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<p>Cyclic peptides are a promising therapeutic modality, offering the potential to target challenging intracellular protein-protein interactions involved in cancer and other diseases. However, their clinical utility is frequently restricted by poor membrane permeability. While deep learning offers new methodologies to predict permeability, current models are limited by a reliance on 2D molecular representations that fail to capture the conformational flexibility inherent to macrocycles. Existing 3D resources also lack physics-based sampling of conformational dynamics across solvent environments that are critical for membrane permeability. To bridge this gap, we present CycPeptMPDB-4D, a comprehensive dataset comprising atomistic molecular dynamics trajectories for 5,160 structurally diverse cyclic peptides, including unnatural, N-methylated, and D-residues in circle and lariat topologies. Each peptide was simulated using the AMBER14SB force field in both explicit water and hexane environments for 50 nanoseconds to generate conformational ensembles in aqueous and membrane-mimicking phases. The trajectories capture the “chameleon-like” property, evidenced by markedly reduced conformational flexibility and polar surface area in the hydrophobic phase. Technical validation demonstrates that the simulated ensembles are in high agreement with experimental NMR data, covering NMR conformers within an RMSD of 1.6 Å. The dataset provides clustered ensembles, representative structures, and specialized descriptors such as desolvation free energy. This resource is designed to facilitate the development of deep learning models that incorporate 3D or 4D (trajectory- or ensemble-based) information to improve the prediction of cyclic peptide membrane permeability.</p>

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Multi-solvent conformational ensembles for predicting cyclic peptide permeability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Wei Liu,
  • Nguyen Hung Pham,
  • Chandra S. Verma,
  • Hwee Kuan Lee,
  • Jianguo Li

摘要

Cyclic peptides are a promising therapeutic modality, offering the potential to target challenging intracellular protein-protein interactions involved in cancer and other diseases. However, their clinical utility is frequently restricted by poor membrane permeability. While deep learning offers new methodologies to predict permeability, current models are limited by a reliance on 2D molecular representations that fail to capture the conformational flexibility inherent to macrocycles. Existing 3D resources also lack physics-based sampling of conformational dynamics across solvent environments that are critical for membrane permeability. To bridge this gap, we present CycPeptMPDB-4D, a comprehensive dataset comprising atomistic molecular dynamics trajectories for 5,160 structurally diverse cyclic peptides, including unnatural, N-methylated, and D-residues in circle and lariat topologies. Each peptide was simulated using the AMBER14SB force field in both explicit water and hexane environments for 50 nanoseconds to generate conformational ensembles in aqueous and membrane-mimicking phases. The trajectories capture the “chameleon-like” property, evidenced by markedly reduced conformational flexibility and polar surface area in the hydrophobic phase. Technical validation demonstrates that the simulated ensembles are in high agreement with experimental NMR data, covering NMR conformers within an RMSD of 1.6 Å. The dataset provides clustered ensembles, representative structures, and specialized descriptors such as desolvation free energy. This resource is designed to facilitate the development of deep learning models that incorporate 3D or 4D (trajectory- or ensemble-based) information to improve the prediction of cyclic peptide membrane permeability.