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螺旋肽的混合粗粒化方法:溶剂化、二级结构和组装。

A hybrid approach for coarse-graining helical peptoids: Solvation, secondary structure, and assembly.

机构信息

Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA.

出版信息

J Chem Phys. 2023 Mar 21;158(11):114105. doi: 10.1063/5.0138510.

Abstract

Protein mimics such as peptoids form self-assembled nanostructures whose shape and function are governed by the side chain chemistry and secondary structure. Experiments have shown that a peptoid sequence with a helical secondary structure assembles into microspheres that are stable under various conditions. The conformation and organization of the peptoids within the assemblies remains unknown and is elucidated in this study via a hybrid, bottom-up coarse-graining approach. The resultant coarse-grained (CG) model preserves the chemical and structural details that are critical for capturing the secondary structure of the peptoid. The CG model accurately captures the overall conformation and solvation of the peptoids in an aqueous solution. Furthermore, the model resolves the assembly of multiple peptoids into a hemispherical aggregate that is in qualitative agreement with the corresponding results from experiments. The mildly hydrophilic peptoid residues are placed along the curved interface of the aggregate. The composition of the residues on the exterior of the aggregate is determined by two conformations adopted by the peptoid chains. Hence, the CG model simultaneously captures sequence-specific features and the assembly of a large number of peptoids. This multiscale, multiresolution coarse-graining approach could help in predicting the organization and packing of other tunable oligomeric sequences of relevance to biomedicine and electronics.

摘要

蛋白质模拟物,如肽类,形成自组装纳米结构,其形状和功能由侧链化学和二级结构决定。实验表明,具有螺旋二级结构的肽类序列可以组装成在各种条件下都稳定的微球。然而,组装体中肽类的构象和组织仍然未知,本研究通过混合的自下而上的粗粒化方法阐明了这一点。所得的粗粒化 (CG) 模型保留了对于捕捉肽类二级结构至关重要的化学和结构细节。CG 模型准确地捕捉了在水溶液中肽类的整体构象和溶剂化。此外,该模型解决了多个肽类组装成半球形聚集体的问题,这与实验的相应结果定性一致。轻度亲水的肽类残基沿聚集体的弯曲界面排列。聚集体外部残基的组成由肽链采用的两种构象决定。因此,CG 模型同时捕捉了序列特异性特征和大量肽类的组装。这种多尺度、多分辨率的粗粒化方法有助于预测与生物医学和电子学相关的其他可调节寡聚物序列的组织和包装。

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