In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy.
Simple Summary: Human epidermal growth factor receptor 2, a protein found on cell surfaces, is often overproduced in aggressive forms of cancers like breast, gastric, ovarian, and lung cancers. This overproduction can lead to rapid tumor growth and poor patient outcomes. Current treatments target th...
| Publicado en: | Cancers Vol. 16; no. 23; pp. 3979 - 4005 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181660978&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181660978 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Dec2024 vid: 16 iid: 23 pid: 97109 pub: MDPI artinfo: ui: 181660978 181660978 181660978 10.3390/cancers16233979 181660978 ppf: 3979 ppct: 26 formats: tig: atl: In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy. aug: au: Alkhatabi, Heba Ahmed Alatyb, Hisham N. affil: Faculty of Applied Medical Science, King Abdulaziz University, Jeddah 22254, Saudi Arabia sug: subj: Lung Neoplasms Drug Therapy Drug Design Intracellular Signaling Peptides and Proteins Antagonists and Inhibitors Peptides Pharmacodynamics Epidermal Growth Factors Drug Effects Gene Expression Machine Learning Molecular Docking Simulation Computer Simulation Human Funding Source Models, Anatomic Molecular Structure Nuclear Physics Peptides Analysis Proteins Metabolism Signal Transduction Peptides Metabolism ab: Simple Summary: Human epidermal growth factor receptor 2, a protein found on cell surfaces, is often overproduced in aggressive forms of cancers like breast, gastric, ovarian, and lung cancers. This overproduction can lead to rapid tumor growth and poor patient outcomes. Current treatments target this protein to slow cancer progression, but more effective and precise options are needed. This study explores a new approach by designing small protein-like molecules called peptides to specifically block this receptor. Using advanced computer-based methods, researchers identified and optimized several peptides, selecting the ones most likely to bind effectively to this protein. Through further testing, one peptide showed promise as a potential therapy due to its stable binding and favorable properties. This research could lead to new, targeted treatment options for patients with these types of cancers and contribute to ongoing efforts to improve cancer therapies through precision medicine. 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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