Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
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自组装二肽水凝胶的设计和通过其化学特性进行机器学习
DOI：
10.1073/pnas.1903376116
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发表时间：
2019-06-04
影响因子：
11.1
通讯作者：
Li, Linxian
中科院分区：
文献类型：
--
作者：
Li, Fei;Han, Jinsong;Li, Linxian
多肽自组装水凝胶在生物医学领域的应用引起了人们极大的兴趣。然而，肽的化学结构与其相应的水凝胶性质之间的联系仍然不清楚。在这里，我们展示了一种组合方法来生成具有2，000多种肽的结构多样的水凝胶文库，并评估了其相应的特性。我们使用定量结构-性质关系来计算它们的化学特征，反映拓扑和物理化学性质，并应用机器学习来预测自组装行为。我们观察到水凝胶的刚度与凝胶的直径和交联度相关。重要的是，我们证明了水凝胶支持培养中的细胞增殖，表明了水凝胶的生物相容性。我们开发的组合水凝胶库和机器学习方法将化学结构与其自组装行为联系起来，可以加速设计用于生物医学用途的新型肽结构。
Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.