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

Canine CNGA3 Gene Mutations Provide Novel Insights Into Human Achromatopsia-Associated Channelopathies and Treatment

Cyclic nucleotide-gated (CNG) ion channels are key mediators underlying signal transduction in retinal and olfactory receptors. Genetic defects in CNGA3 and CNGB3, encoding two structurally related subunits of cone CNG channels, lead to achromatopsia (ACHM). ACHM is a congenital, autosomal recessive retinal disorder that manifests by cone photoreceptor dysfunction, severely reduced visual acuity, impaired or complete color blindness and photophobia. Here, we report the first canine models for CNGA3-associated channelopathy caused by R424W or V644del mutations in the canine CNGA3 ortholog that accurately mimic the clinical and molecular features of human CNGA3-associated ACHM. These two spontaneous mutations exposed CNGA3 residues essential for the preservation of channel function and biogenesis. The CNGA3-R424W results in complete loss of cone function in vivoand channel activity confirmed by in vitro electrophysiology. Structural modeling and molecular dynamics (MD) simulations revealed R424-E306 salt bridge formation and its disruption with the R424W mutant. Reversal of charges in a CNGA3-R424E-E306R double mutant channel rescued cGMP-activated currents uncovering new insights into channel gating. The CNGA3-V644del affects the C-terminal leucine zipper (CLZ) domain destabilizing intersubunit interactions of the coiled-coil complex in the MD simulations; the in vitro experiments showed incompetent trimeric CNGA3 subunit assembly consistent with abnormal biogenesis of in vivochannels. These newly characterized large animal models not only provide a valuable system for studying cone-specific CNG channel function in health and disease, but also represent prime candidates for proof-of-concept studies of CNGA3 gene replacement therapy for ACHM patients

Self-interrupted synthesis of sterically hindered aliphatic polyamide dendrimers

Hydrolytically and enzymatically stable nanoscale synthetic constructs, with well-defined structures that exhibit antimicrobial activity, offer exciting possibilities for diverse applications in the emerging field of nanomedicine. Herein, we demonstrate that it is the core conformation, rather than periodicity, that ultimately controls the synthesis of sterically hindered aliphatic polyamide dendrimers. The latter self-interrupt at a predictable low generation number due to backfolding of their peripheral groups, which in turn leads to well-defined nanoarchitectures

Computational design of proteins and protein crystals

Protein design is motivated by the desire to study, understand, and exploit the versatile structures and functions capable with proteins. Nature leverages the physico-chemical properties of the amino acids to arrive at highly functional sequences that spontaneously fold, where structural and functional properties are fine-tuned during the course of evolution. Proteins comprise tens to thousands of amino acids, and backbone and side-chain degrees of freedom result in an immense number of possible configurations for a single sequence. Enumerating these myriad sequences is prohibitively expensive even with modern computational power, thus, protein designers have sought methods and/or approximations to reduce the computational burden. One such approximation is the inverse design\u27\u27 approach. In essence, rather than attempting to design a sequence outright that will fold into a particular structure, the designer specifies a single scaffold structure having the particular fold of interest. A sequence is then designed with the goal of stabilizing that scaffold. Removing the scaffold flexibility is often seen as an overly-stringent constraint, leading protein designers to seek new methodologies that incorporate some scaffold flexibility. Throughout this work we take advantage of both the inverted approach and a unique sequence design methodology to arrive at energy landscapes that provide a birds-eye\u27\u27 view of the relationship between sequence and structure. These landscapes are particularly useful in helping to identify structures that are designable\u27\u27 and thus stand a better chance of being experimentally realized. Central to the approach is the recent increase in computational power, allowing us to design entire ensembles (many thousands) of scaffold structures with closely related geometries providing for many possible sequences that fit our design objectives. This is in marked contrast to other design efforts where only a single structure is identified consistent with a single or handful of scaffold structures, providing for little or no recourse when a design fails in the laboratory. Here, we focus on two types of sequence-structure relationships: (i) that of coiled-coil secondary structure where we take advantage of the Crick parameterization to design ensembles of coiled-coil dimers that reversibly fold in the presence of zinc and (ii) that of protein-protein interfaces and quaternary structures, were we optimize the protein sequence in a periodic crystalline\u27\u27 environment consistent with ensembles of unit cells with varying geometries. The sequence-structure approach has proven to be invaluable; we\u27ve successfully designed the first ever protein crystal and have taken that success to the next level by proposing designs that bind and orient non-biological non-linear optical chromophores in high density arrangements. We\u27ve also designed a coiled-coil dimer that folds reversibly dependent on the presence of coordinating zinc ions and has particular advantages for reversible cell-tethering, fluorescence labeling and nanoparticle assembly

Computational design of proteins and protein crystals

Protein design is motivated by the desire to study, understand, and exploit the versatile structures and functions capable with proteins. Nature leverages the physico-chemical properties of the amino acids to arrive at highly functional sequences that spontaneously fold, where structural and functional properties are fine-tuned during the course of evolution. Proteins comprise tens to thousands of amino acids, and backbone and side-chain degrees of freedom result in an immense number of possible configurations for a single sequence. Enumerating these myriad sequences is prohibitively expensive even with modern computational power, thus, protein designers have sought methods and/or approximations to reduce the computational burden. One such approximation is the inverse design\u27\u27 approach. In essence, rather than attempting to design a sequence outright that will fold into a particular structure, the designer specifies a single scaffold structure having the particular fold of interest. A sequence is then designed with the goal of stabilizing that scaffold. Removing the scaffold flexibility is often seen as an overly-stringent constraint, leading protein designers to seek new methodologies that incorporate some scaffold flexibility. Throughout this work we take advantage of both the inverted approach and a unique sequence design methodology to arrive at energy landscapes that provide a birds-eye\u27\u27 view of the relationship between sequence and structure. These landscapes are particularly useful in helping to identify structures that are designable\u27\u27 and thus stand a better chance of being experimentally realized. Central to the approach is the recent increase in computational power, allowing us to design entire ensembles (many thousands) of scaffold structures with closely related geometries providing for many possible sequences that fit our design objectives. This is in marked contrast to other design efforts where only a single structure is identified consistent with a single or handful of scaffold structures, providing for little or no recourse when a design fails in the laboratory. Here, we focus on two types of sequence-structure relationships: (i) that of coiled-coil secondary structure where we take advantage of the Crick parameterization to design ensembles of coiled-coil dimers that reversibly fold in the presence of zinc and (ii) that of protein-protein interfaces and quaternary structures, were we optimize the protein sequence in a periodic crystalline\u27\u27 environment consistent with ensembles of unit cells with varying geometries. The sequence-structure approach has proven to be invaluable; we\u27ve successfully designed the first ever protein crystal and have taken that success to the next level by proposing designs that bind and orient non-biological non-linear optical chromophores in high density arrangements. We\u27ve also designed a coiled-coil dimer that folds reversibly dependent on the presence of coordinating zinc ions and has particular advantages for reversible cell-tethering, fluorescence labeling and nanoparticle assembly

RNA folding using quantum computers.

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots