Transition from disordered aggregates to ordered lattices: kinetic control of the assembly of a computationally designed peptide

Abstract

Natural biomolecular self-assembly typically occurs under a narrow range of solution conditions, and the design of sequences that can form prescribed structures under a range of such conditions would be valuable in the bottom-up assembly of predetermined nanostructures. We present a computationally designed peptide that robustly self-assembles into regular arrays under a wide range of solution pH and temperature conditions. Controling the solution conditions provides the opportunity to exploit a simple and reproducible approach for altering the pathway of peptide solution self-assembly. The computationally designed peptide forms a homotetrameric coiled-coil bundle that further self-assembles into 2-D plate structures with well-defined inter-bundle symmetry. Herein, we present how modulation of solution conditions, such as pH and temperature, can be used to control the kinetics of the inter-bundle assembly and manipulate the final morphology. Changes in solution pH primarily influence the inter-bundle assembly by affecting the charged state of ionizable residues on the bundle exterior while leaving the homotetrameric coiled-coil structure intact. At low pH, repulsive interactions prevent 2-D lattice nanostructure formation. Near the estimated isoelectric point of the peptide, bundle aggregation is rapid and yields disordered products, which subsequently transform into ordered nanostructures over days to weeks. At elevated temperatures (T = 40 °C or 50 °C), the formation of disordered, kinetically-trapped products largely can be eliminated, allowing the system to quickly assemble into plate-like nanostructured lattices. Moreover, subtle changes in pH and in the peptide charge state have a significant influence on the thickness of formed plates and on the hierarchical manner in which plates fuse into larger material structures with observable grain boundaries. These findings confirm the ability to finely tune the peptide assembly process to achieve a range of engineered structures with one simple 29-residue peptide building block.

Multicomponent peptide assemblies

“…While the same noncovalent interactions govern the formation of single component self-assembled peptide nanostructures and multicomponent coassembled nanostructures, 99–102 the kinetic and thermodynamic parameters that influence these processes are of special interest in the controlled coassembly of multicomponent materials. 102,103 Environmental factors such as temperature, 104,105 pH, 104–106 salt effects, 107–109 and solvent interactions 110–115 have been shown to influence the kinetics and thermodynamics peptide self-assembly in ways that dictate the ultimate supramolecular materials that are formed. When considering coassembly of multiple peptides, the kinetics and thermodynamics of both self-assembly and coassembly of the various components must be accounted for in order to selectively form the desired multicomponent materials.…”

Infinite Assembly of Folded Proteins in Evolution, Disease, and Engineering

“…Saven and co‐workers used a computational approach to identify a sequence variant of a 3‐helix bundle that would self‐interact to form a crystalline lattice with a specific geometry (Figure g) . This computational approach was also used to design robust arrays of two‐dimensional helical bundles that could tolerate variability in the solution conditions as well as chemical modification of the termini …”

Biomolecular Assemblies: Moving from Observation to Predictive Design

“…Unlike β-sheets, which rely on intermolecular hydrogen bonding for assembly, the assembly of α-helices is governed by intermolecular sidechain interactions, which can be optimized to pack hydrophobic residues together and pair polar or oppositely-charged residues. 133,134,136,163,186,189–191 Interestingly, specific combinations of sidechains on adjacent molecules tend to promote specific intermolecular alignments. 192–194 When small numbers of α-helices align with one another in a “blunt-ended” manner (Figure 9b), they can assemble to form nanoparticle structures (Figures 10a and 11a).…”

“…105,157,200,201 Computational tools have proven to be effective for the prediction and optimization of helix-helix interfaces, enabling rational design of α-helical peptide assemblies to form nanoparticles (or oligomeric structures), nanofibers, and nanosheets. 133,136,137,200,201…”