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<p>Milk, a complex biological matrix, is a rich source of bioactive peptides with diverse biofunctional properties, including anticancer activity. This study aimed to investigate the potential of Artiodactyla milk-derived peptides as therapeutic agents for breast cancer using computational approaches. Amino acid sequences of major milk proteins from six Artiodactyla species namely cow (<i>Bos taurus</i>), water buffalo (<i>Bubalus bubalis</i>), goat (<i>Capra hircus</i>), yak (<i>Bos grunniens</i>), sheep (<i>Ovis aries</i>), and camel (<i>Camelus dromedarius</i>) extensively raised as cattle in the Indian subcontinent were subjected to <i>in-silico</i> gastrointestinal digestion simulating the actions of pepsin and trypsin. The resulting peptide pool was screened for anticancer potential using computational methods. Peptides exhibiting promising anti-cancer activity were further evaluated for intestinal stability, physicochemical properties, and three-dimensional structural characterization. The peptides having a predicted anti-cancer score of above 0.6 and predicted half-life values of above 1&#xa0;s were selected as potential candidates. Molecular docking simulations revealed favorable interactions between specific peptides and key breast cancer targets, Myeloid Cell Leukemia-1 (MCL-1) and Estrogen-Related Receptor Alpha (ERα). Notably, peptides P18 and P5 exhibited potent binding affinities of -8.1 and -7.4&#xa0;kcal/mol respectively towards the target proteins which were comparable to the reference inhibitors. These findings suggest that milk-derived peptides, particularly those of Artiodactyla origin, may serve as a promising class of anticancer therapeutics. The in-silico approach utilized in this study highlights the potential for investigating diverse natural sources to uncover bioactive peptides beyond oncology. This combined computational and experimental strategy accelerates drug discovery, enabling the identification of promising antimicrobial and anti-inflammatory compounds.</p> Graphical Abstract <p></p>

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Anticancer efficacy of artiodactyla milk peptides targeting MCL-1 and ERα receptors in breast cancer: insights from in-silico screening and molecular dynamics studies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月21日

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  • Piyush Pratik,
  • Riya Singh,
  • Mitun Chakraborty

摘要

Milk, a complex biological matrix, is a rich source of bioactive peptides with diverse biofunctional properties, including anticancer activity. This study aimed to investigate the potential of Artiodactyla milk-derived peptides as therapeutic agents for breast cancer using computational approaches. Amino acid sequences of major milk proteins from six Artiodactyla species namely cow (Bos taurus), water buffalo (Bubalus bubalis), goat (Capra hircus), yak (Bos grunniens), sheep (Ovis aries), and camel (Camelus dromedarius) extensively raised as cattle in the Indian subcontinent were subjected to in-silico gastrointestinal digestion simulating the actions of pepsin and trypsin. The resulting peptide pool was screened for anticancer potential using computational methods. Peptides exhibiting promising anti-cancer activity were further evaluated for intestinal stability, physicochemical properties, and three-dimensional structural characterization. The peptides having a predicted anti-cancer score of above 0.6 and predicted half-life values of above 1 s were selected as potential candidates. Molecular docking simulations revealed favorable interactions between specific peptides and key breast cancer targets, Myeloid Cell Leukemia-1 (MCL-1) and Estrogen-Related Receptor Alpha (ERα). Notably, peptides P18 and P5 exhibited potent binding affinities of -8.1 and -7.4 kcal/mol respectively towards the target proteins which were comparable to the reference inhibitors. These findings suggest that milk-derived peptides, particularly those of Artiodactyla origin, may serve as a promising class of anticancer therapeutics. The in-silico approach utilized in this study highlights the potential for investigating diverse natural sources to uncover bioactive peptides beyond oncology. This combined computational and experimental strategy accelerates drug discovery, enabling the identification of promising antimicrobial and anti-inflammatory compounds.

Graphical Abstract