How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach

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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Publication Details

Journal
Food Research International
Published
2024-11-28
DOI
https://doi.org/10.1016/j.foodres.2024.115417
Citations
9
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
2.10

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article

How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach

Ismaı̈l Fliss, Pier-Luc Plante, Aurore Cournoyer, Laurent Bazinet et al.
9 citations
Food Research International
Machine Learning in Bioinformatics
2.10
article

How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach

Ismaı̈l Fliss, Pier-Luc Plante, Aurore Cournoyer, Laurent Bazinet, Jean-Pierre Clément, Mathieu Bazinet
article en
9 citations

Abstract

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.

Food Research InternationalVol. 200
Université Laval (CA)
Université Laval, Natural Sciences and Engineering Research Council of Canada
Reduced inequalities
Openalex Percentile: Top 12%
Machine Learning in Bioinformatics
2.10
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