Bioactive scaffolds with enhanced supramolecular motion promote recovery from spinal cord injury.

The signaling of cells by scaffolds of synthetic molecules that mimic proteins is known to be effective in the regeneration of tissues. Here, we describe peptide amphiphile supramolecular polymers containing two distinct signals and test them in a mouse model of severe spinal cord injury. One signal activates the transmembrane receptor ß1-integrin and a second one activates the basic fibroblast growth factor 2 receptor. By mutating the peptide sequence of the amphiphilic monomers in nonbioactive domains, we intensified the motions of molecules within scaffold fibrils. This resulted in notable differences in vascular growth, axonal regeneration, myelination, survival of motor neurons, reduced gliosis, and functional recovery. We hypothesize that the signaling of cells by ensembles of molecules could be optimized by tuning their internal motions.

Molecular dynamics simulations reveal disruptive self-assembly in dynamic peptide libraries.

There is significant interest in the use of unmodified self-assembling peptides as building blocks for functional, supramolecular biomaterials. Recently, dynamic peptide libraries (DPLs) have been proposed to select self-assembling materials from dynamically exchanging mixtures of dipeptide inputs in the presence of a nonspecific protease enzyme, where peptide sequences are selected and amplified based on their self-assembling tendencies. It was shown that the results of the DPL of mixed sequences (e.g. starting from a mixture of dileucine, L2, and diphenylalanine, F2) did not give the same outcome as the separate L2 and F2 libraries (which give rise to the formation of F6 and L6), implying that interactions between these sequences could disrupt the self-assembly. In this study, coarse grained molecular dynamics (CG-MD) simulations are used to understand the DPL results for F2, L2 and mixed libraries. CG-MD simulations demonstrate that interactions between precursors can cause the low formation yield of hexapeptides in the mixtures of dipeptides and show that this ability to disrupt is influenced by the concentration of the different species in the DPL. The disrupting self-assembly effect between the species in the DPL is an important effect to take into account in dynamic combinatorial chemistry as it affects the possible discovery of new materials. This work shows that combined computational and experimental screening can be used complementarily and in combination providing a powerful means to discover new supramolecular peptide nanostructures.

Metastable hydrogels from aromatic dipeptides.

We demonstrate that the well-known self-assembling dipeptide diphenylalanine (FF) and its amidated derivative (FF-NH2) can form metastable hydrogels upon sonication of the dipeptide solutions. The hydrogels show instantaneous syneresis upon mechanical contact resulting in rapid expulsion of water and collapse into a semi-solid gel.

Using experimental and computational energy equilibration to understand hierarchical self-assembly of Fmoc-dipeptide amphiphiles.

Despite progress, a fundamental understanding of the relationships between the molecular structure and self-assembly configuration of Fmoc-dipeptides is still in its infancy. In this work, we provide a combined experimental and computational approach that makes use of free energy equilibration of a number of related Fmoc-dipeptides to arrive at an atomistic model of Fmoc-threonine-phenylalanine-amide (Fmoc-TF-NH2) which forms twisted fibres. By using dynamic peptide libraries where closely related dipeptide sequences are dynamically exchanged to eventually favour the formation of the thermodynamically most stable configuration, the relative importance of C-terminus modifications (amide versus methyl ester) and contributions of aliphatic versus aromatic amino acids (phenylalanine F vs. leucine L) is determined (F > L and NH2 > OMe). The approach enables a comparative interpretation of spectroscopic data, which can then be used to aid the construction of the atomistic model of the most stable structure (Fmoc-TF-NH2). The comparison of the relative stabilities of the models using molecular dynamic simulations and the correlation with experimental data using dynamic peptide libraries and a range of spectroscopy methods (FTIR, CD, fluorescence) allow for the determination of the nanostructure with atomistic resolution. The final model obtained through this process is able to reproduce the experimentally observed formation of intertwining fibres for Fmoc-TF-NH2, providing information of the interactions involved in the hierarchical supramolecular self-assembly. The developed methodology and approach should be of general use for the characterization of supramolecular structures.