Computational Design of a Protein Crystal

Protein crystals have catalytic and materials applications and are central to efforts in structural biology and therapeutic development. Designing predetermined crystal structures can be subtle given the complexity of proteins and the noncovalent interactions that govern crystallization. De novo protein design provides an approach to engineer highly complex nanoscale molecular structures, and often the positions of atoms can be programmed with sub-Å precision. Herein, a computational approach is presented for the design of proteins that self-assemble in three dimensions to yield macroscopic crystals. A three-helix coiled-coil protein is designed de novo to form a polar, layered, three-dimensional crystal having the P6 space group, which has a “honeycomb-like” structure and hexameric channels that span the crystal. The approach involves: (i) creating an ensemble of crystalline structures consistent with the targeted symmetry; (ii) characterizing this ensemble to identify “designable” structures from minima in the sequence-structure energy landscape and designing sequences for these structures; (iii) experimentally characterizing candidate proteins. A 2.1 Å resolution X-ray crystal structure of one such designed protein exhibits sub-Å agreement [backbone root mean square deviation (rmsd)] with the computational model of the crystal. This approach to crystal design has potential applications to the de novo design of nanostructured materials and to the modification of natural proteins to facilitate X-ray crystallographic analysis

Computational design of protein crystals: Controlling macroscopic molecular orientation through protein engineering

Computational protein design algorithms rapidly identify amino acid sequences consistent with a target protein fold. Given protein function is dictated by fold, through protein design, it is possible to engineer protein functionality, structure and assembly. Despite many advances in protein design methodology, the de novo design of a protein crystal lattice has yet to be conducted. Protein crystallography is the centerpiece of structural biology, however realization of diffraction quality crystals can be difficult to almost impossible in some cases. In addition to an inherent importance in structure determination, protein crystallization has the potential to be leveraged into materials with well-defined order. As a result, ab initio protein crystal design has the potential to alleviate difficulties associated with protein crystallization and develop new nanostructured materials. In this thesis a computational energy landscape approach for designing protein crystals was developed and experimentally tested. A three-helix coiled-coil protein was designed de novo to form a polar three-dimension crystal consistent with the honey-comb P6 space group. A high-resolution x-ray structure of the designed protein crystal revealed remarkable precision compared to the computational model. The approach has been expanded to more complex single-chain four-helix bundle protein, that has been previously designed to selectively bind a non-biological, non-linear optical cofactor. The ultimate goal of the project is to crystal this protein into a highly ordered, polar space group. Subsequently, orientating the cofactor in a high-density, polar, three-dimensional arrangement and achieving a nonlinear optical biomaterial

Computational design of protein crystals: Controlling macroscopic molecular orientation through protein engineering

Computational protein design algorithms rapidly identify amino acid sequences consistent with a target protein fold. Given protein function is dictated by fold, through protein design, it is possible to engineer protein functionality, structure and assembly. Despite many advances in protein design methodology, the de novo design of a protein crystal lattice has yet to be conducted. Protein crystallography is the centerpiece of structural biology, however realization of diffraction quality crystals can be difficult to almost impossible in some cases. In addition to an inherent importance in structure determination, protein crystallization has the potential to be leveraged into materials with well-defined order. As a result, ab initio protein crystal design has the potential to alleviate difficulties associated with protein crystallization and develop new nanostructured materials. In this thesis a computational energy landscape approach for designing protein crystals was developed and experimentally tested. A three-helix coiled-coil protein was designed de novo to form a polar three-dimension crystal consistent with the honey-comb P6 space group. A high-resolution x-ray structure of the designed protein crystal revealed remarkable precision compared to the computational model. The approach has been expanded to more complex single-chain four-helix bundle protein, that has been previously designed to selectively bind a non-biological, non-linear optical cofactor. The ultimate goal of the project is to crystal this protein into a highly ordered, polar space group. Subsequently, orientating the cofactor in a high-density, polar, three-dimensional arrangement and achieving a nonlinear optical biomaterial

Controlling Association and Separation of Gold Nanoparticles with Computationally Designed Zinc-Coordinating Proteins

Functionalization of nanoparticles with biopolymers has yielded a wide range of structured and responsive hybrid materials. DNA provides the ability to program length and recognition using complementary oligonucleotide sequences. Nature more often leverages the versatility of proteins, however, where structure, assembly, and recognition are more subtle to engineer. Herein, a protein was computationally designed to present multiple Zn²⁺ coordination sites and cooperatively self-associate to form an antiparallel helical homodimer. Each subunit was unstructured in the absence of Zn²⁺ or when the cation was sequestered with a chelating agent. When bound to the surface of gold nanoparticles via cysteine, the protein provided a reversible molecular linkage between particles. Nanoparticle association and changes in interparticle separation were monitored by redshifts in the surface plasmon resonance (SPR) band and by transmission electron microscopy (TEM). Titrations with Zn²⁺ revealed sigmoidal transitions at submicromolar concentrations. The metal-ion concentration required to trigger association varied with the loading of the proteins on the nanoparticles, the solution ionic strength, and the cation employed. Specifying the number of helical (heptad) repeat units conferred control over protein length and nanoparticle separation. Two different length proteins were designed via extension of the helical structure. TEM and extinction measurements revealed distributions of nanoparticle separations consistent with the expected protein structures. Nanoparticle association, interparticle separation, and SPR properties can be tuned using computationally designed proteins, where protein structure, folding, length, and response to molecular species such as Zn²⁺ can be engineered

Observing Changes in the Structure and Oligomerization State of a Helical Protein Dimer Using Solid-State Nanopores

Protein analysis using solid-state nanopores is challenging due to limitations in bandwidth and signal-to-noise ratio. Recent improvements of those two aspects have made feasible the study of small peptides using solid-state nanopores, which have an advantage over biological counterparts in tunability of the pore diameter. Here, we report on the detection and characterization of peptides as small as 33 amino acids. Silicon nitride nanopores with thicknesses less than 10 nm are used to provide signal-to-noise (S/N) levels up to S/N ∼ 10 at 100 kHz. We demonstrate differentiation of monomer and dimer forms of the GCN4-p1 leucine zipper, a coiled-coil structure well studied in molecular biology, and compare with the unstructured 33-residue monomer. GCN4-p1 is sequence segment associated with homodimerization of the transcription factor General Control Nonderepressible 4 (GCN4), which is involved in the control of amino acid synthesis in yeast. The differentiation between two oligomeric forms demonstrates the capabilities of improved solid-state nanopore platforms to extract structural information involving short peptide structures

Photoinduced Electron Transfer Elicits a Change in the Static Dielectric Constant of a <i>de Novo</i> Designed Protein

We provide a direct measure of the change in effective dielectric constant (ε_S) within a protein matrix after a photoinduced electron transfer (ET) reaction. A linked donor–bridge–acceptor molecule, PZn–Ph–NDI, consisting of a (porphinato)­Zn donor (PZn), a phenyl bridge (Ph), and a naphthalene diimide acceptor (NDI), is shown to be a “meter” to indicate protein dielectric environment. We calibrated PZn–Ph–NDI ET dynamics as a function of solvent dielectric, and computationally <i>de novo</i> designed a protein <i>SCPZnI3</i> to bind PZn–Ph–NDI in its interior. Mapping the protein ET dynamics onto the calibrated ET catalogue shows that <i>SCPZnI3</i> undergoes a switch in the effective dielectric constant following photoinduced ET, from ε_S ≈ 8 to ε_S ≈ 3