There is an urgent need for biomaterials that support tissue healing, particularly neuronal regeneration. In a medium throughput screen novel self-assembling peptide (SAP) sequences that form fibrils and stimulated nerve fiber growth of peripheral nervous system (PNS)-derived neurons are identified. Based on the peptide sequences and fibril morphologies and by applying rational data-mining, important structural parameters stimulating neuronal activity are elucidated. Three SAPs (SAP 1e , SAP 2e , and SAP 5c ) enhance adhesion and growth of PNS neurons. These SAPs form 2D and 3D matrices that serve as bioactive scaffolds stimulating cell adhesion and growth. The newly discovered SAPs also support the growth of CNS neurons and glia cells. Subsequently, the potential of SAPs to enhance PNS regeneration in vivo is analyzed. For this, the facial nerve driving whisker movement in mice is injured. Notably, SAPs persist for up to 3 weeks in the injury site indicating highly adhesive properties and stability. After SAP administration, more motor neurons incorporating markers for successive regeneration are observed. Recovery of whisker movement is elevated in SAP-injected mice. In summary, short peptides that form fibrils are identified and the adhesion, growth, and regeneration of neurons have been efficiently enhanced without the necessity to attach hormones or growth factors.

Gene therapy via retroviral vectors holds great promise for treating a variety of serious diseases. It requires the use of additives to boost infectivity. Amyloid-like peptide nanofibers (PNFs) were shown to efficiently enhance retroviral gene transfer. However, the underlying mode of action of these peptides remains largely unknown. This data-mining study elucidates the multi-scale structure-property-activity relationship of transduction enhancing peptides for retroviral gene transfer. In contrast to previous reports, we find that not the amyloid fibrils themselves, but rather m-sized -sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of -sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge were identified as key material properties. We validate the reliability of the amphiphilic sequence pattern and the general applicability of the key properties by rationally creating new active sequences and identifying short amyloidal peptides from various pathogenic and functional origin. Data-mining - even for small datasets - enables the development of new efficient retroviral transduction enhancers and provides important insights into the diverse bioactivity of the functional material class of amyloids.

We study the asymptotic properties of the steady state mass distribution for a class of collision kernels in an aggregation-shattering model in the limit of small shattering probabilities. It is shown that the exponents characterizing the large and small mass asymptotic behavior of the mass distribution depend on whether the collision kernel is local (the aggregation mass flux is essentially generated by collisions between particles of similar masses) or nonlocal (collision between particles of widely different masses give the main contribution to the mass flux). We show that the nonlocal regime is further divided into two subregimes corresponding to weak and strong nonlocality. We also observe that at the boundaries between the local and nonlocal regimes, the mass distribution acquires logarithmic corrections to scaling and calculate these corrections. Exact solutions for special kernels and numerical simulations are used to validate some nonrigorous steps used in the analysis. Our results show that for local kernels, the scaling solutions carry a constant flux of mass due to aggregation, whereas for the nonlocal case there is a correction to the constant flux exponent. Our results suggest that for general scale-invariant kernels, the universality classes of mass distributions are labeled by two parameters: the homogeneity degree of the kernel and one further number measuring the degree of the nonlocality of the kernel.