Bioinformatic design of optimized therapeutic peptides for targeted breast cancer therapy

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Abstract Breast cancer remains a leading cause of mortality among women globally, underscoring the urgent need for novel therapies that combine high efficacy with minimal adverse effects. This study examines the bioinformatic design of animal-derived therapeutic peptides targeting two key breast cancer biomarkers: matrix metalloproteinase 1 (MMP1) and epidermal growth factor receptor (EGFR). Approximately 1,500 antimicrobial peptides were retrieved from the antimicrobial peptide database 3 (APD3). They were screened for hemolytic properties using the Hemolytik database. This screening resulted in 255 non-hemolytic candidates, which were further screened through VaxiJen 2.0 and ToxinPred2 for antigenicity and allergenicity. Moreover, physicochemical properties were analyzed using the Expasy ProtParam server. We applied thresholds of molecular weight (1.7–3.1 kDa), instability index (≤ 28), and net charge (+ 2 to + 7). Five peptides met all criteria and were screened for anticancer potential using AntiCP 2.0 and ACPred, identifying three overlapping anticancer peptides (ACPs), including Metalnikowin IIA (AP00363) from Palomena prasina, Guentherin (AP00584) from Hylarana guentheri, and P-10 (AP02393) from Ciona intestinalis. Furthermore, peptide structures were modeled using Colab AlphaFold2. Tertiary structures of target proteins were obtained from UniProt, and Buserelin (FDA-approved ACP) was obtained from the DCTPep database. All peptides were docked against MMP1 and EGFR using HDock and ClusPro. AP00363 showed the highest docking affinity with MMP1 and EGFR, scoring −235.61 and −271.70, respectively. Molecular dynamics simulations supported the stability and interaction potential of these peptides. Overall, this study presents promising bioinformatically designed, animal-derived ACPs for targeted breast cancer therapy. Further in vitro and in vivo validation is necessary to assess clinical relevance.