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

ABSTRACT: 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.

SUBMITTER: Li F

PROVIDER: S-EPMC6561259 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Li Fei F Han Jinsong J Cao Tian T Lam William W Fan Baoer B Tang Wen W Chen Sijie S Fok Kin Lam KL Li Linxian L

Proceedings of the National Academy of Sciences of the United States of America 20190520 23

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 ...[more]

PMID: 31110004

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Project description:Cartilage injury represents a frequent dilemma in clinical practice owing to its inherently limited self-renewal capacity. Biomimetic strategy-based engineered biomaterial, capable of coordinated regulation for cellular and microenvironmental crosstalk, provides an adequate avenue to boost cartilage regeneration. The level of oxidative stress in microenvironments is verified to be vital for tissue regeneration, yet it is often overlooked in engineered biomaterials for cartilage regeneration. Herein, inspired by natural cartilage architecture, a fibril-network glycopeptide hydrogel (Nap-FFGRGD@FU), composed of marine-derived polysaccharide fucoidan (FU) and naphthalenephenylalanine-phenylalanine-glycine-arginine-glycine-aspartic peptide (Nap-FFGRGD), was presented through a simple supramolecular self-assembly approach. The Nap-FFGRGD@FU hydrogels exhibit a native cartilage-like architecture, characterized by interwoven collagen fibers and attached proteoglycans. Beyond structural simulation, fucoidan-exerted robust biological effects and Arg-Gly-Asp (RGD) sequence-provided cell attachment sites realized functional reinforcement, synergistically promoted extracellular matrix (ECM) production and reactive oxygen species (ROS) elimination, thus contributing to chondrocytes-ECM harmony. In vitro co-culture with glycopeptide hydrogels not only facilitated cartilage ECM anabolic metabolism but also scavenged ROS accumulation in chondrocytes. Mechanistically, the chondro-protective effects induced by glycopeptide hydrogels rely on the activation of endogenous antioxidant pathways associated with nuclear factor erythroid 2-related factor 2 (NRF2). In vivo implantation of glycopeptide hydrogels successfully improved the de novo cartilage generation by 1.65-fold, concomitant with coordinately restructured subchondral bone structure. Collectively, our ingeniously crafted bionic glycopeptide hydrogels simultaneously rewired chondrocytes' function by augmenting anabolic metabolism and rebuilt ECM microenvironment via preserving redox equilibrium, holding great potential for cartilage tissue engineering.

Dataset Information

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

Publications

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

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