Structural Analysis of Human Serum Albumin Complexes with Cationic Lipids
Bibliographic record
Abstract
Human serum albumin (HSA) is a major transporter for delivering several endogenous compounds including fatty acids in vivo. Even though HSA is the primary target of fatty acid binding, the effects of cationic lipid on protein stability and conformation have not been investigated. The aim of this study was to examine the interaction of human serum albumin (HSA) with helper lipids--cholesterol (Chol) and dioleoylphosphatidylethanolamine (DOPE)--and with cationic lipids--dioctadecyldimethylammonium bromide (DDAB) and 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), at physiological conditions, using constant protein concentration and various lipid contents. Fourier transform infrared (FTIR), circular dichroism (CD), and fluorescence spectroscopic methods were used to analyze the lipid binding mode, the binding constant, and the effects of lipid interaction on HSA stability and conformation. Structural analysis showed that cholesterol and DOPE (helper lipids) interact mainly with HSA polypeptide polar groups and via hydrophobic moieties. Hydrophobic interactions dominate the binding of cationic lipids to HSA. The number of bound lipids (n) calculated was 1.22 (cholesterol), 1.82 (DDAB), 1.76 (DOPE), and 1.56 (DOTAP). The overall binding constants estimated were KChol=2.3 (+/-0.50)x10(3) M(-1), KDDAB=8.9 (+/-0.95)x10(3) M(-1), KDOTAP=9.1 (+/-0.90)x10(3) M(-1), and KDOPE=4.7 (+/-0.70)x10(3) M(-1). HSA conformation was stabilized by cholesterol and DOPE with a slight increase of protein alpha-helical structures, while DOTAP and DDAB induced an important (alpha-->beta) transition, suggesting a partial protein unfolding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".