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Background <p>Breast cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the need for more selective and effective therapies. This study aimed to identify and validate novel anticancer peptides targeting Luminal A (MCF-7) and triple-negative (MDA-MB-231) breast cancer subtypes using an integrated in silico and in vitro approach.</p> Methods <p>Overexpressed proteins in each cell line were identified through literature mining, followed by the construction of protein–protein interaction (PPI) networks using STRING. Conserved motifs within PPI components were identified via multiple sequence alignment using the MEME Suite. Immunogenicity and anticancer potential of the selected motifs were predicted using VaxiJen and AntiCP, respectively. Peptide–target interactions were assessed through molecular docking (PatchDock/FireDock) and refined using molecular dynamics simulations in GROMACS. Based on these analyses, two lead peptides per subtype were selected, synthesized, and experimentally evaluated for cytotoxicity and subcellular localization.</p> Results <p>Two lead peptides were identified for each breast cancer subtype. Notably, the peptides RVCGDRGFFF and WYLKMMWQW exhibited strong membrane-associated interactions and significantly reduced the viability of MDA-MB-231 cells by approximately 75% and 90%, respectively. The combined computational and experimental approach enabled the identification of peptides with selective cytotoxic effects and favorable predicted immunogenic profiles.</p> Conclusion <p>These findings demonstrate that integrating computational screening with experimental validation is an effective strategy for accelerating the discovery of selective anticancer peptides. The identified candidates represent promising leads for the development of peptide-based therapeutics targeting specific breast cancer subtypes, with potential applications in oncology.</p>

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Integrated Computational and Experimental Discovery of Antitumoral Peptides Targeting Luminal A and Triple-Negative Breast Cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Isabella Fagundes Gurgel,
  • Ana Carolini Almeida Marcarini,
  • Carlos Marchioro Lacerda,
  • Aline de Souza,
  • Maria Alice de Oliveira,
  • Hugo Oliveira Faria,
  • Ananda Lima Sanson,
  • Ana Paula Moreira Barboza,
  • Saulo Fehelberg Pinto Braga,
  • Amanda de Vasconcelos Quaresma,
  • Vanessa Carla Furtado Mosqueira,
  • Karina Taciana Santos Rubio

摘要

Background

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the need for more selective and effective therapies. This study aimed to identify and validate novel anticancer peptides targeting Luminal A (MCF-7) and triple-negative (MDA-MB-231) breast cancer subtypes using an integrated in silico and in vitro approach.

Methods

Overexpressed proteins in each cell line were identified through literature mining, followed by the construction of protein–protein interaction (PPI) networks using STRING. Conserved motifs within PPI components were identified via multiple sequence alignment using the MEME Suite. Immunogenicity and anticancer potential of the selected motifs were predicted using VaxiJen and AntiCP, respectively. Peptide–target interactions were assessed through molecular docking (PatchDock/FireDock) and refined using molecular dynamics simulations in GROMACS. Based on these analyses, two lead peptides per subtype were selected, synthesized, and experimentally evaluated for cytotoxicity and subcellular localization.

Results

Two lead peptides were identified for each breast cancer subtype. Notably, the peptides RVCGDRGFFF and WYLKMMWQW exhibited strong membrane-associated interactions and significantly reduced the viability of MDA-MB-231 cells by approximately 75% and 90%, respectively. The combined computational and experimental approach enabled the identification of peptides with selective cytotoxic effects and favorable predicted immunogenic profiles.

Conclusion

These findings demonstrate that integrating computational screening with experimental validation is an effective strategy for accelerating the discovery of selective anticancer peptides. The identified candidates represent promising leads for the development of peptide-based therapeutics targeting specific breast cancer subtypes, with potential applications in oncology.