Accurate computational design of three-dimensional protein crystals.

Protein crystallization plays a central role in structural biology. Despite this, the process of crystallization remains poorly understood and highly empirical, with crystal contacts, lattice packing arrangements and space group preferences being largely unpredictable. Programming protein crystallization through precisely engineered side-chain-side-chain interactions across protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach for designing three-dimensional protein crystals with prespecified lattice architectures at atomic accuracy that hierarchically constrains the overall number of degrees of freedom of the system. We design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into >100 µm three-dimensional crystals. The structures of these crystals are nearly identical to the computational design models, closely corresponding in both overall architecture and the specific protein-protein interactions. The dimensions of the crystal unit cell can be systematically redesigned while retaining the space group symmetry and overall architecture, and the crystals are extremely porous and highly stable. Our approach enables the computational design of protein crystals with high accuracy, and the designed protein crystals, which have both structural and assembly information encoded in their primary sequences, provide a powerful platform for biological materials engineering.

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders.

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

Importance of Substrate-Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles.

We study the protein-directed assembly of colloidal gold nanoparticles on de novo designed protein nanofiber templates. Using sequential assembly on glass substrates, we attach positively charged gold nanoparticles to protein nanofibers engineered to have a high density of negatively charged surface residues. Using a combination of electron and optical microscopy, we measure the density of particle attachment and characterize binding specificity. By varying nanoparticle size and pH of the solution, we explore the importance of charge-dependent particle-fiber and particle-substrate interactions. We find an inverse correlation between particle size and attachment density to protein nanofibers, attributed to the balance between size-dependent electrostatic particle-fiber attraction and particle-substrate repulsion. We show pH-dependent particle attachment density and binding specificity in relation to the protonation fraction of each assembly layer. Finally, we employ hyperspectral scattering microscopy to draw conclusions about particle density and interparticle spacings of optically observable particle assemblies.

Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis.

In this study, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.

Modifying Titania Using the Molten-Salt-Assisted Self-Assembly Process for Cadmium Selenide-Quantum Dot-Sensitized Photoanodes.

Sensitizing titania with semiconducting quantum dots (QDs) is an important field for the development of third-generation photovoltaics. Many methods have been developed to effectively incorporate QDs over the surface of mesoporous titania, assembled from the 20-25 nm titania nanoparticles. Here, we introduce a molten-salt-assisted self-assembly (MASA) method to fabricate CdSe-modified mesoporous titania photoanodes. A mixture of ethanol, two surfactants (cetyltrimethylammonium bromide and 10-lauryl ether), silica (tetramethyl orthosilicate) or titania source (Ti(OC4H9)4, acid (HNO3), and cadmium nitrate solution was infiltrated into the pores of mesoporous titania (assembled using Degussa 25, P25) and immediately calcined at 450 °C to obtain mesoporous cadmium oxide-silica-titania (meso-CdO-SiO2-P25) or cadmium titanate-titania (meso-CdTiO3-P25) films. The MASA process is a simple method to smoothly coat or fill the pores of titania with mesoporous CdO-SiO2 or CdTiO3 that can be reacted under an H2Se atmosphere to convert cadmium species to CdSe at 100 °C. Etching of the silica films with a very dilute hydrogen fluoride solution produces mesoporous CdSe-titania (meso-CdSe-P25) electrodes. The method is flexible to adjust the CdSe/TiO2 mole ratio over a very broad range in the films. The films were characterized at every stage of the preparation to demonstrate the effectiveness of the method. The electrodes were also tested in a simple two-electrode solar cell to demonstrate the performance of the electrodes that have a power conversion efficiency of 3.35%.