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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
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一个模块化和可扩展的CHARMM兼容模型,用于对多类的全原子模拟.

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我们开发了一种新的模块化侧链CGenFF-NTOID (MoSiC-CGenFF-NTOID) 模型,用于模拟类聚合物. 这种模块化方法简化了添加新的侧链,增强了药物输送和仿生应用的分子建模.

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科学领域:

  • 聚合物化学 聚合物化学
  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 类 (N替代型甘氨酸) 是多功能合成聚合物,在药物输送,催化和生物仿真方面具有应用.
  • 经典的分子模拟,特别是使用CGenFF-NTOID模型,对于理解peptoid行为至关重要.
  • 传统上,将现有的力场扩展到新的peptoid侧链需要进行广泛的重构,限制模块化.

研究的目的:

  • 为类动物开发CGenFF-NTOID力场的模块化扩展.
  • 为了使任意侧链从CHARMM通用力量场 (CGenFF) 直接纳入.
  • 为类模拟创建一个可扩展的建模范式.

主要方法:

  • 通过分解类骨干和侧链参数化,开发了模块化侧链CGenFF-NTOID (MoSiC-CGenFF-NTOID) 模型.
  • 将任意侧链从CGenFF力场直接移植到peptoid模型中.
  • 根据初始计算和实验数据验证了MoSiC-CGenFF-NTOID模型.

主要成果:

  • 创建了一个经过验证的MoSiC-CGenFF-NTOID模型,涵盖所有20个天然氨基酸侧链和13个常见的合成侧链.
  • 建立了一个可扩展的框架来结合新的类侧链,确定重新调整CGenFF参数的需要.
  • 开发了一种用于自动化初始结构生成的工具,可供研究界免费使用.

结论:

  • 这种MoSiC-CGenFF-NTOID模型显著提高了peptoid分子模拟的模块化和可扩展性.
  • 这种方法简化了多样化的侧链的整合,促进了类研究的更广泛应用.
  • 免费可用的模型和工具使研究人员能够有效地探索新的类序列和属性.