Protein thermal stability and phospholipid–protein interaction investigated by capillary isoelectric focusing with whole column imaging detection
Bibliographic record
Abstract
CIEF with whole column imaging detection (WCID) is an attractive technique for studying protein reaction and protein-ligand interaction due to its fast separation, simple operation, and high efficiency. In this study, two interesting applications by the CIEF-WCID were developed, involving the study of protein thermal stability and phospholipid-protein interaction. Four proteins (beta-lactoglobulin B, trypsin inhibitor, phosphorylase b, and trypsinogen) with different pI, and two types of phospholipids, including phosphatidylcholine (PC) and phosphatidylserine (PS), were used for this purpose. First, the altered CIEF profiles of four proteins were exhibited due to conformational changes resulting from protein denaturation induced by a high incubation temperature at 60 degrees C. It was demonstrated that the addition of a zwitterionic phospholipid (PC) played a crucial role in the thermal stability of targeted proteins, especially for trypsin inhibitor whose thermal stability was promoted with the addition of the PC vesicles at 60 degrees C. Second, the zwitterionic (PC) and acidic (PS) phospholipids displayed completely different interactions with the proteins. The addition of PS vesicles modified the zwitterionic phospholipids to carry negative charges, which correspondingly changed the interaction between the phospholipid and the protein. Our study demonstrates that the CIEF-WCID is a powerful approach to study protein reaction and protein-ligand interaction with high efficiency, high selectivity, and fast separation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".