Rapid topology probing using fluorescence spectroscopy in planar lipid bilayer: the pore-forming mechanism of the toxin Cry1Aa of <i>Bacillus thuringiensis</i>
Bibliographic record
Abstract
Pore-forming toxins, many of which are pathogenic to humans, are highly dynamic proteins that adopt a different conformation in aqueous solution than in the lipid environment of the host membrane. Consequently, their crystal structures obtained in aqueous environment do not reflect the active conformation in the membrane, making it difficult to deduce the molecular determinants responsible for pore formation. To obtain structural information directly in the membrane, we introduce a fluorescence technique to probe the native topology of pore-forming toxins in planar lipid bilayers and follow their movement during pore formation. Using a Förster resonance energy transfer (FRET) approach between site-directedly labeled proteins and an absorbing compound (dipicrylamine) in the membrane, we simultaneously recorded the electrical current and fluorescence emission in horizontal planar lipid bilayers formed in plastic chips. With this system, we mapped the topology of the pore-forming domain of Cry1Aa, a biological pesticide from Bacillus thuringiensis, by determining the location of the loops between its seven α helices. We found that the majority of the toxins initially traverse from the cis to the trans leaflet of the membrane. Comparing the topologies of Cry1Aa in the active and inactive state in order to identify the pore-forming mechanism, we established that only the α3-α4 hairpin translocates through the membrane from the trans to the cis leaflet, whereas all other positions remained constant. As toxins are highly dynamic proteins, populations that differ in conformation might be present simultaneously. To test the presence of different populations, we designed double-FRET experiments, where a single donor interacts with two acceptors with very different kinetics (dipicrylamine and oxonol). Due to the nonlinear response of FRET and the dynamic change of the acceptor distribution, we can deduce the distribution of the acceptors in the membrane from the time course of the donor fluorescence. We found that Cry1Aa is present on both membrane leaflets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".