De novo design of a transmembrane Zn²⁺-transporting four-helix bundle

Design principles of protein switches

Protein switches perform essential roles in many biological processes and are exciting targets for de novo protein design, which aims to produce proteins of arbitrary shape and functionality. However, the biophysical requirements for switch function - multiple conformational states, fine-tuned energetics, and stimuli-responsiveness - pose a formidable challenge for design by computation (or intuition). A variety of methods have been developed toward tackling this challenge, usually taking inspiration from the wealth of sequence and structural information available for naturally occurring protein switches. More recently, modular switches have been designed computationally, and new methods have emerged for sampling unexplored structure space, providing promising new avenues toward the generation of purpose-built switches and de novo signaling systems for cellular engineering.

A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?

Principles and Methods in Computational Membrane Protein Design

After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.

Constructing ion channels from water-soluble α-helical barrels

The design of peptides that assemble in membranes to form functional ion channels is challenging. Specifically, hydrophobic interactions must be designed between the peptides and at the peptide-lipid interfaces simultaneously. Here, we take a multi-step approach towards this problem. First, we use rational de novo design to generate water-soluble α-helical barrels with polar interiors, and confirm their structures using high-resolution X-ray crystallography. These α-helical barrels have water-filled lumens like those of transmembrane channels. Then, we modify the sequences to facilitate their insertion into lipid bilayers. Single-channel electrical recordings and fluorescent imaging of the peptides in membranes show monodisperse, cation-selective channels of unitary conductance. Surprisingly, however, an X-ray structure solved from lipidic cubic phase for one peptide reveals an alternative state with tightly packed helices and a constricted channel. To reconcile these observations, we perform computational analyses to compare the properties of possible different states of the peptide.

Molecular flexibility in computational protein design: an algorithmic perspective

Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

How Coiled-Coil Assemblies Accommodate Multiple Aromatic Residues

Rational protein design requires understanding the contribution of each amino acid to a targeted protein fold. For a subset of protein structures, namely, α-helical coiled coils (CCs), knowledge is sufficiently advanced to allow the rational de novo design of many structures, including entirely new protein folds. Current CC design rules center on using aliphatic hydrophobic residues predominantly to drive the folding and assembly of amphipathic α helices. The consequences of using aromatic residues-which would be useful for introducing structural probes, and binding and catalytic functionalities-into these interfaces are not understood. There are specific examples of designed CCs containing such aromatic residues, e.g., phenylalanine-rich sequences, and the use of polar aromatic residues to make buried hydrogen-bond networks. However, it is not known generally if sequences rich in tyrosine can form CCs, or what CC assemblies these would lead to. Here, we explore tyrosine-rich sequences in a general CC-forming background and resolve new CC structures. In one of these, an antiparallel tetramer, the tyrosine residues are solvent accessible and pack at the interface between the core and the surface. In another more complex structure, the residues are buried and form an extended hydrogen-bond network.

Bottom-up de novo design of functional proteins with complex structural features

De novo protein design has enabled the creation of new protein structures. However, the design of functional proteins has proved challenging, in part due to the difficulty of transplanting structurally complex functional sites to available protein structures. Here, we used a bottom-up approach to build de novo proteins tailored to accommodate structurally complex functional motifs. We applied the bottom-up strategy to successfully design five folds for four distinct binding motifs, including a bifunctionalized protein with two motifs. Crystal structures confirmed the atomic-level accuracy of the computational designs. These de novo proteins were functional as components of biosensors to monitor antibody responses and as orthogonal ligands to modulate synthetic signaling receptors in engineered mammalian cells. Our work demonstrates the potential of bottom-up approaches to accommodate complex structural motifs, which will be essential to endow de novo proteins with elaborate biochemical functions, such as molecular recognition or catalysis.

Design of buried charged networks in artificial proteins

Soluble proteins are universally packed with a hydrophobic core and a polar surface that drive the protein folding process. Yet charged networks within the central protein core are often indispensable for the biological function. Here, we show that natural buried ion-pairs are stabilised by amphiphilic residues that electrostatically shield the charged motif from its surroundings to gain structural stability. To explore this effect, we build artificial proteins with buried ion-pairs by combining directed computational design and biophysical experiments. Our findings illustrate how perturbation in charged networks can introduce structural rearrangements to compensate for desolvation effects. We validate the physical principles by resolving high-resolution atomic structures of the artificial proteins that are resistant towards unfolding at extreme temperatures and harsh chemical conditions. Our findings provide a molecular understanding of functional charged networks and how point mutations may alter the protein’s conformational landscape.

Buried charged networks in proteins are often important for their biological functionality and are believed to destabilise the protein fold. Here, the authors combine computational design, MD simulations, biophysical experiments, NMR and X-ray crystallography to design and characterise artificial 4α-helical proteins with buried charged elements. They analyse their conformational landscapes and observe that the ion-pairs are stabilised by amphiphilic residues that electrostatically shield the charged motif, which increases structural stability.

Self-assembly and regulation of protein cages from pre-organised coiled-coil modules

Coiled-coil protein origami (CCPO) is a modular strategy for the de novo design of polypeptide nanostructures. CCPO folds are defined by the sequential order of concatenated orthogonal coiled-coil (CC) dimer-forming peptides, where a single-chain protein is programmed to fold into a polyhedral cage. Self-assembly of CC-based nanostructures from several chains, similarly as in DNA nanotechnology, could facilitate the design of more complex assemblies and the introduction of functionalities. Here, we show the design of a de novo triangular bipyramid fold comprising 18 CC-forming segments and define the strategy for the two-chain self-assembly of the bipyramidal cage from asymmetric and pseudo-symmetric pre-organised structural modules. In addition, by introducing a protease cleavage site and masking the interfacial CC-forming segments in the two-chain bipyramidal cage, we devise a proteolysis-mediated conformational switch. This strategy could be extended to other modular protein folds, facilitating the construction of dynamic multi-chain CC-based complexes.

Coiled-coil protein origami is a strategy for the de novo design of polypeptide nanostructures based on coiled-coil dimer forming peptides, where a single chain protein folds into a polyhedral cage. Here, the authors design a single-chain triangular bipyramid and also demonstrate that the bipyramid can be self-assembled as a heterodimeric complex, comprising pre-defined subunits.

A Biomimetic DNA-Based Membrane Gate for Protein-Controlled Transport of Cytotoxic Drugs

Chemistry is ideally placed to replicate biomolecular structures with tuneable building materials. Of particular interest are molecular nanopores, which transport cargo across membranes, as in DNA sequencing. Advanced nanopores control transport in response to triggers, but this cannot be easily replicated with biogenic proteins. Here we use DNA nanotechnology to build a synthetic molecular gate that opens in response to a specific protein. The gate self-assembles from six DNA strands to form a bilayer-spanning pore, and a lid strand comprising a protein-binding DNA aptamer to block the channel entrance. Addition of the trigger protein, thrombin, selectively opens the gate and enables a 330-fold increase inw the transport rate of small-molecule cargo. The molecular gate incorporates in delivery vesicles to controllably release enclosed cytotoxic drugs and kill eukaryotic cells. The generically designed gate may be applied in biomedicine, biosensing or for building synthetic cells.

Biological Nanopores: Engineering on Demand

Nanopore-based sensing is a powerful technique for the detection of diverse organic and inorganic molecules, long-read sequencing of nucleic acids, and single-molecule analyses of enzymatic reactions. Selected from natural sources, protein-based nanopores enable rapid, label-free detection of analytes. Furthermore, these proteins are easy to produce, form pores with defined sizes, and can be easily manipulated with standard molecular biology techniques. The range of possible analytes can be extended by using externally added adapter molecules. Here, we provide an overview of current nanopore applications with a focus on engineering strategies and solutions.

Membrane Protein Engineering with Rosetta

Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.

Molecular design of stapled pentapeptides as building blocks of self-assembled coiled coil-like fibers

Peptide self-assembly inspired by natural superhelical coiled coils has been actively pursued but remains challenging due to limited helicity of short peptides. Side chain stapling can strengthen short helices but is unexplored in design of self-assembled helical nanofibers as it is unknown how staples could be adapted to coiled coil architecture. Here, we demonstrate the feasibility of this design for pentapeptides using a computational method capable of predicting helicity and fiber-forming tendency of stapled peptides containing noncoded amino acids. Experiments showed that the best candidates, which carried an aromatically substituted staple and phenylalanine analogs, displayed exceptional helicity and assembled into nanofibers via specific head-to-tail hydrogen bonding and packing between staple and noncoded side chains. The fibers exhibited sheet-of-helix structures resembling the recently found collapsed coiled coils whose formation was sensitive to side chain flexibility. This study expands the chemical space of coiled coil assemblies and provides guidance for their design.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

Building Synthetic Transmembrane Peptide Pores

Membrane protein pores have demonstrated applications in nanopore technology. Previous studies have mostly focused on β-barrel protein pores, whereas α-helix-based transmembrane protein pores are rarely explored in nanopore applications. Here, we developed a synthetic transmembrane peptide pore built entirely from short synthetic α-helical peptides. We examined the formation of a stable uniform ion-selective pore in single-channel electrical recordings. Furthermore, we show that cyclodextrins (CDs) block the peptide pores and determine the kinetics of CD binding and translocation. We suggest that such designed synthetic transmembrane pores will be useful for several applications in biotechnology, including stochastic sensing.

Allosteric cooperation in a de novo-designed two-domain protein

We describe the de novo design of an allosterically regulated protein, which comprises two tightly coupled domains. One domain is based on the DF (Due Ferri in Italian or two-iron in English) family of de novo proteins, which have a diiron cofactor that catalyzes a phenol oxidase reaction, while the second domain is based on PS1 (Porphyrin-binding Sequence), which binds a synthetic Zn-porphyrin (ZnP). The binding of ZnP to the original PS1 protein induces changes in structure and dynamics, which we expected to influence the catalytic rate of a fused DF domain when appropriately coupled. Both DF and PS1 are four-helix bundles, but they have distinct bundle architectures. To achieve tight coupling between the domains, they were connected by four helical linkers using a computational method to discover the most designable connections capable of spanning the two architectures. The resulting protein, DFP1 (Due Ferri Porphyrin), bound the two cofactors in the expected manner. The crystal structure of fully reconstituted DFP1 was also in excellent agreement with the design, and it showed the ZnP cofactor bound over 12 Å from the dimetal center. Next, a substrate-binding cleft leading to the diiron center was introduced into DFP1. The resulting protein acts as an allosterically modulated phenol oxidase. Its Michaelis-Menten parameters were strongly affected by the binding of ZnP, resulting in a fourfold tighter Km and a 7-fold decrease in kcat These studies establish the feasibility of designing allosterically regulated catalytic proteins, entirely from scratch.

Deep Learning in Protein Structural Modeling and Design

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.

A Biomimetic DNA-Based Membrane Gate for Protein-Controlled Transport of Cytotoxic Drugs

Chemistry is ideally placed to replicate biomolecular structures with tuneable building materials. Of particular interest are molecular nanopores, which transport cargo across membranes, as in DNA sequencing. Advanced nanopores control transport in response to triggers, but this cannot be easily replicated with biogenic proteins. Here we use DNA nanotechnology to build a synthetic molecular gate that opens in response to a specific protein. The gate self‐assembles from six DNA strands to form a bilayer‐spanning pore, and a lid strand comprising a protein‐binding DNA aptamer to block the channel entrance. Addition of the trigger protein, thrombin, selectively opens the gate and enables a 330‐fold increase inw the transport rate of small‐molecule cargo. The molecular gate incorporates in delivery vesicles to controllably release enclosed cytotoxic drugs and kill eukaryotic cells. The generically designed gate may be applied in biomedicine, biosensing or for building synthetic cells.

Designed alpha-helical barrels for charge-selective peptide translocation

Synthetic alpha-helix based pores for selective sensing of peptides have not been characterized previously. Here, we report large transmembrane pores, pPorA formed from short synthetic alpha-helical peptides of tunable conductance and selectivity for single-molecule sensing of peptides. We quantified the selective translocation kinetics of differently charged cationic and anionic peptides through these synthetic pores at single-molecule resolution. The charged peptides are electrophoretically pulled into the pores resulting in an increase in the dissociation rate with the voltage indicating successful translocation of peptides. More specifically, we elucidated the charge pattern lining the pore lumen and the orientation of the pores in the membrane based on the asymmetry in the peptide-binding kinetics. The salt and pH-dependent measurements confirm the electrostatic dominance and charge selectivity in controlling target peptide interaction with the pores. Remarkably, we tuned the selectivity of the pores to charged peptides by modifying the charge composition of the pores, thus establishing the molecular and electrostatic basis of peptide translocation. We suggest that these synthetic pores that selectively conduct specific ions and biomolecules are advantageous for nanopore proteomics analysis and synthetic nanobiotechnology applications.

RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network

The ability to perform de novo protein design will allow researchers to expand the variety of available proteins. By designing synthetic structures computationally, they can utilise more structures than those available in the Protein Data Bank, design structures that are not found in nature, or direct the design of proteins to acquire a specific desired structure. While some researchers attempt to design proteins from first physical and thermodynamic principals, we decided to attempt to test whether it is possible to perform de novo helical protein design of just the backbone statistically using machine learning by building a model that uses a long short-term memory (LSTM) architecture. The LSTM model used only the φ and ψ angles of each residue from an augmented dataset of only helical protein structures. Though the network’s generated backbone structures were not perfect, they were idealised and evaluated post generation where the non-ideal structures were filtered out and the adequate structures kept. The results were successful in developing a logical, rigid, compact, helical protein backbone topology. This paper is a proof of concept that shows it is possible to generate a novel helical backbone topology using an LSTM neural network architecture using only the φ and ψ angles as features. The next step is to attempt to use these backbone topologies and sequence design them to form complete protein structures.

Metal-Templated Design of Chemically Switchable Protein Assemblies with High-Affinity Coordination Sites

To mimic a hypothetical pathway for protein evolution, we previously tailored a monomeric protein (cyt cb₅₆₂ ) for metal-mediated self-assembly, followed by re-design of the resulting oligomers for enhanced stability and metal-based functions. We show that a single hydrophobic mutation on the cyt cb₅₆₂ surface drastically alters the outcome of metal-directed oligomerization to yield a new trimeric architecture, (TriCyt1)_3. This nascent trimer was redesigned into second and third-generation variants (TriCyt2)₃ and (TriCyt3)₃ with increased structural stability and preorganization for metal coordination. The three TriCyt variants combined furnish a unique platform to 1) provide tunable coupling between protein quaternary structure and metal coordination, 2) enable the construction of metal/pH-switchable protein oligomerization motifs, and 3) generate a robust metal coordination site that can coordinate all mid-to-late first-row transition-metal ions with high affinity.

Small-residue packing motifs modulate the structure and function of a minimal de novo membrane protein

Alpha-helical integral membrane proteins contain conserved sequence motifs that are known to be important in helix packing. These motifs are a promising starting point for the construction of artificial proteins, but their potential has not yet been fully explored. Here, we study the impact of introducing a common natural helix packing motif to the transmembrane domain of a genetically-encoded and structurally dynamic de novo membrane protein. The resulting construct is an artificial four-helix bundle with lipophilic regions that are defined only by the amino acids L, G, S, A and W. This minimal proto-protein could be recombinantly expressed by diverse prokaryotic and eukaryotic hosts and was found to co-sediment with cellular membranes. The protein could be extracted and purified in surfactant micelles and was monodisperse and stable in vitro, with sufficient structural definition to support the rapid binding of a heme cofactor. The reduction in conformational diversity imposed by this design also enhances the nascent peroxidase activity of the protein-heme complex. Unexpectedly, strains of Escherichia coli expressing this artificial protein specifically accumulated zinc protoporphyrin IX, a rare cofactor that is not used by natural metalloenzymes. Our results demonstrate that simple sequence motifs can rigidify elementary membrane proteins, and that orthogonal artificial membrane proteins can influence the cofactor repertoire of a living cell. These findings have implications for rational protein design and synthetic biology.

Computational design of transmembrane pores

Protein pores play key roles in fundamental biological processes¹ and biotechnological applications such as DNA nanopore sequencing^2–4, and hence the design of pore-containing proteins is of considerable scientific and biotechnological interest. Synthetic amphiphilic peptides have been found to form ion channels^5,6, and there have been recent advances in de novo membrane protein design^7,8 and in redesigning naturally occurring channel-containing proteins^9,10. However, the de novo design of stable, well-defined transmembrane protein pores capable of conducting ions selectively or large enough to allow passage of small-molecule fluorophores remains an outstanding challenge^11,12. Here, we report the computational design of protein pores formed by two concentric rings of ɑ-helices that are stable and mono-disperse in both water-soluble and transmembrane forms. Crystal structures of the water-soluble forms of a 12 helical and a 16 helical pore are close to the computational design models. Patch-clamp electrophysiology experiments show that the transmembrane form of the 12-helix pore expressed in insect cells allows passage of ions across the membrane with high selectivity for potassium over sodium, which is blocked by specific chemical modification at the pore entrance. The transmembrane form of the 16-helix pore, but not the 12-helix pore, allows passage of biotinylated Alexa Fluor 488 when incorporated into liposomes using in vitro protein synthesis. A cryo-EM structure of the 16-helix transmembrane pore closely matches the design model. The ability to produce structurally and functionally well-defined transmembrane pores opens the door to the creation of designer pores for a wide variety of applications.

Recent Advances in Liposome-Based Molecular Robots

A molecular robot is a microorganism-imitating micro robot that is designed from the molecular level and constructed by bottom-up approaches. As with conventional robots, molecular robots consist of three essential robotics elements: control of intelligent systems, sensors, and actuators, all integrated into a single micro compartment. Due to recent developments in microfluidic technologies, DNA nanotechnologies, synthetic biology, and molecular engineering, these individual parts have been developed, with the final picture beginning to come together. In this review, we describe recent developments of these sensors, actuators, and intelligence systems that can be applied to liposome-based molecular robots. First, we explain liposome generation for the compartments of molecular robots. Next, we discuss the emergence of robotics functions by using and functionalizing liposomal membranes. Then, we discuss actuators and intelligence via the encapsulation of chemicals into liposomes. Finally, the future vision and the challenges of molecular robots are described.

Molecular architecture and activation of the insecticidal protein Vip3Aa from Bacillus thuringiensis

Bacillus thuringiensis Vip3 (Vegetative Insecticidal Protein 3) toxins are widely used in biotech crops to control Lepidopteran pests. These proteins are produced as inactive protoxins that need to be activated by midgut proteases to trigger cell death. However, little is known about their three-dimensional organization and activation mechanism at the molecular level. Here, we have determined the structures of the protoxin and the protease-activated state of Vip3Aa at 2.9 Å using cryo-electron microscopy. The reconstructions show that the protoxin assembles into a pyramid-shaped tetramer with the C-terminal domains exposed to the solvent and the N-terminal region folded into a spring-loaded apex that, after protease activation, drastically remodels into an extended needle by a mechanism akin to that of influenza haemagglutinin. These results provide the molecular basis for Vip3 activation and function, and serves as a strong foundation for the development of more efficient insecticidal proteins.

Synthetic biology approaches to dissecting linear motor protein function: towards the design and synthesis of artificial autonomous protein walkers

Molecular motors and machines are essential for all cellular processes that together enable life. Built from proteins with a wide range of properties, functionalities and performance characteristics, biological motors perform complex tasks and can transduce chemical energy into mechanical work more efficiently than human-made combustion engines. Sophisticated studies of biological protein motors have provided many structural and biophysical insights and enabled the development of models for motor function. However, from the study of highly evolved, biological motors, it remains difficult to discern detailed mechanisms, for example, about the relative role of different force generation mechanisms, or how information is communicated across a protein to achieve the necessary coordination. A promising, complementary approach to answering these questions is to build synthetic protein motors from the bottom up. Indeed, much effort has been invested in functional protein design, but so far, the "holy grail" of designing and building a functional synthetic protein motor has not been realized. Here, we review the progress made to date, and we put forward a roadmap for achieving the aim of constructing the first artificial, autonomously running protein motor. Specifically, we propose to break down the task into (i) enzymatic control of track binding, (ii) the engineering of asymmetry and (iii) the engineering of allosteric control for internal communication. We also propose specific approaches for solving each of these challenges.

Biomolecular complex viewed by dynamic nuclear polarization solid-state NMR spectroscopy

Solid-state nuclear magnetic resonance (ssNMR) is an indispensable tool for elucidating the structure and dynamics of insoluble and non-crystalline biomolecules. The recent advances in the sensitivity-enhancing technique magic-angle spinning dynamic nuclear polarization (MAS-DNP) have substantially expanded the territory of ssNMR investigations and enabled the detection of polymer interfaces in a cellular environment. This article highlights the emerging MAS-DNP approaches and their applications to the analysis of biomolecular composites and intact cells to determine the folding pathway and ligand binding of proteins, the structural polymorphism of low-populated biopolymers, as well as the physical interactions between carbohydrates, proteins, and lignin. These structural features provide an atomic-level understanding of many cellular processes, promoting the development of better biomaterials and inhibitors. It is anticipated that the capabilities of MAS-DNP in biomolecular and biomaterial research will be further enlarged by the rapid development of instrumentation and methodology.

The advent of de novo proteins for cancer immunotherapy

Engineered proteins are revolutionizing immunotherapy, but advances are still needed to harness their full potential. Traditional protein engineering methods use naturally existing proteins as a starting point, and therefore, are intrinsically limited to small alterations of a protein's natural structure and function. Conversely, computational de novo protein design is free of such limitation, and can produce a virtually infinite number of novel protein sequences, folds, and functions. Recently, we used de novo protein engineering to create Neoleukin-2/15 (Neo-2/15), a protein mimetic of the function of both interleukin-2 (IL-2) and interleukin-15 (IL-15). To our knowledge, Neo-2/15 is the first de novo protein with immunotherapeutic activity, and in murine cancer models, it has demonstrated enhanced therapeutic potency and reduced toxicity compared to IL-2. De novo protein design is already showcasing its tremendous potential for driving the next wave of protein-based therapeutics that are explicitly engineered to treat disease.

Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.

Designing minimalist membrane proteins

The construction of artificial membrane proteins from first principles is of fundamental interest and holds considerable promise for new biotechnologies. This review considers the potential advantages of adopting a strictly minimalist approach to the process of membrane protein design. As well as the practical benefits of miniaturisation and simplicity for understanding sequence-structure-function relationships, minimalism should also support the abstract conceptualisation of membrane proteins as modular components for synthetic biology. These ideas are illustrated with selected examples that focus upon α-helical membrane proteins, and which demonstrate how such minimalist membrane proteins might be integrated into living biosystems.

De novo protein design, a retrospective

Proteins are molecular machines whose function depends on their ability to achieve complex folds with precisely defined structural and dynamic properties. The rational design of proteins from first-principles, or de novo, was once considered to be impossible, but today proteins with a variety of folds and functions have been realized. We review the evolution of the field from its earliest days, placing particular emphasis on how this endeavor has illuminated our understanding of the principles underlying the folding and function of natural proteins, and is informing the design of macromolecules with unprecedented structures and properties. An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple physical models. As computers became more powerful, the use of the rotamer approximation allowed one to discover amino acid sequences that stabilize the desired fold. As the crystallographic database of protein structures expanded in subsequent years, it became possible to construct proteins by assembling short backbone fragments that frequently recur in Nature. The second set of milestones in de novo design involves the discovery of complex functions. Proteins have been designed to bind a variety of metals, porphyrins, and other cofactors. The design of proteins that catalyze hydrolysis and oxygen-dependent reactions has progressed significantly. However, de novo design of catalysts for energetically demanding reactions, or even proteins that bind with high affinity and specificity to highly functionalized complex polar molecules remains an importnant challenge that is now being achieved. Finally, the protein design contributed significantly to our understanding of membrane protein folding and transport of ions across membranes. The area of membrane protein design, or more generally of biomimetic polymers that function in mixed or non-aqueous environments, is now becoming increasingly possible.

Parallel Homochiral and Anti-Parallel Heterochiral Hydrogen-Bonding Interfaces in Multi-Helical Abiotic Foldamers

A hydrogen-bonding interface between helical aromatic oligoamide foldamers has been designed to promote the folding of a helix-turn-helix motif with a head-to-tail arrangement of two helices of opposite handedness. This design complements an earlier helix-turn-helix motif with a head-to-head arrangement of two helices of identical handedness interface. The two motifs were shown to have comparable stability and were combined in a unimolecular tetra-helix fold constituting the largest abiotic tertiary structure to date.

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

Single-chain heteropolymers transport protons selectively and rapidly

Precise protein sequencing and folding are believed to generate the structure and chemical diversity of natural channels^1,2, both of which are essential to synthetically achieve proton transport performance comparable to that seen in natural systems. Geometrically defined channels have been fabricated using peptides, DNAs, carbon nanotubes, sequence-defined polymers and organic frameworks^3-13. However, none of these channels rivals the performance observed in their natural counterparts. Here we show that without forming an atomically structured channel, four-monomer-based random heteropolymers (RHPs)¹⁴ can mimic membrane proteins and exhibit selective proton transport across lipid bilayers at a rate similar to those of natural proton channels. Statistical control over the monomer distribution in an RHP leads to segmental heterogeneity in hydrophobicity, which facilitates the insertion of single RHPs into the lipid bilayers. It also results in bilayer-spanning segments containing polar monomers that promote the formation of hydrogen-bonded chains^15,16 for proton transport. Our study demonstrates the importance of the adaptability that is enabled by statistical similarity among RHP chains and of the modularity provided by the chemical diversity of monomers, to achieve uniform behaviour in heterogeneous systems. Our results also validate statistical randomness as an unexplored approach to realize protein-like behaviour at the single-polymer-chain level in a predictable manner.

Highly coupled transport can be achieved in free-exchange transport models

Secondary active transporters couple the transport of an ion species down its concentration gradient to the uphill transport of another substrate. Despite the importance of secondary active transport to multidrug resistance, metabolite transport, and nutrient acquisition, among other biological processes, the microscopic steps of the coupling mechanism are not well understood. Often, transport models illustrate coupling mechanisms through a limited number of "major" conformations or states, yet recent studies have indicated that at least some transporters violate these models. The small multidrug resistance transporter EmrE has been shown to couple proton influx to multidrug efflux via a mechanism that incorporates both "major" and "minor" conformational states and transitions. The resulting free exchange transport model includes multiple leak pathways and theoretically allows for both exchange and cotransport of ion and substrate. To better understand how coupled transport can be achieved in such a model, we numerically simulate a free-exchange model of transport to determine the step-by-step requirements for coupled transport. We find that only moderate biasing of rate constants for key transitions produce highly efficient net transport approaching a perfectly coupled, stoichiometric model. We show how a free-exchange model can enable complex phenotypes, including switching transport direction with changing environmental conditions or substrates. This research has broad implications for synthetic biology, as it demonstrates the utility of free-exchange transport models and the fine tuning required for perfectly coupled transport.

Proteins as diverse, efficient, and evolvable scaffolds for artificial metalloenzymes

We have extracted and categorized the desirable properties of proteins that are adapted as the scaffolds for artificial metalloenzymes.

Advances in protein structure prediction and design

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

Applying graph theory to protein structures: an Atlas of coiled coils

Motivation

To understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterized experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analyzing this resource. Here, we use tools from graph theory to define an Atlas classification scheme for automatically categorizing certain protein substructures.

Results

Focusing on the α-helical coiled coils, which are ubiquitous protein-structure and protein-protein interaction motifs, we present a suite of computational resources designed for analyzing these assemblies. iSOCKET enables interactive analysis of side-chain packing within proteins to identify coiled coils automatically and with considerable user control. Applying a graph theory-based Atlas classification scheme to structures identified by iSOCKET gives the Atlas of Coiled Coils, a fully automated, updated overview of extant coiled coils. The utility of this approach is illustrated with the first formal classification of an emerging subclass of coiled coils called α-helical barrels. Furthermore, in the Atlas, the known coiled-coil universe is presented alongside a partial enumeration of the 'dark matter' of coiled-coil structures; i.e. those coiled-coil architectures that are theoretically possible but have not been observed to date, and thus present defined targets for protein design.

Availability and implementation

iSOCKET is available as part of the open-source GitHub repository associated with this work (https://github.com/woolfson-group/isocket). This repository also contains all the data generated when classifying the protein graphs. The Atlas of Coiled Coils is available at: http://coiledcoils.chm.bris.ac.uk/atlas/app.

Designed for life: biocompatible de novo designed proteins and components

A principal goal of synthetic biology is the de novo design or redesign of biomolecular components. In addition to revealing fundamentally important information regarding natural biomolecular engineering and biochemistry, functional building blocks will ultimately be provided for applications including the manufacture of valuable products and therapeutics. To fully realize this ambitious goal, the designed components must be biocompatible, working in concert with natural biochemical processes and pathways, while not adversely affecting cellular function. For example, de novo protein design has provided us with a wide repertoire of structures and functions, including those that can be assembled and function in vivo. Here we discuss such biocompatible designs, as well as others that have the potential to become biocompatible, including non-protein molecules, and routes to achieving full biological integration.

MemSTATS: A Benchmark Set of Membrane Protein Symmetries and Pseudosymmetries

In membrane proteins, symmetry and pseudosymmetry often have functional or evolutionary implications. However, available symmetry detection methods have not been tested systematically on this class of proteins because of the lack of an appropriate benchmark set. Here we present MemSTATS, a publicly available benchmark set of both quaternary- and internal-symmetries in membrane protein structures. The symmetries are described in terms of order, repeated elements, and orientation of the axis with respect to the membrane plane. Moreover, using MemSTATS, we compare the performance of four widely used symmetry detection algorithms and highlight specific challenges and areas for improvement in the future.

Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico

BACKGROUND

Evolutionary information contained in the amino acid sequences of proteins specifies the biological function and fold, but exactly what information contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding in silico.

RESULTS

Here we describe a computational tool named Sibe for use in studies of protein sequence, folding, and design using evolutionary coupling between amino acids as a driving factor. In this study, Sibe is used to identify positionally conserved couplings between pairwise amino acids and aid rational protein design. In this process, pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several 'blocks', which could contribute to driving protein folding and design. A human β2-adrenergic receptor (β2AR) was used to demonstrate that those 'blocks' contribute the rational design for specifying functional residues. Sibe also provides folding modules based on both the positionally conserved couplings and well-established statistical potentials for simulating protein folding in silico and predicting tertiary structure. Our results show that statistically inferences of basic evolutionary principles, such as conservations and coupled-mutations, can be used to rapidly design a diverse set of proteins and study protein folding.

CONCLUSIONS

The developed software Sibe provides a computational tool for systematical analysis from protein primary to its tertiary structure using the evolutionary couplings as a driving factor. Sibe, written in C++, accounts for compatibility with the 'big data' era in biological science, and it primarily focuses on protein sequence analysis, but it is also applicable to extend to other modeling and predictions of experimental measurements.