Background and Aims: Breast cancer remains a significant global health concern, necessitating the exploration of novel therapeutic strategies. Despite advancements in cancer therapeutics, effective treatments with minimal side effects remain elusive. Natural sources, such as camel milk, harbor bioactive compounds such as lactoferrin peptides, which hold promise as anticancer agents. This study investigated the potential of camel milk-derived lactoferrin peptides against breast cancer cells through a combined in silico and in vitro approach. By integrating computational modeling with experimental assays, we aimed to elucidate the anticancer mechanisms of these peptides and provide insights for their optimization as anticancer therapeutics.Methods: In silico analysis involving pepetid design, and validation, then molecular docking and molecular dynamics (MD) simulations was used to explore peptide-protein interactions and stability. Peptides were synthesized and tested for anticancer activity using MTT assays on MCF-7 cells, with HDFa normal cells used as controls.Results: Results of this study showed that camel milk-derived lactoferrin peptides, particularly PEP66, exhibited strong anticancer activity against MCF-7 breast cancer cells, with the lowest IC50 value (52.82 μg/mL) compared to other peptides. In silico molecular docking and dynamics simulations revealed that PEP66 formed stable interactions with key residues in the HER2 catalytic site, indicating its potential as an effective anticancer agent. The selectivity index (SI) of PEP66 (3.19) also suggested lower toxicity to normal cells compared to cancer cells, reinforcing its therapeutic potential. Hydrogen bonding analysis highlighted key residues involved in stabilizing peptide-protein complexes, while molecular dynamics simulations demonstrated the stability of these interactions over time. Notably, PEP66 exhibited the highest stability and formed significant interactions with essential residues in the HER2 catalytic site, suggesting its potential as an effective anticancer agent.Conclusion: Camel milk-derived lactoferrin peptides show promise as anticancer agents against breast cancer cells. The multidisciplinary approach employed in this study provides valuable insights into the mechanisms underlying their activity, paving the way for rational design strategies to enhance their efficacy. Further experimental validation is warranted to validate the anticancer potential of these peptides and advance their development as novel therapeutic agents for breast cancer treatment.

Background/Objectives: Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation and unfavorable prognosis. Targeting HER2 has thus emerged as a crucial therapeutic strategy, particularly for HER2-positive malignancies. The present study focusses on the design and optimization of peptide inhibitors targeting HER2, utilizing machine learning to identify and enhance peptide candidates with elevated binding affinities. The aim is to provide novel therapeutic options for malignancies linked to HER2 overexpression. Methods: This study started with the extraction and structural examination of the HER2 protein, succeeded by designing the peptide sequences derived from essential interaction residues. A machine learning technique (XGBRegressor model) was employed to predict binding affinities, identifying the top 20 peptide possibilities. The candidates underwent further screening via the FreeSASA methodology and binding free energy calculations, resulting in the selection of four primary candidates (pep-17, pep-7, pep-2, and pep-15). Density functional theory (DFT) calculations were utilized to evaluate molecular and reactivity characteristics, while molecular dynamics simulations were performed to investigate inhibitory mechanisms and selectivity effects. Advanced computational methods, such as QM/MM simulations, offered more understanding of peptide–protein interactions. Results: Among the four principal peptides, pep-7 exhibited the most elevated DFT values (−3386.93 kcal/mol) and the maximum dipole moment (10,761.58 Debye), whereas pep-17 had the lowest DFT value (−5788.49 kcal/mol) and the minimal dipole moment (2654.25 Debye). Molecular dynamics simulations indicated that pep-7 had a steady binding free energy of −12.88 kcal/mol and consistently bound inside the HER2 pocket during a 300 ns simulation. The QM/MM simulations showed that the overall total energy of the system, which combines both QM and MM contributions, remained around −79,000 ± 400 kcal/mol, suggesting that the entire protein–peptide complex was in a stable state, with pep-7 maintaining a strong, well-integrated binding. Conclusions: Pep-7 emerged as the most promising therapeutic peptide, displaying strong binding stability, favorable binding free energy, and molecular stability in HER2-overexpressing cancer models. These findings suggest pep-7 as a viable therapeutic candidate for HER2-positive cancers, offering a potential novel treatment strategy against HER2-driven malignancies.

The zoonotic potential of bat coronaviruses, especially HKU5, is a significant issue because of their capacity to utilize human angiotensin-converting enzyme 2 (ACE2) as a receptor for cellular entry. This study offers structural insights into the binding kinetics of HKU5 (Bat Merbecovirus HKU5) receptor-binding domain (RBD) spike protein with human ACE2 through a multiscale computational method. This study employed structural modeling, 300-nanosecond (ns) molecular dynamics (MD) simulations, alanine-scanning mutagenesis, and computational peptide design to investigate ACE2 recognition by the HKU5 RBD and its interactions with peptides. The root mean square deviation (RMSD) investigation of HKU5–ACE2 complexes indicated that HKU5 exhibited greater flexibility than SARS-CoV-2, with RMSD values reaching a maximum of 1.2 nm. Free energy analysis, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA), indicated a more robust binding affinity of HKU5 to ACE2 (ΔG_Total = −21.61 kcal/mol) in contrast to SARS-CoV-2 (ΔG_Total = −5.82 kcal/mol), implying that HKU5 binding with ACE2 had higher efficiency. Additionally, a peptide was designed from the ACE2 interface, resulting in the development of 380 single-site mutants by mutational alterations. The four most promising mutant peptides were selected for 300-nanosecond (ns) MD simulations, subsequently undergoing quantum chemical calculations (DFT) to evaluate their electronic characteristics. MM/GBSA of −37.83 kcal/mol indicated that mutant-1 exhibits the most favorable binding with HKU5, hence potentially inhibiting ACE2 interaction. Mutant-1 formed hydrogen bonds involving Glu74, Ser202, Ser204, and Asn152 residues of HKU5. Finally, QM/MM calculations on the peptide–HKU5 complexes showed the most favorable ΔE_interaction of −170.47 (Hartree) for mutant-1 peptide. These findings offer a thorough comprehension of receptor-binding dynamics and are crucial for evaluating the zoonotic risk associated with HKU5-CoV and guiding the design of receptor-targeted antiviral treatments.