Punching Holes in Membranes: How Oligomeric Pore-Forming Proteins and Lipids Cooperate to Form Aqueous Channels in Membranes
Bibliographic record
Abstract
Many important biological processes are carried out by a small number of proteins working together as a team to accomplish a specific task. Cooperation between the different proteins is often accomplished through the formation of a supramolecular complex, comprised of either identical or different subunits. Although the formation of protein assemblies is a favored mechanism throughout the cell, it becomes especially important in lipid membranes, as evidenced by the numerous cellular events that are either triggered by or result in the formation of protein complexes in membranes. However, due to the difficulties associated with the study of membrane proteins, the formation of oligomers in lipid membranes is perhaps one of the least understood cellular processes. In this chapter we focus our attention on a subset of membrane complexes — namely, those formed by proteins that are able to pass from a water-soluble to a transmembrane form in order to create a water-filled channel through the lipid membrane. These pore-forming proteins (PFPs) are found in many organisms throughout different kingdoms of life, from bacteria to human. They are often involved in cell death mechanisms through their capacity to break membrane permeability barriers, which can lead to dissipation of the membrane potential as well as introduction or leakage of enzymatic proteins. In fact, a large subset of the PFPs are toxins, and referred to in the literature as pore-forming toxins (PFTs). The association of several monomers into an oligomer is almost always an important aspect of the modus operandi of these proteins. Oligomerization can be useful in several ways: it results in structures large enough to delineate nanometer-size water-filled channels in lipid bilayers, it ensures the presence of large hydrophobic surfaces that can support insertion in the membrane, and it permits cooperative formation and insertion mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".