De novo protein design: how do we expand into the universe of possible protein structures?

Functionally Active Synthetic α-Helical Pores

ConspectusTransmembrane pores are currently at the forefront of nanobiotechnology, nanopore chemistry, and synthetic chemical biology research. Over the past few decades, significant studies in protein engineering have paved the way for redesigning membrane protein pores tailored for specific applications in nanobiotechnology. Most previous efforts predominantly centered on natural β-barrel pores designed with atomic precision for nucleic acid sequencing and sensing of biomacromolecules, including protein fragments. The requirement for a more efficient single-molecule detection system has driven the development of synthetic nanopores. For example, engineering channels to conduct ions and biomolecules selectively could lead to sophisticated nanopore sensors. Also, there has been an increased interest in synthetic pores, which can be fabricated to provide more control in designing architecture and diameter for single-molecule sensing of complex biomacromolecules. There have been impressive advancements in developing synthetic DNA-based pores, although their application in nanopore technology is limited. This has prompted a significant shift toward building synthetic transmembrane α-helical pores, a relatively underexplored field offering novel opportunities. Recently, computational tools have been employed to design and construct α-helical barrels of defined structure and functionality.We focus on building synthetic α-helical pores using naturally occurring transmembrane motifs of membrane protein pores. Our laboratory has developed synthetic α-helical transmembrane pores based on the natural porin PorACj (Porin A derived from Corynebacterium jeikeium) that function as nanopore sensors for single-molecule sensing of cationic cyclodextrins and polypeptides. Our breakthrough lies in being the first to create a functional and large stable synthetic transmembrane pore composed of short synthetic α-helical peptides. The key highlight of our work is that these pores can be synthesized using easy chemical synthesis, which permits its easy modification to include a variety of functional groups to build charge-selective sophisticated pores. Additionally, we have demonstrated that stable functional pores can be constructed from D-amino acid peptides. The analysis of pores composed of D- and L-amino acids in the presence of protease showed that only the D pores are highly functional and stable. The structural models of these pores revealed distinct surface charge conformation and geometry. These new classes of synthetic α-helical pores are highly original systems of general interest due to their unique architecture, functionality, and potential applications in nanopore technology and chemical biology. We emphasize that these simplified transmembrane pores have the potential to be components of functional nanodevices and therapeutic tools. We also suggest that such designed peptides might be valuable as antimicrobial agents and can be targeted to cancer cells. This article will focus on the evolutions in assembling α-helical transmembrane pores and highlight their advantages, including structural and functional versatility.

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.

Origin of the multi-phasic quenching dynamics in the BLUF domains across the species

Blue light using flavin (BLUF) photoreceptors respond to light via one of nature's smallest photo-switching domains. Upon photo-activation, the flavin cofactor in the BLUF domain exhibits multi-phasic dynamics, quenched by a proton-coupled electron transfer reaction involving the conserved Tyr and Gln. The dynamic behavior varies drastically across different species, the origin of which remains controversial. Here, we incorporate site-specific fluorinated Trp into three BLUF proteins, i.e., AppA, OaPAC and SyPixD, and characterize the percentages for the Wout, WinNHin and WinNHout conformations using 19F nuclear magnetic resonance spectroscopy. Using femtosecond spectroscopy, we identify that one key WinNHin conformation can introduce a branching one-step proton transfer in AppA and a two-step proton transfer in OaPAC and SyPixD. Correlating the flavin quenching dynamics with the active-site structural heterogeneity, we conclude that the quenching rate is determined by the percentage of WinNHin, which encodes a Tyr-Gln configuration that is not conducive to proton transfer.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Sequence-Tunable Phase Behavior and Intrinsic Fluorescence in Dynamically Interacting Peptides

A conceptual framework towards understanding biological condensed phases is emerging, derived from biological, biomimetic, and synthetic sequences. However, de novo peptide condensate design remains a challenge due to an incomplete understanding of the structural and interactive complexity. We designed peptide modules based on a simple repeat motif composed of tripeptide spacers (GSG, SGS, GLG) interspersed with adhesive amino acids (R/H and Y). We show, using sequence editing and a combination of computation and experiment, that n→π* interactions in GLG backbones are a dominant factor in providing sufficient backbone structure, which in turn regulates the water interface, collectively promoting liquid droplet formation. Moreover, these R(GLG)Y and H(GLG)Y condensates unexpectedly display sequence-dependent emission that is a consequence of their non-covalent network interactions, and readily observable by confocal microscopy.

Illuminating protein space with a programmable generative model

Three billion years of evolution has produced a tremendous diversity of protein molecules1, but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology.

Exploring the Protein Sequence Space with Global Generative Models

Recent advancements in specialized large-scale architectures for training images and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, have demonstrated exceptional capabilities in processing, translating, and generating human language. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Several of these models have been developed with the goal of generating sequences in novel regions of the protein space. In this work, we provide an overview of the use of protein generative models, reviewing (1) language models for the design of novel artificial proteins, (2) works that use non-transformer architectures, and (3) applications in directed evolution approaches.

Metal-Promoted Higher-Order Assembly of Disulfide-Stapled Helical Barrels

Peptide-based helical barrels are a noteworthy building block for hierarchical assembly, with a hydrophobic cavity that can serve as a host for cargo. In this study, disulfide-stapled helical barrels were synthesized containing ligands for metal ions on the hydrophilic face of each amphiphilic peptide helix. The major product of the disulfide-stapling reaction was found to be composed of five amphiphilic peptides, thereby going from a 16-amino-acid peptide to a stapled 80-residue protein in one step. The structure of this pentamer, 5HB1, was optimized in silico, indicating a significant hydrophobic cavity of ~6 Å within a helical barrel. Metal-ion-promoted assembly of the helical barrel building blocks generated higher order assemblies with a three-dimensional (3D) matrix morphology. The matrix was decorated with hydrophobic dyes and His-tagged proteins both before and after assembly, taking advantage of the hydrophobic pocket within the helical barrels and coordination sites within the metal ion-peptide framework. As such, this peptide-based biomaterial has potential for a number of biotechnology applications, including supplying small molecule and protein growth factors during cell and tissue growth within the matrix.

Understanding a protein fold: the physics, chemistry, and biology of α-helical coiled coils

Protein science is being transformed by powerful computational methods for structure prediction and design: AlphaFold2 can predict many natural protein structures from sequence, and other AI methods are enabling the de novo design of new structures. This raises a question: how much do we understand the underlying sequence-to-structure/function relationships being captured by these methods? This perspective presents our current understanding of one class of protein assembly, the α-helical coiled coils. At first sight, these are straightforward: sequence repeats of hydrophobic (h) and polar (p) residues, (hpphppp)_n, direct the folding and assembly of amphipathic α helices into bundles. However, many different bundles are possible: they can have two or more helices (different oligomers); the helices can have parallel, antiparallel or mixed arrangements (different topologies); and the helical sequences can be the same (homomers) or different (heteromers). Thus, sequence-to-structure relationships must be present within the hpphppp repeats to distinguish these states. I discuss the current understanding of this problem at three levels: First, physics gives a parametric framework to generate the many possible coiled-coil backbone structures. Second, chemistry provides a means to explore and deliver sequence-to-structure relationships. Third, biology shows how coiled coils are adapted and functionalized in nature, inspiring applications of coiled coils in synthetic biology. I argue that the chemistry is largely understood; the physics is partly solved, though the considerable challenge of predicting even relative stabilities of different coiled-coil states remains; but there is much more to explore in the biology and synthetic biology of coiled coils.

Reshaping de Novo Protein Switches into Bioresponsive Materials for Biomarker, Toxin, and Viral Detection

De novo designed protein switches are powerful tools to specifically and sensitively detect diverse targets with simple chemiluminescent readouts. Finding an appropriate material host for de novo designed protein switches without altering their thermodynamics while preserving their intrinsic stability over time would enable the development of a variety of sensing formats to monitor exposure to pathogens, toxins, and for disease diagnosis. Here, a de novo protein-biopolymer hybrid that maintains the detection capabilities induced by the conformational change of the incorporated proteins in response to analytes of interest is generated in multiple, shelf-stable material formats without the need of refrigerated storage conditions. A set of functional demonstrator devices including personal protective equipment such as masks and laboratory gloves, free-standing films, air quality monitors, and wearable devices is presented to illustrate the versatility of the approach. Such formats are designed to be responsive to human epidermal growth factor receptor (HER2), anti-hepatitis B (HBV) antibodies, Botulinum neurotoxin B (BoNT/B), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This combination of form and function offers wide opportunities for ubiquitous sensing in multiple environments by enabling a large class of bio-responsive interfaces of broad utility.

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.

PDBench: evaluating computational methods for protein-sequence design

SUMMARY

Ever increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wells-wood-research/PDBench.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An artificial self-assembling peptide with carboxylesterase activity and substrate specificity restricted to short-chain acid p-nitrophenyl esters

Natural enzymes possess remarkable catalytic activity and high substrate specificity. Many efforts have been dedicated to construct artificial enzymes with high catalytic activity. However, how to mimic the exquisite substrate specificity of a natural enzyme remains challenging because of the complexity of the enzyme structure. Here, we report artificial carboxylesterases that are specific for short chain fatty acids and were constructed via peptide self-assembly. These artificial systems have esterase-like activity rather than lipase-like activity towards p-nitrophenyl esters. The designer peptides self-assembled into nanofibers with strong β-sheet character. The extending histidine units and the hydrophobic edge of the fibrillar structure collectively form the active center of the artificial esterase. These artificial esterases show substrate specificity for short-chain acids esters. Moreover, 1-isopropoxy-4-nitrobenzene could function as a competitive inhibitor of hydrolysis of p-nitrophenyl acetate for an artificial esterase.

Assembly of transmembrane pores from mirror-image peptides

Tailored transmembrane alpha-helical pores with desired structural and functional versatility have promising applications in nanobiotechnology. Herein, we present a transmembrane pore DpPorA, based on the natural pore PorACj, built from D-amino acid α-helical peptides. Using single-channel current recordings, we show that DpPorA peptides self-assemble into uniform cation-selective pores in lipid membranes and exhibit properties distinct from their L-amino acid counterparts. DpPorA shows resistance to protease and acts as a functional nanopore sensor to detect cyclic sugars, polypeptides, and polymers. Fluorescence imaging reveals that DpPorA forms well-defined pores in giant unilamellar vesicles facilitating the transport of hydrophilic molecules. A second D-amino acid peptide based on the polysaccharide transporter Wza forms transient pores confirming sequence specificity in stable, functional pore formation. Finally, molecular dynamics simulations reveal the specific alpha-helical packing and surface charge conformation of the D-pores consistent with experimental observations. Our findings will aid the design of sophisticated pores for single-molecule sensing related technologies.

Alpha-helix nanopores have a range of potential applications and the inclusion of non-natural amino acids allows for modification. Here, the authors report on the creation of alpha-helix pores using D-amino acids and show the pores formed, have different properties to the L-counterparts and were resistant to proteases.

A topological refactoring design strategy yields highly stable granulopoietic proteins

Protein therapeutics frequently face major challenges, including complicated production, instability, poor solubility, and aggregation. De novo protein design can readily address these challenges. Here, we demonstrate the utility of a topological refactoring strategy to design novel granulopoietic proteins starting from the granulocyte-colony stimulating factor (G-CSF) structure. We change a protein fold by rearranging the sequence and optimising it towards the new fold. Testing four designs, we obtain two that possess nanomolar activity, the most active of which is highly thermostable and protease-resistant, and matches its designed structure to atomic accuracy. While the designs possess starkly different sequence and structure from the native G-CSF, they show specific activity in differentiating primary human haematopoietic stem cells into mature neutrophils. The designs also show significant and specific activity in vivo. Our topological refactoring approach is largely independent of sequence or structural context, and is therefore applicable to a wide range of protein targets.

Skokowa et al. reconstruct the fold of a granulopoietic cytokine, resulting in de novo, hyperstable, highly active proteins with therapeutic potential for treating several neutropenia disorders.

Mariz B, Moreira IP, Kalafatovic D, Morais Faustino BM, Narang V, Wang T, Pappas CG, Ferreira I, Roque ACA, Ulijn RV. Discovery of phosphotyrosine-binding oligopeptides with supramolecular target selectivity

Phage-display screening on self-assembled tyrosine-phosphate ligands enables the identification of oligopeptides selective to dynamic supramolecular targets, with the lead peptide showing a preferred hairpin-like conformation and catalytic activity.

De novo protein design by an energy function based on series expansion in distance and orientation dependence

MOTIVATION

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing.

RESULTS

The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design.

AVAILABILITY AND IMPLEMENTATION

The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

Icosahedral 60-meric porous structure of designed supramolecular protein nanoparticle TIP60

Supramolecular protein nanoparticles and nanocages have potential in a broad range of applications. Recently, we developed a uniform supramolecular protein nanoparticle, TIP60, symmmetrically self-assembled from fusion proteins of a pentameric Sm-like protein and a dimeric MyoX-coil domain. Herein, we report the icosahedral 60-meric structure of TIP60 solved using single-particle cryo-electron microscopy. Interestingly, the structure revealed 20 regular-triangle-like pores on the surface. TIP60 and its mutants have many modifiable sites on their exterior and interior surfaces. The TIP60 architecture will be useful in the development of biomedical and biochemical nanoparticles/nanocages for future applications.

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?

Coiled coils 9-to-5: rational de novo design of α-helical barrels with tunable oligomeric states

The rational design of linear peptides that assemble controllably and predictably in water is challenging. Short sequences must encode unique target structures and avoid alternative states. However, the non-covalent forces that stabilize and discriminate between states are weak. Nonetheless, for α-helical coiled-coil assemblies considerable progress has been made in rational de novo design. In these, sequence repeats of nominally hydrophobic (h) and polar (p) residues, hpphppp, direct the assembly of amphipathic helices into dimeric to tetrameric bundles. Expanding this pattern to hpphhph can produce larger α-helical barrels. Here, we show that pentameric to nonameric barrels are accessed by varying the residue at one of the h sites. In peptides with four L/I-K-E-I-A-x-Z repeats, decreasing the size of Z from threonine to serine to alanine to glycine gives progressively larger oligomers. X-ray crystal structures of the resulting α-helical barrels rationalize this: side chains at Z point directly into the helical interfaces, and smaller residues allow closer helix contacts and larger assemblies.

On the Emergence of Orientational Order in Folded Proteins with Implications for Allostery

The beautiful structures of single- and multi-domain proteins are clearly ordered in some fashion but cannot be readily classified using group theory methods that are successfully used to describe periodic crystals. For this reason, protein structures are considered to be aperiodic, and may have evolved this way for functional purposes, especially in instances that require a combination of softness and rigidity within the same molecule. By analyzing the solved protein structures, we show that orientational symmetry is broken in the aperiodic arrangement of the secondary structure elements (SSEs), which we deduce by calculating the nematic order parameter, P2. We find that the folded structures are nematic droplets with a broad distribution of P2. We argue that a non-zero value of P2, leads to an arrangement of the SSEs that can resist external forces, which is a requirement for allosteric proteins. Such proteins, which resist mechanical forces in some regions while being flexible in others, transmit signals from one region of the protein to another (action at a distance) in response to binding of ligands (oxygen, ATP, or other small molecules).

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.

Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance

Inappropriate and injudicious use of antimicrobial drugs in human health, hygiene, agriculture, animal husbandry and food industries has contributed significantly to rapid emergence and persistence of antimicrobial resistance (AMR), one of the serious global public health threats. The crisis of AMR versus slower discovery of newer antibiotics put forth a daunting task to control these drug-resistant superbugs. Several phyto-antimicrobials have been identified in recent years with direct-killing (bactericidal) and/or drug-resistance reversal (re-sensitization of AMR phenotypes) potencies. Phyto-antimicrobials may hold the key in combating AMR owing to their abilities to target major microbial drug-resistance determinants including cell membrane, drug-efflux pumps, cell communication and biofilms. However, limited distribution, low intracellular concentrations, eco-geographical variations, beside other considerations like dynamic environments, climate change and over-exploitation of plant-resources are major blockades in full potential exploration phyto-antimicrobials. Synthetic biology (SynBio) strategies integrating metabolic engineering, RNA-interference, genome editing/engineering and/or systems biology approaches using plant chassis (as engineerable platforms) offer prospective tools for production of phyto-antimicrobials. With expanding SynBio toolkit, successful attempts towards introduction of entire gene cluster, reconstituting the metabolic pathway or transferring an entire metabolic (or synthetic) pathway into heterologous plant systems highlight the potential of this field. Through this perspective review, we are presenting herein the current situation and options for addressing AMR, emphasizing on the significance of phyto-antimicrobials in this apparently post-antibiotic era, and effective use of plant chassis for phyto-antimicrobial production at industrial scales along with major SynBio tools and useful databases. Current knowledge, recent success stories, associated challenges and prospects of translational success are also discussed.

Orientational Ambiguity in Septin Coiled Coils and its Structural Basis

Septins are an example of subtle molecular recognition whereby different paralogues must correctly assemble into functional filaments important for essential cellular events such as cytokinesis. Most possess C-terminal domains capable of forming coiled coils which are believed to be involved in filament formation and bundling. Here, we report an integrated structural approach which aims to unravel their architectural diversity and in so doing provide direct structural information for the coiled-coil regions of five human septins. Unexpectedly, we encounter dimeric structures presenting both parallel and antiparallel arrangements which are in consonance with molecular modelling suggesting that both are energetically accessible. These sequences therefore code for two metastable states of different orientations which employ different but overlapping interfaces. The antiparallel structures present a mixed coiled-coil interface, one side of which is dominated by a continuous chain of core hydrophilic residues. This unusual type of coiled coil could be used to expand the toolkit currently available to the protein engineer for the design of previously unforeseen coiled-coil based assemblies. Within a physiological context, our data provide the first atomic details related to the assumption that the parallel orientation is likely formed between septin monomers from the same filament whilst antiparallelism may participate in the widely described interfilament cross bridges necessary for higher order structures and thereby septin function.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Antimicrobial effects of syndiotactic polypeptides

We present design and antibacterial studies of stereochemically diversified antimicrobial peptides against multidrug-resistant bacterial pathogens. Syndiotactic polypeptides are polymers of alternating L and D amino acids with LDLD or DLDL backbone stereochemical sequence, which can form stable gramicidin like helical conformations. We designed, synthesized and characterized eight model molecular systems with varied electrostatic fingerprints, modulated through calibrated sequence positioning. Six out of eight model systems showed very impressive antimicrobial activity against three difficult to treat bacterial species, Gentamicin resistant MRSA, E. coli and Mycobacterium. More importantly, the designed LDLD peptides were equally potent in serum, an important drawback of poly L peptide sequences due to enzyme mediated degradation and ion sensitivity. Further, we tested the activity of the designed peptides against drug-resistant clinical isolates of Staphylococcus aureus and Escherichia coli. Molecular dynamics simulation studies suggest formation of an assembly of individual peptides, preceding the membrane interaction and deformation. The activity estimates are comparable with the available peptide based antimicrobials, and are also highly specific and less toxic as per standard estimates. Incorporation of D amino-acids can significantly expand the peptide design space, which can in turn manifest in future biomaterial designs, especially antimicrobials.

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.

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.

Arming Yourself for The In Silico Protein Design Revolution

Proteins mediate many essential processes of life to a degree of functional precision unmatched by any synthetic device. While engineered proteins are currently used in biotech, food, biomedicine, and material technology-based industries, the true potential of proteins is practically untapped. The emerging field of in silico protein design is predicted to provide the next quantum leap in the biotech industry. Having predictive control over protein function and the ability to redefine these functions have driven the field of protein engineering into an era of unprecedented development. This article provides a holistic analysis of protein design R&D (current state-of-the-art tools and knowhow) and commercial landscape, as well as a one-stop-shop profile of in silico protein design technology for biotechnology stakeholders.

Rationally Designed Protein Building Blocks for Programmable Hierarchical Architectures

Diverse natural/artificial proteins have been used as building blocks to construct a variety of well-ordered nanoscale structures over the past couple of decades. Sophisticated protein self-assemblies have attracted great scientific interests due to their potential applications in disease diagnosis, illness treatment, biomechanics, bio-optics and bio-electronics, etc. This review outlines recent efforts directed to the creation of structurally defined protein assemblies including one-dimensional (1D) strings/rings/tubules, two-dimensional (2D) planar sheets and three-dimensional (3D) polyhedral scaffolds. We elucidate various innovative strategies for manipulating proteins to self-assemble into desired architectures. The emergent applications of protein assemblies as versatile platforms in medicine and material science with improved performances have also been discussed.

Two-tier supramolecular encapsulation of small molecules in a protein cage

Expanding protein design to include other molecular building blocks has the potential to increase structural complexity and practical utility. Nature often employs hybrid systems, such as clathrin-coated vesicles, lipid droplets, and lipoproteins, which combine biopolymers and lipids to transport a broader range of cargo molecules. To recapitulate the structure and function of such composite compartments, we devised a supramolecular strategy that enables porous protein cages to encapsulate poorly water-soluble small molecule cargo through templated formation of a hydrophobic surfactant-based core. These lipoprotein-like complexes protect their cargo from sequestration by serum proteins and enhance the cellular uptake of fluorescent probes and cytotoxic drugs. This design concept could be applied to other protein cages, surfactant mixtures, and cargo molecules to generate unique hybrid architectures and functional capabilities.

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.

An enumerative algorithm for de novo design of proteins with diverse pocket structures

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

Learning from Nature to Expand the Genetic Code

The genetic code is the manual that cells use to incorporate amino acids into proteins. It is possible to artificially expand this manual through cellular, molecular, and chemical manipulations to improve protein functionality. Strategies for in vivo genetic code expansion are under the same functional constraints as natural protein synthesis. Here, we review the approaches used to incorporate noncanonical amino acids (ncAAs) into designer proteins through the manipulation of the translation machinery and draw parallels between these methods and natural adaptations that improve translation in extant organisms. Following this logic, we propose new nature-inspired tactics to improve genetic code expansion (GCE) in synthetic organisms.

Beyond natural: synthetic expansions of botanical form and function

Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.

Protein Engineering for Improving and Diversifying Natural Product Biosynthesis

Proteins found in nature have traditionally been the most frequently used biocatalysts to produce numerous natural products ranging from commodity chemicals to pharmaceuticals. Protein engineering has emerged as a powerful biotechnological toolbox in the development of metabolic engineering, particularly for the biosynthesis of natural products. Recently, protein engineering has become a favored method to improve enzymatic activity, increase enzyme stability, and expand product spectra in natural product biosynthesis. This review summarizes recent advances and typical strategies in protein engineering, highlighting the paramount role of protein engineering in improving and diversifying the biosynthesis of natural products. Future prospects and research directions are also discussed.

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.

Catalysis and Electron Transfer in De Novo Designed Helical Scaffolds

The relationship between protein structure and function is one of the greatest puzzles within biochemistry. De novo metalloprotein design is a way to wipe the board clean and determine what is required to build in function from the ground up in an unrelated structure. This Review focuses on protein design efforts to create de novo metalloproteins within alpha-helical scaffolds. Examples of successful designs include those with carbonic anhydrase or nitrite reductase activity by incorporating a ZnHis3 or CuHis3 site, or that recapitulate the spectroscopic properties of unique electron-transfer sites in cupredoxins (CuHis2 Cys) or rubredoxins (FeCys4 ). This work showcases the versatility of alpha helices as scaffolds for metalloprotein design and the progress that is possible through careful rational design. Our studies cover the invariance of carbonic anhydrase activity with different site positions and scaffolds, refinement of our cupredoxin models, and enhancement of nitrite reductase activity up to 1000-fold.

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.

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.

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance of AMP optimization. In this context, the computer-aided design of AMPs has put together crucial information on chemical parameters and bioactivities in AMP sequences, thus providing modes of prediction to evaluate the antibacterial potential of a candidate sequence before synthesis. Quantitative structure-activity relationship (QSAR) computational models, for instance, have greatly contributed to AMP sequence optimization aimed at improved biological activities. In addition to machine-learning methods, the de novo design, linguistic model, pattern insertion methods, and genetic algorithms, have shown the potential to boost the automated design of AMPs. However, how successful have these approaches been in generating effective antibacterial drug candidates? Bearing this in mind, this review will focus on the main computational strategies that have generated AMPs with promising activities against pathogenic bacteria, as well as anti-infective potential in different animal models, including sepsis and cutaneous infections. Moreover, we will point out recent studies on the computer-aided design of antibiofilm peptides. As expected from automated design strategies, diverse candidate sequences with different structural arrangements have been generated and deposited in databases. We will, therefore, also discuss the structural diversity that has been engendered.