Improved Helix and Kink Characterization in Membrane Proteins Allows Evaluation of Kink Sequence Predictors
Bibliographic record
Abstract
Although the α-helical secondary structure of proteins is well-defined, the exact causes and structures of helical kinks are not. This is especially important for transmembrane (TM) helices of integral membrane proteins, many of which contain kinks providing functional diversity despite predominantly helical structure. We have developed a Monte Carlo method based algorithm, MC-HELAN, to determine helical axes alongside positions and angles of helical kinks. Analysis of all nonredundant high-resolution α-helical membrane protein structures (842 TM helices from 205 polypeptide chains) revealed kinks in 64% of TM helices, demonstrating that a significantly greater proportion of TM helices are kinked than those indicated by previous analyses. The residue proline is over-represented by a factor >5 if it is two or three residues C-terminal to a bend. Prolines also cause kinks with larger kink angles than other residues. However, only 33% of TM kinks are in proximity to a proline. Machine learning techniques were used to test for sequence-based predictors of kinks. Although kinks are somewhat predicted by sequence, kink formation appears to be driven predominantly by other factors. This study provides an improved view of the prevalence and architecture of kinks in helical membrane proteins and highlights the fundamental inaccuracy of the typical topological depiction of helical membrane proteins as series of ideal helices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".