Computational design and experimental verification of a symmetric protein homodimer

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Investigation of the impact of nonsynonymous mutations on thyroid peroxidase dimer

Congenital hypothyroidism is one of the most common preventable endocrine disorders associated with thyroid dysgenesis or dyshormonogenesis. Thyroid peroxidase (TPO) gene defect is mainly responsible for dyshormonogenesis; a defect in the thyroid hormone biosynthesis pathway. In Bangladesh, there is limited data regarding the genetic etiology of Congenital Hypothyroidism (CH). The present study investigates the impact of the detected mutations (p.Ala373Ser, and p.Thr725Pro) on the TPO dimer protein. We have performed sequential molecular docking of H2O2 and I- ligands with both monomers of TPO dimer to understand the iodination process in thyroid hormone biosynthesis. Understanding homodimer interactions at the atomic level is a critical challenge to elucidate their biological mechanisms of action. The docking results reveal that mutations in the dimer severely disrupt its catalytic interaction with essential ligands. Molecular dynamics simulation has been performed to validate the docking results, thus realizing the consequence of the mutation in the biological system's mimic. The dynamics results expose that mutations destabilize the TPO dimer protein. Finally, principal component analysis exhibits structural and energy profile discrepancies in wild-type and mutant dimers. The findings of this study highlight that the mutations in TPO protein can critically affect the dimer structure and loss of enzymatic activity is persistent. Other factors also might influence the hormone synthesis pathway, which is under investigation.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ β-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Designed Artificial Protein Heterodimers With Coupled Functions Constructed Using Bio-Orthogonal Chemistry

The formation of protein complexes is central to biology, with oligomeric proteins more prevalent than monomers. The coupling of functionally and even structurally distinct protein units can lead to new functional properties not accessible by monomeric proteins alone. While such complexes are driven by evolutionally needs in biology, the ability to link normally functionally and structurally disparate proteins can lead to new emergent properties for use in synthetic biology and the nanosciences. Here we demonstrate how two disparate proteins, the haem binding helical bundle protein cytochrome b 562 and the β-barrel green fluorescent protein can be combined to form a heterodimer linked together by an unnatural triazole linkage. The complex was designed using computational docking approaches to predict compatible interfaces between the two proteins. Models of the complexes where then used to engineer residue coupling sites in each protein to link them together. Genetic code expansion was used to incorporate azide chemistry in cytochrome b 562 and alkyne chemistry in GFP so that a permanent triazole covalent linkage can be made between the two proteins. Two linkage sites with respect to GFP were sampled. Spectral analysis of the new heterodimer revealed that haem binding and fluorescent protein chromophore properties were retained. Functional coupling was confirmed through changes in GFP absorbance and fluorescence, with linkage site determining the extent of communication between the two proteins. We have thus shown here that is possible to design and build heterodimeric proteins that couple structurally and functionally disparate proteins to form a new complex with new functional properties.

An RNA Triangle with Six Ribozyme Units Can Promote a Trans-Splicing Reaction through Trimerization of Unit Ribozyme Dimers

Ribozymes are catalytic RNAs that are attractive platforms for the construction of nanoscale objects with biological functions. We designed a dimeric form of the Tetrahymena group I ribozyme as a unit structure in which two ribozymes were connected in a tail-to-tail manner with a linker element. We introduced a kink-turn motif as a bent linker element of the ribozyme dimer to design a closed trimer with a triangular shape. The oligomeric states of the resulting ribozyme dimers (kUrds) were analyzed biochemically and observed directly by atomic force microscopy (AFM). Formation of kUrd oligomers also triggered trans-splicing reactions, which could be monitored with a reporter system to yield a fluorescent RNA aptamer as the trans-splicing product.

Protein homodimers structure prediction based on deep neural network

Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimental techniques to identify the structure of protein complex require enormous amount of time and resources, and each method has its own limitations. Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. Building on the idea of this approach, we developed an algorithm to model the 3D structure of homodimer based on deep learning. It consists of two major steps: at the first step a protein complex contact map is predicted with the deep convolutional neural network, and the second stage is used to predict 3D structure of homodimer based on obtained contact map and optimization procedure. The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. The suggested approach was tested and validated on a dataset of protein homodimers from Protein Data Bank (PDB). The developed procedure could be also used for evaluating protein homodimer models as one of the stages in drug compounds developing.

Better together: building protein oligomers naturally and by design

Protein oligomers are more common in nature than monomers, with dimers being the most prevalent final structural state observed in known structures. From a biological perspective, this makes sense as it conserves vital molecular resources that may be wasted simply by generating larger single polypeptide units, and allows new features such as cooperativity to emerge. Taking inspiration from nature, protein designers and engineers are now building artificial oligomeric complexes using a variety of approaches to generate new and useful supramolecular protein structures. Oligomerisation is thus offering a new approach to sample structure and function space not accessible through simply tinkering with monomeric proteins.

Structure-Guided Design of a Peptide Lock for Modular Peptide Binders

Peptides play an important role in intermolecular interactions and are frequent analytes in diagnostic assays, also as unstructured, linear epitopes in whole proteins. Yet, due to the many different sequence possibilities even for short peptides, classical selection of binding proteins from a library, one at a time, is not scalable to proteomes. However, moving away from selection to a rational assembly of preselected modules binding to predefined linear epitopes would split the problem into smaller parts. These modules could then be reassembled in any desired order to bind to, in principle, arbitrary sequences, thereby circumventing any new rounds of selection. Designed Armadillo repeat proteins (dArmRPs) are modular, and they do bind elongated peptides in a modular way. Their consensus sequence carries pockets that prefer arginine and lysine. In our quest to select pockets for all amino acid side chains, we had discovered that repetitive sequences can lead to register shifts and peptide flipping during selections from libraries, hindering the selection of new binding specificities. To solve this problem, we now created an orthogonal binding specificity by a combination of grafting from β-catenin, computational design and mutual optimization of the pocket and the bound peptide. We have confirmed the design and the desired interactions by X-ray structure determination. Furthermore, we could confirm the absence of sliding in solution by a single-molecule Förster resonance energy transfer. The new pocket could be moved from the N-terminus of the protein to the middle, retaining its properties, further underlining the modularity of the system.

Significant expansion and red-shifting of fluorescent protein chromophore determined through computational design and genetic code expansion

ABSTRACT

Fluorescent proteins (FPs) with emission wavelengths in the far-red and infrared regions of the spectrum provide powerful tools for deep-tissue and super-resolution imaging. The development of red-shifted FPs has evoked widespread interest and continuous engineering efforts. In this article, based on a computational design and genetic code expansion, we report a rational approach to significantly expand and red-shift the chromophore of green fluorescent protein (GFP). We applied computational calculations to predict the excitation and emission wavelengths of a FP chromophore harboring unnatural amino acids (UAA) and identify in silico an appropriate UAA, 2-amino-3-(6-hydroxynaphthalen-2-yl)propanoic acid (naphthol-Ala). Our methodology allowed us to formulate a GFP variant (cpsfGFP-66-Naphthol-Ala) with red-shifted absorbance and emission spectral maxima exceeding 60 and 130 nm, respectively, compared to those of GFP. The GFP chromophore is formed through autocatalytic post-translational modification to generate a planar 4-(p-hydroxybenzylidene)-5-imidazolinone chromophore. We solved the crystal structure of cpsfGFP-66-naphthol-Ala at 1.3 Å resolution and demonstrated the formation of a much larger conjugated π-system when the phenol group is replaced by naphthol. These results explain the significant red-shifting of the excitation and emission spectra of cpsfGFP-66-naphthol-Ala.

GRAPHICAL ABSTRACT

Creation of artificial protein-protein interactions using α-helices as interfaces

Designing novel protein-protein interactions (PPIs) with high affinity is a challenging task. Directed evolution, a combination of randomization of the gene for the protein of interest and selection using a display technique, is one of the most powerful tools for producing a protein binder. However, the selected proteins often bind to the target protein at an undesired surface. More problematically, some selected proteins bind to their targets even though they are unfolded. Current state-of-the-art computational design methods have successfully created novel protein binders. These computational methods have optimized the non-covalent interactions at interfaces and thus produced artificial protein complexes. However, to date there are only a limited number of successful examples of computationally designed de novo PPIs. De novo design of coiled-coil proteins has been extensively performed and, therefore, a large amount of knowledge of the sequence-structure relationship of coiled-coil proteins has been accumulated. Taking advantage of this knowledge, de novo design of inter-helical interactions has been used to produce artificial PPIs. Here, we review recent progress in the in silico design and rational design of de novo PPIs and the use of α-helices as interfaces.

Hierarchical design of artificial proteins and complexes toward synthetic structural biology

In multiscale structural biology, synthetic approaches are important to demonstrate biophysical principles and mechanisms underlying the structure, function, and action of bio-nanomachines. A central goal of "synthetic structural biology" is the design and construction of artificial proteins and protein complexes as desired. In this paper, I review recent remarkable progress of an array of approaches for hierarchical design of artificial proteins and complexes that signpost the path forward toward synthetic structural biology as an emerging interdisciplinary field. Topics covered include combinatorial and protein-engineering approaches for directed evolution of artificial binding proteins and membrane proteins, binary code strategy for structural and functional de novo proteins, protein nanobuilding block strategy for constructing nano-architectures, protein-metal-organic frameworks for 3D protein complex crystals, and rational and computational approaches for design/creation of artificial proteins and complexes, novel protein folds, ideal/optimized protein structures, novel binding proteins for targeted therapeutics, and self-assembling nanomaterials. Protein designers and engineers look toward a bright future in synthetic structural biology for the next generation of biophysics and biotechnology.

Cyclic oligomer design with de novo αβ-proteins

We have previously shown that monomeric globular αβ-proteins can be designed de novo with considerable control over topology, size, and shape. In this paper, we investigate the design of cyclic homo-oligomers from these starting points. We experimented with both keeping the original monomer backbones fixed during the cyclic docking and design process, and allowing the backbone of the monomer to conform to that of adjacent subunits in the homo-oligomer. The latter flexible backbone protocol generated designs with shape complementarity approaching that of native homo-oligomers, but experimental characterization showed that the fixed backbone designs were more stable and less aggregation prone. Designed C2 oligomers with β-strand backbone interactions were structurally confirmed through x-ray crystallography and small-angle X-ray scattering (SAXS). In contrast, C3-C5 designed homo-oligomers with primarily nonpolar residues at interfaces all formed a range of oligomeric states. Taken together, our results suggest that for homo-oligomers formed from globular building blocks, improved structural specificity will be better achieved using monomers with increased shape complementarity and with more polar interfaces.

A critical analysis of computational protein design with sparse residue interaction graphs

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.

Computational structure-based protein design algorithms have successfully redesigned proteins to fold and bind target substrates in vitro, and even in vivo. Because the complexity of a computational design increases dramatically with the number of mutable residues, many design algorithms employ cutoffs (distance or energy) to neglect some pairwise residue interactions, thereby reducing the effective search space and computational cost. However, the energies neglected by such cutoffs can add up, which may have nontrivial effects on the designed sequence and its function. To study the effects of using cutoffs on protein design, we computed the optimal sequence both with and without cutoffs, and showed that neglecting long-range interactions can significantly change the computed conformation and sequence. Designs on proteins with experimentally measured thermostability showed the benefits of computing the optimal sequences (and their conformations), both with and without cutoffs, efficiently and accurately. Therefore, we also showed that a provable, ensemble-based algorithm can efficiently compute the optimal conformation and sequence, both with and without applying cutoffs, by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine cutoffs with provable, ensemble-based algorithms to reap the computational efficiency of cutoffs while avoiding their potential inaccuracies.

Specificity and stability of transient protein-protein interactions

Remarkable features that are achieved in a protein-protein complex to precise levels are stability and specificity. Deviation from the normal levels of specificity and stability, which is often caused by mutations, could result in disease conditions. Chemical nature, 3-D arrangement and dynamics of interface residues code for both specificity and stability. This article reviews roles of interfacial residues in transient protein-protein complexes. It is proposed that aside from hotspot residues conferring stability to the complex, a small set of 'rigid' residues at the interface that maintain conformation between complexed and uncomplexed forms, play a major role in conferring specificity. Exceptionally, 'super hotspot' residues, which confer both stability and specificity, are attractive sites for interaction with small molecule inhibitors.

Computational design of self-assembling cyclic protein homo-oligomers

Self-assembling cyclic protein homo-oligomers play important roles in biology and the ability to generate custom homo-oligomeric structures could enable new approaches to probe biological function. Here we report a general approach to design cyclic homo-oligomers that employs a new residue pair transform method for assessing the design ability of a protein-protein interface. This method is sufficiently rapid to enable systematic enumeration of cyclically docked arrangements of a monomer followed by sequence design of the newly formed interfaces. We use this method to design interfaces onto idealized repeat proteins that direct their assembly into complexes that possess cyclic symmetry. Of 96 designs that were experimentally characterized, 21 were found to form stable monodisperse homo-oligomers in solution, and 15 (4 homodimers, 6 homotrimers, 6 homotetramers and 1 homopentamer) had solution small angle X-ray scattering data consistent with the design models. X-ray crystal structures were obtained for five of the designs and each of these were shown to be very close to their design model.

Computational protein design for given backbone: recent progresses in general method-related aspects

To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.

The Folding of de Novo Designed Protein DS119 via Molecular Dynamics Simulations

As they are not subjected to natural selection process, de novo designed proteins usually fold in a manner different from natural proteins. Recently, a de novo designed mini-protein DS119, with a βαβ motif and 36 amino acids, has folded unusually slowly in experiments, and transient dimers have been detected in the folding process. Here, by means of all-atom replica exchange molecular dynamics (REMD) simulations, several comparably stable intermediate states were observed on the folding free-energy landscape of DS119. Conventional molecular dynamics (CMD) simulations showed that when two unfolded DS119 proteins bound together, most binding sites of dimeric aggregates were located at the N-terminal segment, especially residues 5-10, which were supposed to form β-sheet with its own C-terminal segment. Furthermore, a large percentage of individual proteins in the dimeric aggregates adopted conformations similar to those in the intermediate states observed in REMD simulations. These results indicate that, during the folding process, DS119 can easily become trapped in intermediate states. Then, with diffusion, a transient dimer would be formed and stabilized with the binding interface located at N-terminals. This means that it could not quickly fold to the native structure. The complicated folding manner of DS119 implies the important influence of natural selection on protein-folding kinetics, and more improvement should be achieved in rational protein design.

Designed multi-stranded heme binding β-sheet peptides in membrane

Designed peptides demonstrating well-defined structures and functioning in membrane environment are of significant interest in developing novel proteins for membrane active biological processes including enzymes, electron transfer, ion channels and energy conversion. Heme proteins' ability to carry out multiple functions in nature has inspired the design of several helical heme binding peptides and proteins soluble in water and also recently in membrane. Naturally occurring β-sheet proteins are both water and membrane soluble, and are known to bind heme, however, designed heme binding β-sheet proteins are yet to be reported, plausibly because of the complex folding and difficulty in introducing heme binding sites in the β-sheet structures. Here, we describe the design, NMR structures and biochemical functional characterization of four stranded and six stranded membrane soluble β-sheet peptides that bind heme and di-heme, respectively. The designed peptides contain either DP-G or DP-DA residues for the nucleation of β-turns intended to stabilize multi-stranded β-sheet topologies and ligate heme with bis-His coordination between adjacent antiparallel β-strands. Furthermore, we have optimized a high affinity heme binding pocket, Kd ∼ nM range, in the adjacent β-strands by utilizing a series of four stranded β-sheet peptides employing β- and ω-amino acids. We find that there is a progressive increase in cofactor binding affinity in the designed peptides with the alkyl chain length of ω-amino acids. Notably, the six stranded β-sheet peptide binds two molecules of heme in a cooperative fashion. The designed peptides perform peroxidase activity with varying ability and efficiently carried out electron transfer with membrane associated protein cytochrome c. The current study demonstrates the designing of functional β-sheet proteins in a membrane environment and expands the repertoire of heme protein design.

Protein Engineering by Combined Computational and In Vitro Evolution Approaches

Two alternative strategies are commonly used to study protein-protein interactions (PPIs) and to engineer protein-based inhibitors. In one approach, binders are selected experimentally from combinatorial libraries of protein mutants that are displayed on a cell surface. In the other approach, computational modeling is used to explore an astronomically large number of protein sequences to select a small number of sequences for experimental testing. While both approaches have some limitations, their combination produces superior results in various protein engineering applications. Such applications include the design of novel binders and inhibitors, the enhancement of affinity and specificity, and the mapping of binding epitopes. The combination of these approaches also aids in the understanding of the specificity profiles of various PPIs.

Protein sequence design and its applications

Design of proteins has far-reaching potentials in diverse areas that span repurposing of the protein scaffold for reactions and substrates that they were not naturally meant for, to catching a glimpse of the ephemeral proteins that nature might have sampled during evolution. These non-natural proteins, either in synthesized or virtual form have opened the scope for the design of entities that not only rival their natural counterparts but also offer a chance to visualize the protein space continuum that might help to relate proteins and understand their associations. Here, we review the recent advances in protein engineering and design, in multiple areas, with a view to drawing attention to their future potential.

Metal-Directed Design of Supramolecular Protein Assemblies

Owing to their central roles in cellular signaling, construction, and biochemistry, protein-protein interactions (PPIs) and protein self-assembly have become a major focus of molecular design and synthetic biology. In order to circumvent the complexity of constructing extensive noncovalent interfaces, which are typically involved in natural PPIs and protein self-assembly, we have developed two design strategies, metal-directed protein self-assembly (MDPSA) and metal-templated interface redesign (MeTIR). These strategies, inspired by both the proposed evolutionary roles of metals and their prevalence in natural PPIs, take advantage of the favorable properties of metal coordination (bonding strength, directionality, and reversibility) to guide protein self-assembly with minimal design and engineering. Using a small, monomeric protein (cytochrome cb562) as a model building block, we employed MDPSA and MeTIR to create a diverse array of functional supramolecular architectures which range from structurally tunable oligomers to metalloprotein complexes that can properly self-assemble in living cells into novel metalloenzymes. The design principles and strategies outlined herein should be readily applicable to other protein systems with the goal of creating new PPIs and protein assemblies with structures and functions not yet produced by natural evolution.

Computational design of co-assembling protein-DNA nanowires

Biomolecular self-assemblies are of great interest to nanotechnologists because of their functional versatility and their biocompatibility. Over the past decade, sophisticated single-component nanostructures composed exclusively of nucleic acids, peptides and proteins have been reported, and these nanostructures have been used in a wide range of applications, from drug delivery to molecular computing. Despite these successes, the development of hybrid co-assemblies of nucleic acids and proteins has remained elusive. Here we use computational protein design to create a protein-DNA co-assembling nanomaterial whose assembly is driven via non-covalent interactions. To achieve this, a homodimerization interface is engineered onto the Drosophila Engrailed homeodomain (ENH), allowing the dimerized protein complex to bind to two double-stranded DNA (dsDNA) molecules. By varying the arrangement of protein-binding sites on the dsDNA, an irregular bulk nanoparticle or a nanowire with single-molecule width can be spontaneously formed by mixing the protein and dsDNA building blocks. We characterize the protein-DNA nanowire using fluorescence microscopy, atomic force microscopy and X-ray crystallography, confirming that the nanowire is formed via the proposed mechanism. This work lays the foundation for the development of new classes of protein-DNA hybrid materials. Further applications can be explored by incorporating DNA origami, DNA aptamers and/or peptide epitopes into the protein-DNA framework presented here.