Computational approaches for understanding and predicting the self-assembled peptide hydrogels

Self-assembled peptide hydrogel is promising biomaterial and has been widely applied in many fields. As a typical self-assembly material, peptide hydrogel exhibits properties different from traditional polymer hydrogel, and has unique features in molecular design, structural elements of hydrogel and control strategies. With the desire to apply the principles of self-assembly to the design and prediction of peptide hydrogels, there has more and more emphasis on understanding the driving forces and microscopic behaviors involved in the self-assembly process. Computational methods have played an increasingly important role in recent research in helping to reveal the relationship between molecular chemical structure and self-assembly processes as well as assembled morphologies, thus determining the ability of supramolecular gelation. This review aims to summarize the application of computational tools to obtain a better fundamental understanding of the multi-scale structural details of self-assembled peptide hydrogels and to predict the gelation behavior of supramolecular nanofibers. It is expected that researchers will consider using these computational tools when investigating and designing novel peptide hydrogel materials.