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Identification of selective protein-protein interaction inhibitors using efficient in silico peptide-directed ligand design.

ABSTRACT: The development of protein-protein interaction (PPI) inhibitors with therapeutic value is of increasing importance as the first clinical agent has now been approved, but PPIs remain difficult targets for the development of small molecule ligands. This article describes a highly efficient approach to the development of inhibitors of the p53/hDMX or hDM2 interaction that involves the design of small molecules in silico based upon a peptide/protein structure. The process for molecule design, starting from a virtual library of just over 1200 fragments, led to the eventual synthesis of twenty compounds, of which ten bound to either hDM2, hDMX or both in in vitro binding assays. This 50% success rate is extremely efficient compared to traditional high throughput screening. The identification of two selective hDMX inhibitors from twenty compounds highlights this efficiency as, to date, only two other hDMX-selective agents exist in the literature. Preliminary biological studies show that 20% of the compounds identified have cellular activity and activate downstream pathways associated with p53 activation.

SUBMITTER: Beekman AM

PROVIDER: S-EPMC6482886 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Identification of selective protein-protein interaction inhibitors using efficient in silico peptide-directed ligand design.

Beekman Andrew M AM Cominetti Marco M D MMD Walpole Samuel J SJ Prabhu Saurabh S O'Connell Maria A MA Angulo Jesus J Searcey Mark M

Chemical science 20190322 16

The development of protein-protein interaction (PPI) inhibitors with therapeutic value is of increasing importance as the first clinical agent has now been approved, but PPIs remain difficult targets for the development of small molecule ligands. This article describes a highly efficient approach to the development of inhibitors of the p53/hDMX or hDM2 interaction that involves the design of small molecules in silico based upon a peptide/protein structure. The process for mo ...[more]

PMID: 31057779

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Project description:Background/objectivesHuman 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.MethodsThis 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.ResultsAmong 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.ConclusionsPep-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.

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Identification of selective protein-protein interaction inhibitors using efficient in silico peptide-directed ligand design.

Publications

Identification of selective protein-protein interaction inhibitors using efficient <i>in silico</i> peptide-directed ligand design.

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