Influence of physicochemical conditions on liquid-liquid phase separation and stability of immunoglobulin Y for storage and application
Bibliographic record
Abstract
Liquid-liquid phase separation (LLPS) is a biological process and can lead to the formation of irreversible aggregates of functional proteins upon storage and administration, making it essential to predict and mitigate this phenomenon. Immunoglobulin Y (IgY), a unique class of antibody derived from egg yolk has broad applications in disease diagnosis, prophylaxis, and treatment. In this study, we observed the formation of droplet-shaped condensates of IgY under crowding conditions with polyethylene glycol 8000 (PEG 8000). To assess the relative contribution of different IgY domains to LLPS, we prepared the fragment antigen binding (Fab), fragment crystallizable (Fc) 3-4, and Escherichia coli-expressed IgY-Fc 2-4 domain. After PEG 8000 addition, the Fab fragments more propensity to aggregate, while Fc 3-4 and E. coli-expressed Fc underwent LLPS. Furthermore, we found that LLPS of IgY is influenced by electrostatic interactions. Recognizing the negative effects of LLPS on antibody efficacy, our study showed that the addition of arginine and lysine at low concentrations could prevent PEG-induced LLPS, enhancing IgY stability. These findings provide valuable insights into the optimization of IgY antibody applications and storage conditions, advancing our understanding of antibody stability in solution and facilitating the development of strategies to protect antibodies from aggregation.
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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 | 0.000 | 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) | 0.000 | 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".