Application of machine learning tools to study the synergistic impact of physicochemical properties of peptides and filtration membranes on peptide migration during electrodialysis with filtration membranes
Bibliographic record
Abstract
The present study aimed to explore the application of machine learning-based tools to study the impact of physicochemical properties of peptides and filtration membranes (FM) on peptide migration during electrodialysis with filtration membranes (EDFM). A total of 14 membranes characterized in terms of 10 physicochemical properties were employed to evaluate the selective migration of cationic and anionic peptides from a tryptic hydrolysate of whey protein isolate well characterized by chemometric and bioinformatic methods. Two machine learning approaches were compared: Decision Tree (DT) and Binary Greedy Network (BGN). Based on the feature selection and model performance results, DT appeared to be the most appropriate approach to generate explanatory models of peptide migration. From the selected DT models, selective migration patterns associated with specific physicochemical properties of peptides and FMs were established for the first time. The migration of anionic peptides was positively affected by higher average peak-to-valley roughness (Rz) values and macropores distribution in the filtrating layer (Mp-FL), where S11N+ and PES300 were the FMs providing the highest recovery of this type of peptides, particularly for those with isoelectric point (pI) values lower than 4.407 (IDALNENK, KYLLFCMENSAEPEQSLACQCLVRTPEVD, SLAMAASDISLLDAQSAPL, SLAMAASDISLLDAQSAPLR, TDYKKYLLFCMENSAEPEQ, TPEVDDEALEK, TPEVDDEALEKFDK, VLVLDTDYK, and VYVEELKPTPEGDLEILLQK) which reached average recovery rates of 16.617 and 4.556 %, respectively. Regarding the migration of cationic peptides, this was mainly affected by other membrane characteristics such as volumetric porosity (Vp) and zeta potential (ZP), as well as peptide parameters such as polar residue content, pI, and leucine content. S11, S11SO3-, PES100, and PES300 were the FMs with the highest selective recovery rate for 7 out of 11 cationic peptides (ALPHMIR, IPAVFK, PMHI, PMHIR, TKIPAVF, TKIPAVFK, and VGINYWLAHK) with average values between 2.149 to 4.328 %. This novel DT-based approach represents a suitable tool for studying peptide migration, choosing the most appropriate FMs for selective bioactive peptide migration during EDFM, and understanding the phenomena involved in their migration process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".