Unveiling the intricacies of protein-protein interactions and membrane fouling: Exploring hetero-protein complex formation in binary mixtures
Bibliographic record
Abstract
In practical applications, protein fouling studies often face limitations due to their reliance on single-protein feed experiments. It is crucial to acknowledge that interprotein interactions can significantly differ from intraprotein interactions, leading to variations in adsorption and membrane fouling behaviors. In this review, we delve into the dynamics of adsorption and membrane fouling, with a specific focus on single and binary solutions of Bovine Serum Albumin (BSA) and Lysozyme (LYZ) at or near physiological pH. These two proteins differ in terms of size, charge, and conformational stability, allowing for comparisons between small and large proteins, positively and negatively charged proteins, as well as rigid and flexible proteins. To gain further insights, we compare the findings from LYZ in single and binary solutions with those of alpha lactalbumin (α-LA), which, despite having opposite charges, shares a similar size with LYZ. The formation of BSA-LYZ heteroprotein complexes may introduce unique fouling trends in binary solutions compared to single solutions. This interplay can either enhance, reduce, or leave fouling unaffected. While studies employing the Extended DLVO (Derjaguin, Landau, Vervey, and Overbeek) theory to predict fouling in protein mixtures are limited, preliminary investigations using DLVO show promise. This approach has the potential to extend to binary and multi-protein feeds, providing valuable insights into the dynamics of fouling behavior in complex protein solutions. Considering that BSA is often used as a surrogate for Human Serum Albumin (HSA), the findings of this endeavor hold particular significance. HSA ranks the most abundant plasma proteins and, therefore, represents a crucial subject in numerous protein-related studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".