Folded peptides present complex exterior surfaces specified by their amino acid sequences, and the control of these surfaces offers high-precision routes to self-assembling materials. The complexity of peptide structure and the subtlety of noncovalent interactions make the design of predetermined nanostructures difficult. Computational methods can facilitate this design and are used here to determine 29-residue peptides that form tetrahelical bundles that, in turn, serve as building blocks for lattice-forming materials. Four distinct assemblies were engineered. Peptide bundle exterior amino acids were designed in the context of three different interbundle lattices in addition to one design to produce bundles isolated in solution. Solution assembly produced three different types of lattice-forming materials that exhibited varying degrees of agreement with the chosen lattices used in the design of each sequence. Transmission electron microscopy revealed the nanostructure of the sheetlike nanomaterials. In contrast, the peptide sequence designed to form isolated, soluble, tetrameric bundles remained dispersed and did not form any higher-order assembled nanostructure. Small-angle neutron scattering confirmed the formation of soluble bundles with the designed size. In the lattice-forming nanostructures, the solution assembly process is robust with respect to variation of solution conditions (pH and temperature) and covalent modification of the computationally designed peptides. Solution conditions can be used to control micrometer-scale morphology of the assemblies. The findings illustrate that, with careful control of molecular structure and solution conditions, a single peptide motif can be versatile enough to yield a wide range of self-assembled lattice morphologies across many length scales (1 to 1000 nm).
Keywords: Peptide; computational design; de novo design; nanomaterials; self-assembly; soft matter.