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<p>The discovery of bioactive peptides (BAPs) from food proteins is pivotal for developing functional ingredients that promote health beyond basic nutrition. However, traditional discovery approaches are slow, expensive, and inefficient, creating a bottleneck for innovation. This review aims to critically examine the paradigm shift towards an integrated workflow that combines in silico prediction with high-throughput screening (HTS) to accelerate and rationalize BAP discovery. We dissect the computational tools, including bioinformatic databases like BIOPEP-UWM, QSAR modelling, and molecular docking, that enable predictive mining of peptide bioactivity from protein sequences. Biochemical assays for antioxidant, antihypertensive, antidiabetic, and antimicrobial activities, adapted into HTS formats for rapid empirical validation, are detailed. By analysing recent case studies from dairy, plant, and marine sources, this synergistic pipeline is demonstrated to significantly accelerate the identification of promising BAPs. However, our analysis also reveals critical challenges: a persistent prediction–validation gap, biases and limitations in existing databases, a lack of assay standardization, and the major hurdle of peptide bioavailability. To address these challenges, we highlight emerging solutions, including next-generation artificial intelligence (AI) for <i>de novo</i> peptide design, initiatives to improve data quality and standardization, and strategies to assess and enhance peptide stability and absorption. Bridging the gap between in vitro findings and in vivo efficacy through advanced bioavailability models and human trials is necessary. By providing a comprehensive synthesis of the integrated peptidomics workflow, we lay a foundation for more efficient, data-driven discovery of functional peptide ingredients, ultimately contributing to the development of sustainable, evidence-based functional foods.</p>

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Emerging food peptidomics: integrating in silico discovery with high-throughput screening for the next generation of functional ingredients

  • 期刊论文

  • 发表日期：

    2026年1月18日

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  • Thanh-Do Le,
  • My Khanh Tran Thi Ha,
  • Tolulope Joshua Ashaolu

摘要

The discovery of bioactive peptides (BAPs) from food proteins is pivotal for developing functional ingredients that promote health beyond basic nutrition. However, traditional discovery approaches are slow, expensive, and inefficient, creating a bottleneck for innovation. This review aims to critically examine the paradigm shift towards an integrated workflow that combines in silico prediction with high-throughput screening (HTS) to accelerate and rationalize BAP discovery. We dissect the computational tools, including bioinformatic databases like BIOPEP-UWM, QSAR modelling, and molecular docking, that enable predictive mining of peptide bioactivity from protein sequences. Biochemical assays for antioxidant, antihypertensive, antidiabetic, and antimicrobial activities, adapted into HTS formats for rapid empirical validation, are detailed. By analysing recent case studies from dairy, plant, and marine sources, this synergistic pipeline is demonstrated to significantly accelerate the identification of promising BAPs. However, our analysis also reveals critical challenges: a persistent prediction–validation gap, biases and limitations in existing databases, a lack of assay standardization, and the major hurdle of peptide bioavailability. To address these challenges, we highlight emerging solutions, including next-generation artificial intelligence (AI) for de novo peptide design, initiatives to improve data quality and standardization, and strategies to assess and enhance peptide stability and absorption. Bridging the gap between in vitro findings and in vivo efficacy through advanced bioavailability models and human trials is necessary. By providing a comprehensive synthesis of the integrated peptidomics workflow, we lay a foundation for more efficient, data-driven discovery of functional peptide ingredients, ultimately contributing to the development of sustainable, evidence-based functional foods.