Hierarchical assembly and modeling of DNA nanotube networks using Y-shaped DNA origami seeds.

DNA nanotechnology offers many means to synthesize custom nanostructured materials from the ground up in a hierarchical fashion. While the assembly of DNA nanostructures from small (nanometer-scale) monomeric components has been studied extensively, how the hierarchical assembly of rigid or semi-flexible units produces multi-micron scale structures is less understood. Here we demonstrate a mechanism for assembling micron-scale semi-flexible DNA nanotubes into extended structures. These nanotubes assemble from nanometer-scale tile monomers into materials via heterogeneous nucleation from rigid, Y-shaped DNA origami seeds to form Y-seeded nanotube architectures. These structures then assemble into networks via nanotube end-to-end joining. We measure the kinetics of network growth and find that the assembly of networks can be approximated by a model of hierarchical assembly that assumes a single joining rate between DNA nanotube ends. Because the number of nucleation sites on Y-seeds and their spatial arrangement can be systematically varied by design, this hierarchical assembly process could be used to form a wide variety of networks and to understand the assembly mechanisms that lead to different types of material architectures at length scales of tens to hundreds of microns.

Growth and site-specific organization of micron-scale biomolecular devices on living mammalian cells.

Mesoscale molecular assemblies on the cell surface, such as cilia and filopodia, integrate information, control transport and amplify signals. Designer cell-surface assemblies could control these cellular functions. Such assemblies could be constructed from synthetic components ex vivo, making it possible to form such structures using modern nanoscale self-assembly and fabrication techniques, and then oriented on the cell surface. Here we integrate synthetic devices, micron-scale DNA nanotubes, with mammalian cells by anchoring them by their ends to specific cell surface receptors. These filaments can measure shear stresses between 0-2 dyn/cm2, a regime important for cell signaling. Nanotubes can also grow while anchored to cells, thus acting as dynamic cell components. This approach to cell surface engineering, in which synthetic biomolecular assemblies are organized with existing cellular architecture, could make it possible to build new types of sensors, machines and scaffolds that can interface with, control and measure properties of cells.

Reconfiguring DNA Nanotube Architectures via Selective Regulation of Terminating Structures.

Molecular assemblies inside cells often undergo structural reconfiguration in response to stimuli to alter their function. Adaptive reconfiguration of cytoskeletal networks, for example, enables cellular shape change, movement, and cargo transport and plays a key role in driving complex processes such as division and differentiation. The cellular cytoskeleton is a self-assembling polymer network composed of simple filaments, so reconfiguration often occurs through the rearrangement of its component filaments' connectivities. DNA nanotubes have emerged as promising building blocks for constructing programmable synthetic analogs of cytoskeletal networks. Nucleating seeds can control when and where nanotubes grow, and capping structures can bind nanotube ends to stop growth. Such seeding and capping structures, collectively called termini, can organize nanotubes into larger architectures. However, these structures cannot be selectively activated or inactivated in response to specific stimuli to rearrange nanotube architectures, a key property of cytoskeletal networks. Here, we demonstrate how selective regulation of the binding affinity of DNA nanotube termini for DNA nanotube monomers or nanotube ends can direct the reconfiguration of nanotube architectures. Using DNA hybridization and strand displacement reactions that specifically activate or inactivate four orthogonal nanotube termini, we demonstrate that nanotube architectures can be reconfigured by selective addition or removal of distinct termini. Finally, we show how terminus activation could be a sensitive detector and amplifier of a DNA sequence signal. These results could enable the development of adaptive and multifunctional materials or diagnostic tools.

Chiral switching in biomineral suprastructures induced by homochiral l-amino acid.

How homochiral l-biomolecules in nature induce a chiral switch in biomineralized architectures is unknown, although chiral switching is common in many calcium carbonate-hardened structures found in marine and terrestrial organisms. We created hierarchically organized, chiral biomineral structures of calcium carbonate, whose chirality can be switched by a single l-enantiomer of an amino acid. The control of this chiral switching involves two stages: a calcium carbonate (vaterite) platelet layer inclination stage, followed by a platelet layer rotation stage, the latter stage being responsible for successional chiral switching events within the biomineralized structures. The morphology of the synthesized chiral vaterite structures remarkably resembles pathologic chiral vaterite otoconia found in the human inner ear. In general, these findings describe how a single-enantiomer amino acid might contribute to biomineral architectures having more than one chirality as is commonly seen in biology, and more specifically, they suggest how pathologic chiral malformations may arise in humans.

Nanostructure, osteopontin, and mechanical properties of calcitic avian eggshell.

Avian (and formerly dinosaur) eggshells form a hard, protective biomineralized chamber for embryonic growth-an evolutionary strategy that has existed for hundreds of millions of years. We show in the calcitic chicken eggshell how the mineral and organic phases organize hierarchically across different length scales and how variation in nanostructure across the shell thickness modifies its hardness, elastic modulus, and dissolution properties. We also show that the nanostructure changes during egg incubation, weakening the shell for chick hatching. Nanostructure and increased hardness were reproduced in synthetic calcite crystals grown in the presence of the prominent eggshell protein osteopontin. These results demonstrate the contribution of nanostructure to avian eggshell formation, mechanical properties, and dissolution.

A Parametric Rosetta Energy Function Analysis with LK Peptides on SAM Surfaces.

Although structures have been determined for many soluble proteins and an increasing number of membrane proteins, experimental structure determination methods are limited for complexes of proteins and solid surfaces. An economical alternative or complement to experimental structure determination is molecular simulation. Rosetta is one software suite that models protein-surface interactions, but Rosetta is normally benchmarked on soluble proteins. For surface interactions, the validity of the energy function is uncertain because it is a combination of independent parameters from energy functions developed separately for solution proteins and mineral surfaces. Here, we assess the performance of the RosettaSurface algorithm and test the accuracy of its energy function by modeling the adsorption of leucine/lysine (LK)-repeat peptides on methyl- and carboxy-terminated self-assembled monolayers (SAMs). We investigated how RosettaSurface predictions for this system compare with the experimental results, which showed that on both surfaces, LK-α peptides folded into helices and LK-ß peptides held extended structures. Utilizing this model system, we performed a parametric analysis of Rosetta's Talaris energy function and determined that adjusting solvation parameters offered improved predictive accuracy. Simultaneously increasing lysine carbon hydrophilicity and the hydrophobicity of the surface methyl head groups yielded computational predictions most closely matching the experimental results. De novo models still should be interpreted skeptically unless bolstered in an integrative approach with experimental data.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

Chiral acidic amino acids induce chiral hierarchical structure in calcium carbonate.

Chirality is ubiquitous in biology, including in biomineralization, where it is found in many hardened structures of invertebrate marine and terrestrial organisms (for example, spiralling gastropod shells). Here we show that chiral, hierarchically organized architectures for calcium carbonate (vaterite) can be controlled simply by adding chiral acidic amino acids (Asp and Glu). Chiral, vaterite toroidal suprastructure having a 'right-handed' (counterclockwise) spiralling morphology is induced by L-enantiomers of Asp and Glu, whereas 'left-handed' (clockwise) morphology is induced by D-enantiomers, and sequentially switching between amino-acid enantiomers causes a switch in chirality. Nanoparticle tilting after binding of chiral amino acids is proposed as a chiral growth mechanism, where a 'mother' subunit nanoparticle spawns a slightly tilted, consequential 'daughter' nanoparticle, which by amplification over various length scales creates oriented mineral platelets and chiral vaterite suprastructures. These findings suggest a molecular mechanism for how biomineralization-related enantiomers might exert hierarchical control to form extended chiral suprastructures.

Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.

Determination of protein structure on mineral surfaces is necessary to understand biomineralization processes toward better treatment of biomineralization diseases and design of novel protein-synthesized materials. To date, limited atomic-resolution data have hindered experimental structure determination for proteins on mineral surfaces. Molecular simulation represents a complementary approach. In this chapter, we review RosettaSurface, a computational structure prediction-based algorithm designed to broadly sample conformational space to identify low-energy structures. We summarize the computational approaches, the published applications, and the new releases of the code in the Rosetta 3 framework. In addition, we provide a protocol capture to demonstrate the practical steps to employ RosettaSurface. As an example, we provide input files and output data analysis for a previously unstudied mineralization protein, osteocalcin. Finally, we summarize ongoing challenges in energy function optimization and conformational searching and suggest that the fusion between experiment and calculation is the best route forward.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

Adding diverse noncanonical backbones to rosetta: enabling peptidomimetic design.

Peptidomimetics are classes of molecules that mimic structural and functional attributes of polypeptides. Peptidomimetic oligomers can frequently be synthesized using efficient solid phase synthesis procedures similar to peptide synthesis. Conformationally ordered peptidomimetic oligomers are finding broad applications for molecular recognition and for inhibiting protein-protein interactions. One critical limitation is the limited set of design tools for identifying oligomer sequences that can adopt desired conformations. Here, we present expansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oligomer scaffolds (noncanonical backbones), oligooxopiperazines, oligo-peptoids, [Formula: see text]-peptides, hydrogen bond surrogate helices and oligosaccharides. This work is complementary to prior additions to model noncanonical protein side chains in ROSETTA. The main purpose of our manuscript is to give a detailed description to current and future developers of how each of these noncanonical backbones was implemented. Furthermore, we provide a general outline for implementation of new backbone types not discussed here. To illustrate the utility of this approach, we describe the first tests of the ROSETTA molecular mechanics energy function in the context of oligooxopiperazines, using quantum mechanical calculations as comparison points, scanning through backbone and side chain torsion angles for a model peptidomimetic. Finally, as an example of a novel design application, we describe the automated design of an oligooxopiperazine that inhibits the p53-MDM2 protein-protein interaction. For the general biological and bioengineering community, several noncanonical backbones have been incorporated into web applications that allow users to freely and rapidly test the presented protocols (http://rosie.rosettacommons.org). This work helps address the peptidomimetic community's need for an automated and expandable modeling tool for noncanonical backbones.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.