Towards functional de novo designed proteins

Confinement and Catalysis within De Novo Designed Peptide Barrels

De novo protein design has advanced such that many peptide assemblies and protein structures can be generated predictably and quickly. The drive now is to bring functions to these structures, for example, small-molecule binding and catalysis. The formidable challenge of binding and orienting multiple small molecules to direct chemistry is particularly important for paving the way to new functionalities. To address this, here we describe the design, characterization, and application of small-molecule:peptide ternary complexes in aqueous solution. This uses α-helical barrel (αHB) peptide assemblies, which comprise 5 or more α helices arranged around central channels. These channels are solvent accessible, and their internal dimensions and chemistries can be altered predictably. Thus, αHBs are analogous to "molecular flasks" made in supramolecular, polymer, and materials chemistry. Using Förster resonance energy transfer as a readout, we demonstrate that specific αHBs can accept two different organic dyes, 1,6-diphenyl-1,3,5-hexatriene and Nile red, in close proximity. In addition, two anthracene molecules can be accommodated within an αHB to promote anthracene photodimerization. However, not all ternary complexes are productive, either in energy transfer or photodimerization, illustrating the control that can be exerted by judicious choice and design of the αHB.

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

Advancing sustainable biotechnology through protein engineering

The push for industrial sustainability benefits from the use of enzymes as a replacement for traditional chemistry. Biological catalysts, especially those that have been engineered for increased activity, stability, or novel function, and are often greener than alternative chemical approaches. This Review highlights the role of engineered enzymes (and identifies directions for further engineering efforts) in the application areas of greenhouse gas sequestration, fuel production, bioremediation, and degradation of plastic wastes.

Peptides and metal ions: A successful marriage for developing artificial metalloproteins

The mutual relationship between peptides and metal ions enables metalloproteins to have crucial roles in biological systems, including structural, sensing, electron transport, and catalytic functions. The effort to reproduce or/and enhance these roles, or even to create unprecedented functions, is the focus of protein design, the first step toward the comprehension of the complex machinery of nature. Nowadays, protein design allows the building of sophisticated scaffolds, with novel functions and exceptional stability. Recent progress in metalloprotein design has led to the building of peptides/proteins capable of orchestrating the desired functions of different metal cofactors. The structural diversity of peptides allows proper selection of first- and second-shell ligands, as well as long-range electrostatic and hydrophobic interactions, which represent precious tools for tuning metal properties. The scope of this review is to discuss the construction of metal sites in de novo designed and miniaturized scaffolds. Selected examples of mono-, di-, and multi-nuclear binding sites, from the last 20 years will be described in an effort to highlight key artificial models of catalytic or electron-transfer metalloproteins. The authors' goal is to make readers feel like guests at the marriage between peptides and metal ions while offering sources of inspiration for future architects of innovative, artificial metalloproteins.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Rationally seeded computational protein design of ɑ-helical barrels

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Electrostatic origin of a stabilizing synergistic interaction among b-, c-, and f-residues in a trimeric coiled coil

Coiled coils are one of most common protein quaternary structures and represent the best understood relationship between amino acid sequence and protein conformation. Whereas the roles of residues at the canonical heptad positions the a, d, e, and g are understood in precise detail, conventional approaches often assume that the solvent-exposed b-, c-, and f-positions can be varied broadly for application-specific purposes with minimal consequences. However, a growing body of evidence suggests that interactions among these b, c, and f residues can contribute substantially to coiled-coil conformational stability. In the trimeric coiled coil described here, we find that b-position Glu10 engages in a stabilizing long-range synergistic interaction with c-position Lys18 (ΔΔΔGf = -0.65 ± 0.02 kcal/mol). This favorable interaction depends strongly on the presence of two nearby f-position residues: Lys 7 and Tyr14. Extensive mutational analysis of these residues in the presence of added salt vs. denaturant suggests that this long-range synergistic interaction is primarily electrostatic in origin, but also depends on the precise location and acidity of a side-chain hydrogen-bond donor within f-position Tyr14.

Insulin receptor-inspired soluble insulin binder

The insulin receptor (IR) is a 320 kDa membrane receptor tyrosine kinase mediating the pleiotropic actions of insulin, leading to phosphorylation of several intracellular substrates including serine/threonine-protein kinase (AKT1), and IR autophosphorylation. Structural details of the IR have been recently revealed. A high-binding insulin site, L1 (Kd =2 nM), consists of two distant domains in the primary sequence of the IR. Our design simplified the L1 binding site and transformed it into a soluble insulin binder (sIB). The sIB, a 17 kDa protein, binds insulin with 38 nM affinity. The sIB competes with IR for insulin and reduces by more than 50% phosphorylation of AKT1 in HEK 293 T cells, with similar effects on IR autophosphorylation. The sIB represents a new tool for research of insulin binding and signaling properties.

Molecular torsion springs: alteration of helix curvature in frustrated tertiary folds

The first abiotic foldamer tertiary structures have been recently reported in the form of aromatic helix-turn-helix motifs based on oligo-quinolinecarboxamides held together by intramolecular hydrogen bonds. Tertiary folds were predicted by computational modelling of the hydrogen-bonding interfaces between helices and later verified by X-ray crystallography. However, the prognosis of how the conformational preference inherent to each helix influences the tertiary structure warranted further investigation. Several new helix-turn-helix sequences were synthesised in which some hydrogen bonds have been removed. Contrary to expectations, this change did not strongly destabilise the tertiary folds. On closer inspection, a new crystal structure revealed that helices adopt their natural curvature when some hydrogen bonds are missing and undergo some spring torsion upon forming the said hydrogen bonds, thus potentially giving rise to a conformational frustration. This phenomenon sheds light on the aggregation behaviour of the helices when they are not linked by a turn unit.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

From peptides to proteins: coiled-coil tetramers to single-chain 4-helix bundles

The design of completely synthetic proteins from first principles-de novo protein design-is challenging. This is because, despite recent advances in computational protein-structure prediction and design, we do not understand fully the sequence-to-structure relationships for protein folding, assembly, and stabilization. Antiparallel 4-helix bundles are amongst the most studied scaffolds for de novo protein design. We set out to re-examine this target, and to determine clear sequence-to-structure relationships, or design rules, for the structure. Our aim was to determine a common and robust sequence background for designing multiple de novo 4-helix bundles. In turn, this could be used in chemical and synthetic biology to direct protein-protein interactions and as scaffolds for functional protein design. Our approach starts by analyzing known antiparallel 4-helix coiled-coil structures to deduce design rules. In terms of the heptad repeat, abcdefg -i.e., the sequence signature of many helical bundles-the key features that we identify are: a = Leu, d = Ile, e = Ala, g = Gln, and the use of complementary charged residues at b and c. Next, we implement these rules in the rational design of synthetic peptides to form antiparallel homo- and heterotetramers. Finally, we use the sequence of the homotetramer to derive in one step a single-chain 4-helix-bundle protein for recombinant production in E. coli. All of the assembled designs are confirmed in aqueous solution using biophysical methods, and ultimately by determining high-resolution X-ray crystal structures. Our route from peptides to proteins provides an understanding of the role of each residue in each design.

Changing the tracks: screening for electron transfer proteins to support hydrogen production

Ferredoxins are essential electron transferring proteins in organisms. Twelve plant-type ferredoxins in the green alga Chlamydomonas reinhardtii determine the fate of electrons, generated in multiple metabolic processes. The two hydrogenases HydA1 and HydA2 of. C. reinhardtii compete for electrons from the photosynthetic ferredoxin PetF, which is the first stromal mediator of the high-energy electrons derived from the absorption of light energy at the photosystems. While being involved in many chloroplast-located metabolic pathways, PetF shows the highest affinity for ferredoxin-NADP+ oxidoreductase (FNR), not for the hydrogenases. Aiming to identify other potential electron donors for the hydrogenases, we screened as yet uncharacterized ferredoxins Fdx7, 8, 10 and 11 for their capability to reduce the hydrogenases. Comparing the performance of the Fdx in presence and absence of competitor FNR, we show that Fdx7 has a higher affinity for HydA1 than for FNR. Additionally, we show that synthetic FeS-cluster-binding maquettes, which can be reduced by NADPH alone, can also be used to reduce the hydrogenases. Our findings pave the way for the creation of tailored electron donors to redirect electrons to enzymes of interest.

Identification of novel functional mini-receptors by combinatorial screening of split-WW domains

β-Sheet motifs such as the WW domain are increasingly being explored as building blocks for synthetic biological applications. Since the sequence-structure relationships of β-sheet motifs are generally complex compared to the well-studied α-helical coiled coil (CC), other approaches such as combinatorial screening should be included to vary the function of the peptide. In this study, we present a combinatorial approach to identify novel functional mini-proteins based on the WW-domain scaffold, which takes advantage of the successful reconstitution of the fragmented WW domain of hPin1 (hPin1WW) by CC association. Fragmentation of hPin1WW was performed in both loop 1 (CC-hPin1WW-L1) and loop 2 (CC-hPin1WW-L2), and the respective fragments were linked to the strands of an antiparallel heterodimeric CC. Structural analysis by CD and NMR spectroscopy revealed structural reconstitution of the WW-domain scaffold only in CC-hPin1WW-L1, but not in CC-hPin1WW-L2. Furthermore, by using 1H-15N HSQC NMR, fluorescence and CD spectroscopy, we demonstrated that binding properties of fragmented hPin1WW in CC-hPin1WW-L1 were fully restored by CC association. To demonstrate the power of this approach as a combinatorial screening platform, we synthesized a four-by-six library of N- and C-terminal hPin1WW-CC peptide fragments that was screened for a WW domain that preferentially binds to ATP over cAMP, phophocholine, or IP6. Using this screening platform, we identified one WW domain, which specifically binds ATP, and a phosphorylcholine-specific WW-based mini-receptor, both having binding dissociation constants in the lower micromolar range.

De novo design of discrete, stable 310-helix peptide assemblies

The α-helix is pre-eminent in structural biology¹ and widely exploited in protein folding², design³ and engineering⁴. Although other helical peptide conformations do exist near to the α-helical region of conformational space-namely, 3₁₀-helices and π-helices⁵-these occur much less frequently in protein structures. Less favourable internal energies and reduced tendencies to pack into higher-order structures mean that 3₁₀-helices rarely exceed six residues in length in natural proteins, and that they tend not to form normal supersecondary, tertiary or quaternary interactions. Here we show that despite their absence in nature, synthetic peptide assemblies can be built from 3₁₀-helices. We report the rational design, solution-phase characterization and an X-ray crystal structure for water-soluble bundles of 3₁₀-helices with consolidated hydrophobic cores. The design uses six-residue repeats informed by analysing 3₁₀-helical conformations in known protein structures, and incorporates α-aminoisobutyric acid residues. Design iterations reveal a tipping point between α-helical and 3₁₀-helical folding, and identify features required for stabilizing assemblies of 3₁₀-helices. This work provides principles and rules to open opportunities for designing into this hitherto unexplored region of protein-structure space.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Prerequisites for Stabilizing Long-Range Synergistic Interactions among b-, c-, and f-Residues in Coiled Coils

Coiled coils are among the most abundant tertiary and quaternary structures found in proteins. A growing body of evidence suggests that long-range synergistic interactions among solvent-exposed residues can contribute substantially to coiled-coil conformational stability, but our understanding of the key sequence and structural prerequisites of this effect is still developing. Here, we show that the strength of synergistic interaction involving a b-position Glu (i), an f-position Tyr (i + 4), and a c-position Lys (i + 8) depends on the identity of the f-position residue, the length and stability of the coiled coil, and its oligomerization stoichiometry/surface accessibility. Combined with previous observations, these results map out predictable sequence- and structure-based criteria for enhancing coiled-coil stability by up to -0.58 kcal/mol per monomer (or -2.32 kcal/mol per coiled-coil tetramer). Our observations expand the available tools for enhancing coiled coil stability by sequence variation at solvent-exposed b-, c-, and f-positions and suggest the need to exercise care in the choice of substitutions at these positions for application-specific purposes.

Switchable Zinc(II)-Responsive Globular β-Sheet Peptide

The natural function of many proteins depends on their ability to switch their conformation driven by environmental changes. In this work, we present a small, monomeric β-sheet peptide that switches between a molten globule and a folded state through Zn(II) binding. The solvent-exposed hydrophobic core on the β-sheet surface was substituted by a His₃-site, whereas the internal hydrophobic core was left intact. Zn(II) is specifically recognized by the peptide relative to other divalent metal ions, binds in the lower micromolar range, and can be removed and re-added without denaturation of the peptide. In addition, the peptide is fully pH-switchable, has a pK_a of about 6, and survives several cycles of acidification and neutralization. In-depth structural characterization of the switch was achieved by concerted application of circular dichroism (CD) and multinuclear NMR spectroscopy. Thus, this study represents a viable approach toward a globular β-sheet Zn(II) mini-receptor prototype.

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.

Crystal structures capture multiple stoichiometric states of an aqueous self-assembling oligourea foldamer

We report here an oligourea foldamer able to self-assemble in aqueous conditions into helix bundles of multiple stoichiometries. Importantly, we report crystal structures of several of these stoichiometries, providing a series of high-resolution snap-shots of the structural polymorphism of this foldamer and uncovering a novel self-assembly.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Socket2: A Program for Locating, Visualising, and Analysing Coiled-coil Interfaces in Protein Structures

Motivation

Protein–protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualize and analyze CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterizes CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and nonexpert users alike.

Results

The original Socket only classified CCs with up to six helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating nonnatural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualizing CCs, and tabs for viewing the sequence repeats, helix-packing angles and core-packing geometries of CCs identified and calculated by Socket2.

Availability and implementation

Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT licence and available to download under the Downloads tab of the Socket2 home page.

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?

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein

Our understanding of what determines ligand affinity of proteins is poor, even with high-resolution structures available. Both the non-covalent ligand-protein interactions and the relative free energies of available conformations contribute to the affinity of a protein for a ligand. Distant, non-binding site residues can influence the ligand affinity by altering the free energy difference between a ligand-free and ligand-bound conformation. Our hypothesis is that when different ligands induce distinct ligand-bound conformations, it should be possible to tweak their affinities by changing the free energies of the available conformations. We tested this idea for the maltose-binding protein (MBP) from Escherichia coli. We used single-molecule Förster resonance energy transfer (smFRET) to distinguish several unique ligand-bound conformations of MBP. We engineered mutations, distant from the binding site, to affect the stabilities of different ligand-bound conformations. We show that ligand affinity can indeed be altered in a conformation-dependent manner. Our studies provide a framework for the tuning of ligand affinity, apart from modifying binding site residues.

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.

Structural resolution of switchable states of a de novo peptide assembly

De novo protein design is advancing rapidly. However, most designs are for single states. Here we report a de novo designed peptide that forms multiple α-helical-bundle states that are accessible and interconvertible under the same conditions. Usually in such designs amphipathic α helices associate to form compact structures with consolidated hydrophobic cores. However, recent rational and computational designs have delivered open α-helical barrels with functionalisable cavities. By placing glycine judiciously in the helical interfaces of an α-helical barrel, we obtain both open and compact states in a single protein crystal. Molecular dynamics simulations indicate a free-energy landscape with multiple and interconverting states. Together, these findings suggest a frustrated system in which steric interactions that maintain the open barrel and the hydrophobic effect that drives complete collapse are traded-off. Indeed, addition of a hydrophobic co-solvent that can bind within the barrel affects the switch between the states both in silico and experimentally.

So far most of the de novo designed proteins are for single states only. Here, the authors present the de novo design and crystal structure determination of a coiled-coil peptide that assembles into multiple, distinct conformational states under the same conditions and further characterise its properties with biophysical experiments, NMR and MD simulations.

Interfacing Living and Synthetic Cells as an Emerging Frontier in Synthetic Biology

The construction of artificial cells from inanimate molecular building blocks is one of the grand challenges of our time. In addition to being used as simplified cell models to decipher the rules of life, artificial cells have the potential to be designed as micromachines deployed in a host of clinical and industrial applications. The attractions of engineering artificial cells from scratch, as opposed to re-engineering living biological cells, are varied. However, it is clear that artificial cells cannot currently match the power and behavioural sophistication of their biological counterparts. Given this, many in the synthetic biology community have started to ask: is it possible to interface biological and artificial cells together to create hybrid living/synthetic systems that leverage the advantages of both? This article will discuss the motivation behind this cellular bionics approach, in which the boundaries between living and non-living matter are blurred by bridging top-down and bottom-up synthetic biology. It details the state of play of this nascent field and introduces three generalised hybridisation modes that have emerged.

Interfacing Living and Synthetic Cells as an Emerging Frontier in Synthetic Biology

The construction of artificial cells from inanimate molecular building blocks is one of the grand challenges of our time. In addition to being used as simplified cell models to decipher the rules of life, artificial cells have the potential to be designed as micromachines deployed in a host of clinical and industrial applications. The attractions of engineering artificial cells from scratch, as opposed to re-engineering living biological cells, are varied. However, it is clear that artificial cells cannot currently match the power and behavioural sophistication of their biological counterparts. Given this, many in the synthetic biology community have started to ask: is it possible to interface biological and artificial cells together to create hybrid living/synthetic systems that leverage the advantages of both? This article will discuss the motivation behind this cellular bionics approach, in which the boundaries between living and non-living matter are blurred by bridging top-down and bottom-up synthetic biology. It details the state of play of this nascent field and introduces three generalised hybridisation modes that have emerged.

Linking molecular evolution to molecular grafting

Molecular grafting is a strategy for the engineering of molecular scaffolds into new functional agents, such as next-generation therapeutics. Despite its wide use, studies so far have focused almost exclusively on demonstrating its utility rather than understanding the factors that lead to either poor or successful grafting outcomes. Here, we examine protein evolution and identify parallels between the natural process of protein functional diversification and the artificial process of molecular grafting. We discuss features of natural proteins that are correlated to innovability-the capacity to acquire new functions-and describe their implications to molecular grafting scaffolds. Disulfide-rich peptides are used as exemplars because they are particularly promising scaffolds onto which new functions can be grafted. This article provides a perspective on why some scaffolds are more suitable for grafting than others, identifying opportunities on how molecular grafting might be improved.

Enzymes in biotechnology: Critical platform technologies for bioprocess development

Enzymes are core elements of biosynthetic pathways employed in the synthesis of numerous bioproducts. Here, we review enzyme promiscuity, enzyme engineering, enzyme immobilization, and cell-free systems as fundamental strategies of bioprocess development. Initially, promiscuous enzymes are the first candidates in the quest for new activities to power new, artificial, or bypass pathways that expand substrate range and catalyze the production of new products. If the activity or regulation of available enzymes is unsuitable for a process, protein engineering can be applied to improve them to the required level. When cell toxicity and low productivity cannot be engineered away, cell-free systems are an attractive option, especially in combination with enzyme immobilization that allows extended enzyme use. Overall, the above methods support powerful platforms for bioprocess development and optimization.

Progress, challenges and future directions of heterocycles as building blocks in iterative methodologies towards sequence-defined oligomers and polymers

Heterocyclic building blocks for iterative methodologies leading to sequence-defined oligomers and polymers are reviewed. Solid- as well as solution-phase methods, challenges surrounding these systems and potential future directions are presented.

Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly

Synthetic Biology is a rapidly growing interdisciplinary field that is primarily built upon foundational advances in molecular biology combined with engineering design principles such as modularity and interoperability. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies and methodological advances. A key concept driving the field is the Design-Build-Test-Learn cycle which provides a systematic framework for building new biological systems. One major application area for synthetic biology is biosynthetic pathway engineering that requires the modular assembly of different genetic regulatory elements and biosynthetic enzymes. In this review we provide an overview of modular DNA assembly and describe and compare the plethora of in vitro and in vivo assembly methods for combinatorial pathway engineering. Considerations for part design and methods for enzyme balancing are also presented, and we briefly discuss alternatives to intracellular pathway assembly including microbial consortia and cell-free systems for biosynthesis. Finally, we describe computational tools and automation for pathway design and assembly and argue that a deeper understanding of the many different variables of genetic design, pathway regulation and cellular metabolism will allow more predictive pathway design and engineering.

Making or Breaking Metal-Dependent Catalytic Activity: The Role of Stammers in Designed Three-Stranded Coiled Coils

While many life-critical reactions would be infeasibly slow without metal cofactors, a detailed understanding of how protein structure can influence catalytic activity remains elusive. Using de novo designed three-stranded coiled coils (TRI and Grand peptides formed using a heptad repeat approach), we examine how the insertion of a three residue discontinuity, known as a stammer insert, directly adjacent to a (His)3 metal binding site alters catalytic activity. The stammer, which locally alters the twist of the helix, significantly increases copper-catalyzed nitrite reductase activity (CuNiR). In contrast, the well-established zinc-catalyzed carbonic anhydrase activity (p-nitrophenyl acetate, pNPA) is effectively ablated. This study illustrates how the perturbation of the protein sequence using non-coordinating and non-acid base residues in the helical core can perturb metalloenzyme activity through the simple expedient of modifying the helical pitch adjacent to the catalytic center.

Engineered biosynthetic pathways and biocatalytic cascades for sustainable synthesis

Nature exploits biosynthetic cascades to construct numerous molecules from a limited set of starting materials. A deeper understanding of biosynthesis and extraordinary developments in gene technology has allowed the manipulation of natural pathways and construction of artificial cascades for the preparation of a range of molecules, which would be challenging to access using traditional synthetic chemical approaches. Alongside these metabolic engineering strategies, there has been continued interest in developing in vivo and in vitro biocatalytic cascades. Advancements in both metabolic engineering and biocatalysis are complementary, and this article aims to highlight some of the most exciting developments in these two areas with a particular focus on exploring those that have the potential to advance both pathway engineering and more traditional biocatalytic cascade development.

Using physical features of protein core packing to distinguish real proteins from decoys

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. We used two sources to obtain datasets of decoys to compare with real protein structures: submissions to the biennial Critical Assessment of protein Structure Prediction competition, in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence, and also decoys generated by 3DRobot, which have user-specified global root-mean-squared deviations from experimentally determined structures. Our analysis revealed that both sets of decoys possess cores that do not recapitulate the key features that define real protein cores. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a feed-forward neural network, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoy structures with similar accuracy to that obtained by state-of-the-art methods that incorporate many additional features. The small number of physical features makes our model interpretable, emphasizing the importance of protein packing and hydrophobicity in protein structure prediction.

Long-range PEG Stapling: Macrocyclization for Increased Protein Conformational Stability and Resistance to Proteolysis

We previously showed that long-range stapling of two Asn-linked O-allyl PEG oligomers via olefin metathesis substantially increases the conformational stability of the WW domain through an entropic effect. The impact of stapling was more favorable when the staple connected positions that were far apart in primary sequence but close in the folded tertiary structure. Here we validate these criteria by identifying new stabilizing PEG-stapling sites within the WW domain and the SH3 domain, both β-sheet proteins. We find that stapling via olefin metathesis vs. the copper(i)-catalyzed azide/alkyne cycloaddition (CuAAC) results in similar energetic benefits, suggesting that olefin and triazole staples can be used interchangeably. Proteolysis assays of selected WW variants reveal that the observed staple-based increases in conformational stability lead to enhanced proteolytic resistance. Finally, we find that an intermolecular staple dramatically increases the quaternary structural stability of an α-helical GCN4 coiled-coil heterodimer.

Long-range stapling of two Asn-linked PEG oligomers via olefin metathesis substantially increases the conformational stability of the WW and SH3 domain tertiary structures and the GCN4 coiled-coil quaternary structure.

Phycobiliproteins from extreme environments and their potential applications

The light-harvesting phycobilisome complex is an important component of photosynthesis in cyanobacteria and red algae. Phycobilisomes are composed of phycobiliproteins, including the blue phycobiliprotein phycocyanin, that are considered high-value products with applications in several industries. Remarkably, several cyanobacteria and red algal species retain the capacity to harvest light and photosynthesise under highly selective environments such as hot springs, and flourish in extremes of pH and elevated temperatures. These thermophilic organisms produce thermostable phycobiliproteins, which have superior qualities much needed for wider adoption of these natural pigment-proteins in the food, textile, and other industries. Here we review the available literature on the thermostability of phycobilisome components from thermophilic species and discuss how a better appreciation of phycobiliproteins from extreme environments will benefit our fundamental understanding of photosynthetic adaptation and could provide a sustainable resource for several industrial processes.

De novo protein design enables the precise induction of RSV-neutralizing antibodies

De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines. Here, we present a protein design algorithm called TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. In both mice and nonhuman primates, cocktails of three de novo-designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused preexisting antibody responses toward defined neutralization epitopes. Overall, our design approach opens the possibility of targeting specific epitopes for the development of vaccines and therapeutic antibodies and, more generally, will be applicable to the design of de novo proteins displaying complex functional motifs.

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.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Characterization and Control of Dynamic Rearrangement in a Self-Assembled Antibody Carrier

Thorough characterization of protein assemblies is required for the control of structure and robust performance in any given application, especially for the safety and stability of protein therapeutics. Here, we report the use of multiple, orthogonal characterization techniques to enable control over the structure of a multivalent antibody carrier for future use in drug delivery applications. The carrier, known as Hex, contains six antibody binding domains that bind the Fc region of antibodies. Using size exclusion chromatography, analytical ultracentrifugation, and dynamic light scattering, we identified the stoichiometry of assembled Hex-antibody complexes and observed changes in the stoichiometry of nanocarriers when incubated at higher temperatures over time. The characterization data informed the modification of Hex to achieve tighter control over the protein assembly structure for future therapeutic applications. This work demonstrates the importance of using orthogonal characterization techniques and observing protein assembly in different conditions over time to fully understand and control structure and dynamics.

A Strategy for Combinatorial Cavity Design in De Novo Proteins

Protein sequence space is vast; nature uses only an infinitesimal fraction of possible sequences to sustain life. Are there solutions to biological problems other than those provided by nature? Can we create artificial proteins that sustain life? To investigate these questions, we have created combinatorial collections, or libraries, of novel sequences with no homology to those found in living organisms. Previously designed libraries contained numerous functional proteins. However, they often formed dynamic, rather than well-ordered structures, which complicated structural and mechanistic characterization. To address this challenge, we describe the development of new libraries based on the de novo protein S-824, a 4-helix bundle with a very stable 3-dimensional structure. Distinct from previous libraries, we targeted variability to a specific region of the protein, seeking to create potential functional sites. By characterizing variant proteins from this library, we demonstrate that the S-824 scaffold tolerates diverse amino acid substitutions in a putative cavity, including buried polar residues suitable for catalysis. We designed and created a DNA library encoding 1.7 × 10⁶ unique protein sequences. This new library of stable de novo α-helical proteins is well suited for screens and selections for a range of functional activities in vitro and in vivo.

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.