How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach
Bibliographic record
Abstract
• A comprehensive and unique machine learning-based peptidomic approach. • Electrical current conditions modulate peptide separation and bioactivities. • 1 s/1s polarity reversal intensify the process despite peptides rapid reorientation. • 10 s/1s generates antibacterial recovery fraction and antifungal feed fraction. • The polarization concentration phenomena influence peptides’ selective migration. Industrial wastewaters are significant global concerns due to their environmental impact. Yet, protein-rich wastewaters can be valorized by enzymatic hydrolysis to release bioactive peptides. However, achieving selective molecular differentiation and eventually enhancing peptide bioactivities require costly cascades of membranes. In this study, a complex porcine cruor hydrolysate, containing 150 well-characterized peptides and demonstrating only an antifungal activity, was used as a model solution to evaluate the impact of current modes (continuous electrical current (CC), pulsed electric field (PEF) and polarity reversal (PR)) and the combination of pulse/pause-reversal pulse duration (10 s/1 s and 1 s/1 s) during peptides separation by an electromembrane process. The data analysis was assisted by a machine learning (ML)-based peptidomic approach to identify which of the 45 physicochemical characteristics of the peptides explain migration, or lack thereof, during electrodialysis with filtration membrane, a generic electromembrane process. The results demonstrated, for the first time, that electric current conditions modulate the population of recovered peptides and their associated fraction bioactivities. ML models identified the main features correlated to peptide migration, allowing tentative explanations of the underlying peptide selective migration phenomena. For CC-PEF 10 s/1 s–PR 10 s/1 s, isoelectric point (pI) (importance of 63.1%) and molecular weight (MW) (17.7%) were most important. For PEF 1 s/1 s, pI (53.9%), MW (23%) and GRAVY score (6.2%) played major roles. Finally, for PR 1 s/1 s, MW (82.5%), GRAVY score (5.5%) and tyrosine content (1.1%) were the key features. In addition, CC, PEF 10 s/1 s and PR 10 s/1 s allowed the production of two reusable fractions, an antibacterial recovery fraction and a feed fraction retaining antifungal activity, which aligns with the concept of circular economy.
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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 | 0.000 |
| 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 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".