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<p>Small proteins, with more pronounced conformational flexibility, are crucial in biological processes and their function is linked to their high flexibility. While several databases are widely used to characterize protein dynamics, a significant data gap remains for small proteins comprising 5–100 amino acids. To address this, we present DynoDB, a dataset of small protein conformational ensembles from all-atom molecular dynamics simulations. In DynoDB, a total of 8,385 small proteins were subjected to all-atom molecular dynamics simulations of 100 ns each. We generated about 9.1 TB of trajectory data, covering diverse functional types of biomolecules, along with structural and functional annotations, as well as dynamic analysis results. This dataset systematically captures the conformational dynamics of the small proteins, serving as a dedicated data resource for the field of peptide design, vaccine development, antimicrobial screening, and AI modeling.</p>

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A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Ying Hu,
  • Xin Yang,
  • Xinlei Zhu,
  • Zhixiang Sui,
  • Pingping Sun,
  • Ming Ni,
  • Xiaochen Bo,
  • Longjia Jia,
  • Zhiguo Fu,
  • Zilin Ren

摘要

Small proteins, with more pronounced conformational flexibility, are crucial in biological processes and their function is linked to their high flexibility. While several databases are widely used to characterize protein dynamics, a significant data gap remains for small proteins comprising 5–100 amino acids. To address this, we present DynoDB, a dataset of small protein conformational ensembles from all-atom molecular dynamics simulations. In DynoDB, a total of 8,385 small proteins were subjected to all-atom molecular dynamics simulations of 100 ns each. We generated about 9.1 TB of trajectory data, covering diverse functional types of biomolecules, along with structural and functional annotations, as well as dynamic analysis results. This dataset systematically captures the conformational dynamics of the small proteins, serving as a dedicated data resource for the field of peptide design, vaccine development, antimicrobial screening, and AI modeling.