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In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy
Heba Ahmed Alkhatabi1,2,3, Hisham N Alatyb3,4
1Faculty of Applied Medical Science, King Abdulaziz University, Jeddah 22254, Saudi Arabia.
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.
Insights
This study developed a novel peptide inhibitor, pep-7, targeting the Human Epidermal Growth Factor Receptor 2 (HER2) for cancer therapy. Pep-7 demonstrates strong binding stability and favorable energetics, showing promise for HER2-positive malignancies.
Area of Science:
- Computational chemistry and bioinformatics
- Drug discovery and development
- Molecular modeling and simulation
Background:
- Human Epidermal Growth Factor Receptor 2 (HER2) overexpression drives aggressive tumor proliferation in various cancers, including breast, gastric, ovarian, and lung.
- Targeting HER2 is a critical therapeutic strategy for HER2-positive malignancies, necessitating the development of novel inhibitors.
- Current therapeutic options face challenges, highlighting the need for innovative treatment approaches.
Purpose of the Study:
- To design and optimize peptide inhibitors targeting HER2 using machine learning.
- To identify peptide candidates with high binding affinities to HER2.
- To provide novel therapeutic options for HER2-overexpressing cancers.
Main Methods:
- Utilized machine learning (XGBRegressor) to predict binding affinities of designed peptide sequences.
- Employed FreeSASA, binding free energy calculations, and Density Functional Theory (DFT) for screening and characterization.
- Conducted molecular dynamics (MD) and QM/MM simulations to assess stability, binding mechanisms, and selectivity.
Main Results:
- Identified four primary peptide candidates, with pep-7 showing the most promising characteristics.
- Pep-7 exhibited strong binding free energy (-12.88 kcal/mol) and stable binding within the HER2 pocket during 300 ns MD simulations.
- DFT calculations indicated favorable molecular and reactivity properties for pep-7, with a high dipole moment (10,761.58 Debye).
Conclusions:
- Pep-7 demonstrated significant binding stability, favorable binding free energy, and molecular stability, positioning it as a promising therapeutic candidate.
- The findings suggest pep-7 as a viable novel treatment strategy for HER2-positive cancers.
- Further development of pep-7 could lead to improved therapeutic outcomes for patients with HER2-driven malignancies.
