentation,The 50th Annual Meeting of the Biophysical Society of Japan)
Bibliographic record
Abstract
TheBiophysicalSociety of Japan General IncorporatedAssociation Protein folding is often hampeTed by protein aggregation, which can be prevented by a variety of chaperones in the cell.A dataset that evaluates which chaperones are effective for aggregation-prone proteins would provide an invaluable resource not on]y for understanding the roles ofchaperones, but also for broader applications in protein science and engineering.Previous]y, we conducted comprehensive aggregation analysis of rnore than 3,OOO Escheriehia co]i pToteins by using a reconstituted cell-free translation system (PURE system).which does not contain any chaperones (Niwa et al,, PNAS.2009).A histogram of the so]ubilities revealed a c]ear bimodal distribution, implying that the many aggregation-prone proteins require ehaperones to fotd corrcctly.Then, we cemprehensively evaluuted the effects of the majoT E. coli chaperones, trigger factor, DnaKVDnalIGrpE, and GroELIGroES, on --80e aggregation-prone cytosotic E. coli proteins, using the PURE system.Statistical analyses revealed the robustness and the intriguing properties ofchaperones.The DnaK and GroEL systerns drastically increased the so]ubilities of hundreds of proteins with weak biases, whereas trigger factor had only a marginal effect on solubility.The combined addition of the chaperones was effective for a subset ofproteins that were not rescued by any single chaperone system.supponing the synergistic effect ofthese
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".