Production Technology and Functionality of Bioactive Peptides
Bibliographic record
Abstract
Bioactive peptides are specific protein fragments that prove health-promoting potential for humans. The bioactivities include antimicrobial, antioxidant, anticancer, immunomodulatory activities, etc. Hence, bioactive peptides' production technology and processes have attracted excessive attention, especially concerning peptides' synthesis, separation, identification, and functionality. This review summarizes the relevant investigations from the above four aspects. Among the production technology of bioactive peptides, biosynthesis, chemosynthesis, technology for separation and purification, and the interactions responsible for peptide-based nanostructures are emphasized. Here, the biosynthesis of peptides includes enzymatic hydrolysis, microbial fermentation, and recombinant DNA technology, and chemosynthesis consists of solution-phase peptide synthesis and solid-phase peptide synthesis (SPPS). The commonly used enzymes in enzymatic hydrolysis are investigated, including pepsin, trypsin, and alcalase. The commonly used microorganisms, typical processes, protein sources, and advantages of microbial fermentation are analyzed. Membrane separation (including ultrafiltration and nanofiltration), chromatography technology (including ion-exchange chromatography, gel filtration chromatography, affinity chromatography, and reverse-phase high-performance liquid chromatography (RP-HPLC)), and electrophoresis technology are detailed for the purification technology. Mass spectrometry (MS), its combination with the high-performance separation method, and nuclear magnetic resonance (NMR) are elucidated for the identification technology. The non-covalent interactions responsible for peptide-based nanostructures involve electrostatic force, hydrogen bonds, π-π stacking, hydrophobic interaction, and van der Waals force. Afterward, we detail the peptides' antihypertensive, antithrombotic, anticancer, antimicrobial, antioxidant, and immunomodulatory activities. The activity analysis mainly involves peptides' sources, structural features, mechanisms of action, and influencing factors. Based on the production and functionality elucidation, potential challenges for peptide application in biomedicine are given. The challenge is analyzed from the aspects of purification and identification technologies and influencing factors of peptides' bioactivities. Our work will elaborate on advances in the production technology of peptides and their bioactivities, which could promote and expand their industrial applications.
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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) | 0.001 | 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 | 0.001 |
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".