Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening

Area of Science:

• Meat science
• Biochemistry
• Food technology

Background:

• Bioactive peptides from meat sources are gaining interest for enhancing meat quality and preservation.
• These peptides can be generated via various methods including enzymatic hydrolysis of by-products.
• Their applications are currently limited by matrix interactions, stability, and safety concerns.

Purpose of the Study:

• To review advances in meat-related peptidomics and computational screening for identifying bioactive peptides.
• To explore how computational approaches can prioritize peptides for specific functions like antioxidant and antimicrobial activity.
• To propose a framework for translating bioactive peptides from discovery to application in meat systems.

Main Methods:

• Synthesis of recent advances in meat-related peptidomics and computational screening.
• Inclusion of sequence-based prediction, machine learning, molecular docking, and molecular dynamics.
• Emphasis on stability assessment and safety-oriented filtering for meat applications.

Main Results:

• High-resolution peptidomics has identified numerous meat-derived peptide sequences.
• Computational methods can predict and prioritize peptides with desired functionalities (antioxidant, antimicrobial, flavor-modulating, preservation).
• A stage-gated framework is proposed for the translation of bioactive peptides.

Conclusions:

• Integrating peptide generation, identification, in silico prioritization, and meat quality endpoints is crucial.
• Further research is needed for matrix-specific validation, standardized reporting, and safety assessment.
• Bioactive peptides hold potential for improving meat quality, clean-label preservation, and circular economy in the meat industry.