Design of self-assembly dipeptide hydrogels and machine learning via their chemical features

Fei Li, Jinsong Han, Tian Cao, William Lam, Baoer Fan, Wen Tang, Sijie Chen, Kin Lam Fok, Linxian Li
2019 Proceedings of the National Academy of Sciences of the United States of America  
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
more » ... 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.
doi:10.1073/pnas.1903376116 pmid:31110004 pmcid:PMC6561259 fatcat:dv3tlgodp5c7fijbghreyoark4