High-Throughput Screening of Protein Surface Activity via Flow Injection Analysis-pH Gradient-Dynamic Surface Tension Detection
Bibliographic record
Abstract
Using flow injection analysis (FIA), a pH gradient is blended in real time with a protein sample as the pH-dependent protein surface activity is measured by a dynamic surface tension detector (FIA-pH-DSTD). This instrumental system was developed as a high-throughput method for the screening of protein surface activity at the air/liquid interface as a function of pH. This method utilizes the continuous flow, drop-based dynamic surface tension detector in combination with flow injection sample introduction and blending of a steady-state concentration of protein sample with a pH gradient ranging from pH 2.0 to pH 11.5. Dynamic surface tension is measured through the differential pressure across the air/liquid interface of repeatedly growing and detaching drops. Continuous surface tension measurement is achieved for each eluting drop of 2-s length (2 muL), providing insight into both the kinetic and thermodynamic behaviors of molecular orientation processes at the liquid/air interface. Three-dimensional data are obtained, with surface tension first converted to surface pressure, which is collected as a function of elution time versus drop time. In FIA-pH-DSTD, a commercial pH probe is used to measure pH during elution time, enabling surface pressure throughout drop time to be subsequently plotted as a function of eluting pH. An automated DSTD calibration procedure and data analysis method is applied, which allows simultaneous use of two different solvents, permitting real-time dynamic surface tension data to be obtained. The method was applied to the analysis of 14 commercial purified proteins, yielding characteristic features of surface activity as a function of pH. The reproducibility of the measurement and selectivity advantage of the DSTD was shown for the analysis of serum albumins from various mammalian sources. Several applications were also suggested and discussed in order to show the potential of the method for protein and food chemistry studies and in the study of protein-polymer interactions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".