Collagen self-assembly supports its mechanical function, but controlling collagen mimetic peptides (CMPs) to self-assemble into higher-order oligomers with numerous functions remains challenging due to the vast potential amino acid sequence space. Herein, we developed a diffusion model to learn features from different types of human collagens and generate CMPs; obtaining 66% of synthetic CMPs could self-assemble into triple helices. Triple-helical and untwisting states were probed by melting temperature (Tm); hence, we developed a model to predict collagen Tm, achieving a state-of-art Pearson's correlation (PC) of 0.95 by cross-validation and a PC of 0.8 for predicting Tm values of synthetic CMPs. Our chemically synthesized short CMPs and recombinantly expressed long CMPs could self-assemble, with the lowest requirement for hydrogel formation at a concentration of 0.08% (w/v). Five CMPs could promote osteoblast differentiation. Our results demonstrated the potential for using computer-aided methods to design functional self-assembling CMPs.
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Diffusion model assisted designing self-assembling collagen mimetic peptides as biocompatible materials.
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