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[Figurre: see text]. Protein aggregation can be defined as the sacrifice of stabilizing intrachain contacts of the functional state that are replaced with interchain contacts to form non-functional states. The resulting aggregate morphologies range from amorphous structures without long-range order typical of nondisease proteins involved in inclusion bodies to highly structured fibril assemblies typical of amyloid disease proteins. In this Account, we describe the development and application of computational models for the investigation of nondisease and disease protein aggregation as illustrated for the proteins L and G and the Alzheimer's Abeta systems. In each case, we validate the models against relevant experimental observables and then expand on the experimental window to better elucidate the link between molecular properties and aggregation outcomes. Our studies show that each class of protein exhibits distinct aggregation mechanisms that are dependent on protein sequence, protein concentration, and solution conditions. Nondisease proteins can have native structural elements in the denatured state ensemble or rapidly form early folding intermediates, which offers avenues of protection against aggregation even at relatively high concentrations. The possibility that early folding intermediates may be evolutionarily selected for their protective role against unwanted aggregation could be a useful strategy for reengineering sequences to slow aggregation and increase folding yield in industrial protein production. The observed oligomeric aggregates that we see for nondisease proteins L and G may represent the nuclei for larger aggregates, not just for large amorphous inclusion bodies, but potentially as the seeds of ordered fibrillar aggregates, since most nondisease proteins can form amyloid fibrils under conditions that destabilize the native state. By contrast, amyloidogenic protein sequences such as Abeta 1-40,42 and the familial Alzheimer's disease (FAD) mutants favor aggregation into ordered fibrils once the free-energy barrier for forming a critical nucleus is crossed. However, the structural characteristics and oligomer size of the soluble nucleation species have yet to be determined experimentally for any disease peptide sequence, and the molecular mechanism of polymerization that eventually delineates a mature fibril is unknown. This is in part due to the limited experimental access to very low peptide concentrations that are required to characterize these early aggregation events, providing an opportunity for theoretical studies to bridge the gap between the monomer and fibril end points and to develop testable hypotheses. Our model shows that Abeta 1-40 requires as few as 6-10 monomer chains (depending on sequence) to begin manifesting the cross-beta order that is a signature of formation of amyloid filaments or fibrils assessed in dye-binding kinetic assays. The richness of the oligomeric structures and viable filament and fibril polymorphs that we observe may offer structural clues to disease virulence variations that are seen for the WT and hereditary mutants.

Using a coarse-grained model of the Abeta peptide, we analyze the Arctic (E22G), Dutch (E22Q), and Flemish (A21G) familial Alzheimer's disease (FAD) mutants for any changes in the stability of amyloid assemblies with respect to the wild-type (WT) sequence. Based on a structural reference state of two protofilaments aligned to create the "agitated" protofibril as determined by solid-state NMR, we determine free energy trends for Abeta assemblies for the WT and FAD familial sequences. We find that the structural characteristics and oligomer size of the critical nucleus vary dramatically among the hereditary mutants. The Arctic mutant's disorder in the turn region introduces new stabilizing interactions that better align the two protofilaments, yielding a well-defined protofibril axis at relatively small oligomer sizes with respect to WT. By contrast, the critical nucleus for the Flemish mutant is beyond the 20 chains characterized in this study, thereby showing a strong shift in the equilibrium toward monomers with respect to larger protofibril assemblies. The Dutch mutant forms more ordered protofilaments than WT, but exhibits greater disorder in protofibril structure that includes an alternative polymorph of the WT fibril. An important conclusion of this work is that the Dutch mutant does not support the agitated protofibril assembly. We discuss the implications of the structural ensembles and free energy profiles for the FAD mutants in regards to interpretation of the kinetics of fibril assembly using chromatography and dye-binding experiments.

Photonic crystals have been widely studied due to their broad technological applications in lasers, sensors, optical telecommunications, and display devices. Typically, photonic crystals are periodic structures of touching dielectric materials with alternating high and low refractive indices, and to date, the variables of interest have focused primarily on crystal symmetry and the refractive indices of the constituent materials, primarily polymers and semiconductors. In contrast, finite difference time domain (FDTD) simulations suggest that plasmonic nanoparticle superlattices with spacer groups offer an alternative route to photonic crystals due to the controllable spacing of the nanoparticles and the high refractive index of the lattices, even far away from the plasmon frequency where losses are low. Herein, the stopband features of 13 Bravais lattices are characterized and compared, resulting in paradigm-shifting design principles for photonic crystals. Based on these design rules, a simple cubic structure with an ~130-nm lattice parameter is predicted to have a broad photonic stopband, a property confirmed by synthesizing the structure via DNA programmable assembly and characterizing it by reflectance measurements. We show through simulation that a maximum reflectance of more than 0.99 can be achieved in these plasmonic photonic crystals by optimizing the nanoparticle composition and structural parameters. [ABSTRACT FROM AUTHOR]