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Stem‐cell behavior is regulated by the material properties of the surrounding extracellular matrix, which has important implications for the design of tissue‐engineering scaffolds. However, our understanding of the material properties of stem‐cell scaffolds is limited to nanoscopic‐to‐macroscopic length scales. Herein, a solid‐state NMR approach is presented that provides atomic‐scale information on complex stem‐cell substrates at near physiological conditions and at natural isotope abundance. Using self‐assembled peptidic scaffolds designed for nervous‐tissue regeneration, we show at atomic scale how scaffold‐assembly degree, mechanics, and homogeneity correlate with favorable stem cell behavior. Integration of solid‐state NMR data with molecular dynamics simulations reveals a highly ordered fibrillar structure as the most favorable stem‐cell scaffold. This could improve the design of tissue‐engineering scaffolds and other self‐assembled biomaterials.

Self‐assembling and molecular folding are ubiquitous in Nature: they drive the organization of systems ranging from living creatures to DNA molecules. Elucidating the complex dynamics underlying these phenomena is of crucial importance. However, a tool for the analysis of the various phenomena involved in protein/peptide aggregation is still missing. Here, an innovative software is developed and validated for the identification and visualization of b‐structuring and b‐sheet formation in both simulated systems and crystal structures of proteins and peptides. The novel software suite, dubbed Morphoscanner, is designed to identify and intuitively represent b‐structuring and b‐sheet formation during molecular dynamics trajectories, paying attention to temporary strand‐strand alignment, suboligomer formation and evolution of local order. Self‐assembling peptides (SAPs) constitute a promising class of biomaterials and an interesting model to study the spontaneous assembly of molecular systems in vitro. With the help of coarse‐grained molecular dynamics the self‐assembling of diverse SAPs is simulated into molten aggregates. When applied to these systems, Morphoscanner highlights different b‐structuring schemes and kinetics related to SAP sequences. It is demonstrated that Morphoscanner is a novel versatile tool designed to probe the aggregation dynamics of self‐assembling systems, adaptable to the analysis of differently coarsened simulations of a variety of biomolecules.