Protein design: Past, present, and future

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Exo-chirality of the α-helix

The structure of helical polymers is dictated by the molecular chirality of their monomer units. Particularly, macromolecular helices with monomer sequence control have the potential to generate chiral topologies. In α-helical folded peptides, the sequential repetition of amino acids generates a chiral layer defined by the amino acid side chains projected outside the amide backbone. Despite being closely related to peptides' structural and functional properties, to the best of our knowledge, a general exo-helical symmetry model has not been yet described for the α-helix. Here, we perform the theoretical, computational, and spectroscopic elucidation of the α-helix principal exo-helical topologies. Non-canonical labeled amino acids are employed to spectroscopically characterize the different exo-helical topologies in solution, which precisely match the theorical prediction. Backbone-to-chromophore distance also shows the expected impact in the exo-helices' geometry and spectroscopic fingerprint. Theoretical prediction and spectroscopic validation of this exo-helical topological model provides robust experimental evidence of the chiral potential on the surface of helical peptides and outlines an entirely new structural scenario for the α-helix.

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.

Advances in Rational Protein Engineering toward Functional Architectures and Their Applications in Food Science

Protein biomolecules including enzymes, cagelike proteins, and specific peptides have been continuously exploited as functional biomaterials applied in catalysis, nutrient delivery, and food preservation in food-related areas. However, natural proteins usually function well in physiological conditions, not industrial conditions, or may possess undesirable physical and chemical properties. Currently, rational protein design as a valuable technology has attracted extensive attention for the rational engineering or fabrication of ideal protein biomaterials with novel properties and functionality. This article starts with the underlying knowledge of protein folding and assembly and is followed by the introduction of the principles and strategies for rational protein design. Basic strategies for rational protein engineering involving experienced protein tailoring, computational prediction, computation redesign, and de novo protein design are summarized. Then, we focus on the recent progress of rational protein engineering or design in the application of food science, and a comprehensive summary ranging from enzyme manufacturing to cagelike protein nanocarriers engineering and antimicrobial peptides preparation is given. Overall, this review highlights the importance of rational protein engineering in food biomaterial preparation which could be beneficial for food science.

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?

The G protein coupled receptor CXCR4 designed by the QTY code becomes more hydrophilic and retains cell signaling activity

G protein-coupled receptors (GPCRs) are vital for diverse biological functions, including vision, smell, and aging. They are involved in a wide range of diseases, and are among the most important targets of medicinal drugs. Tools that facilitate GPCR studies or GPCR-based technologies or therapies are thus critical to develop. Here we report using our QTY (glutamine, threonine, tyrosine) code to systematically replace 29 membrane-facing leucine, isoleucine, valine, and phenylalanine residues in the transmembrane α-helices of the GPCR CXCR4. This variant, CXCR4^QTY29, became more hydrophilic, while retaining the ability to bind its ligand CXCL12. When transfected into HEK293 cells, it inserted into the cell membrane, and initiated cellular signaling. This QTY code has the potential to improve GPCR and membrane protein studies by making it possible to design functional hydrophilic receptors. This tool can be applied to diverse α-helical membrane proteins, and may aid in the development of other applications, including clinical therapies.

A complete rule set for designing symmetry combination materials from protein molecules

Diverse efforts in protein engineering are beginning to produce novel kinds of symmetric self-assembling architectures, from protein cages to extended two-dimensional (2D) and three-dimensional (3D) crystalline arrays. Partial theoretical frameworks for creating symmetric protein materials have been introduced, but no complete system has been articulated. Only a minute fraction of the possible design space has been explored experimentally, in part because that space has not yet been described in theory. Here, in the form of a multiplication table, we lay out a complete rule set for materials that can be created by combining two chiral oligomeric components (e.g., proteins) in precise configurations. A unified system is described for parameterizing and searching the construction space for all such symmetry-combination materials (SCMs). In total, 124 distinct types of SCMs are identified, and then proven by computational construction. Mathematical properties, such as minimal ring or circuit size, are established for each case, enabling strategic predictions about potentially favorable design targets. The study lays out the theoretical landscape and detailed computational prescriptions for a rapidly growing area of protein-based nanotechnology, with numerous underlying connections to mathematical networks and chemical materials such as metal organic frameworks.

Automated solid-phase concatenation of Aib residues to form long, water-soluble, helical peptides

Iterative coupling of 2-aminoisobutyric acid (Aib) has been achieved rapidly and efficiently using automated solid-phase peptide synthesis, employing diisopropylcarbodiimide (DIC) in the presence of ethyl cyanohydroxyiminoacetate (Oxyma). This method has allowed the first total synthesis of the fungal antibiotic Cephaibol D, and enabled the synthesis of water-soluble oligomers of Aib containing up to an unprecedented sequence of 17 consecutive Aib residues. Conformational analysis of the Aib oligomers in aqueous solution shows a length dependence in their CD spectra, with oligomers of more than 14 Aib residues apparently adopting structured helical conformations.

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.

Sequence-based prediction of protein binding mode landscapes

Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways.

Great advances have been made in the last several decades in deciphering how the behavior of proteins is encoded in their amino acid sequences. A variety of sequence-based prediction methods have been developed to estimate a wide range of properties of proteins, including secondary structure propensity, native state structures, preference for being disordered and tendency to aggregate. Much less is known, however, about the rules that regulate the conformational changes of proteins upon binding. In particular, many proteins change their binding modes upon interacting with different partners, or as a consequence of post-translational modifications or changes in the cellular milieu. Here we address the problem of how amino acid sequences can encode different binding modes depending on their binding partners, and describe the FuzPred method of predicting context-dependent binding modes.

Peptide Assembly Directed and Quantified Using Megadalton DNA Nanostructures

In nature, co-assembly of polypeptides, nucleic acids, and polysaccharides is used to create functional supramolecular structures. Here, we show that DNA nanostructures can be used to template interactions between peptides and to enable the quantification of multivalent interactions that would otherwise not be observable. Our functional building blocks are peptide-oligonucleotide conjugates comprising de novo designed dimeric coiled-coil peptides covalently linked to oligonucleotide tags. These conjugates are incorporated in megadalton DNA origami nanostructures and direct nanostructure association through peptide-peptide interactions. Free and bound nanostructures can be counted directly from electron micrographs, allowing estimation of the dissociation constants of the peptides linking them. Results for a single peptide-peptide interaction are consistent with the measured solution-phase free energy; DNA nanostructures displaying multiple peptides allow the effects of polyvalency to be probed. This use of DNA nanostructures as identifiers allows the binding strengths of homo- and heterodimeric peptide combinations to be measured in a single experiment and gives access to dissociation constants that are too low to be quantified by conventional techniques. The work also demonstrates that hybrid biomolecules can be programmed to achieve spatial organization of complex synthetic biomolecular assemblies.

Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations

Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process.

Navigating the Structural Landscape of De Novo α-Helical Bundles

The association of amphipathic α helices in water leads to α-helical-bundle protein structures. However, the driving force for this-the hydrophobic effect-is not specific and does not define the number or the orientation of helices in the associated state. Rather, this is achieved through deeper sequence-to-structure relationships, which are increasingly being discerned. For example, for one structurally extreme but nevertheless ubiquitous class of bundle-the α-helical coiled coils-relationships have been established that discriminate between all-parallel dimers, trimers, and tetramers. Association states above this are known, as are antiparallel and mixed arrangements of the helices. However, these alternative states are less well understood. Here, we describe a synthetic-peptide system that switches between parallel hexamers and various up-down-up-down tetramers in response to single-amino-acid changes and solution conditions. The main accessible states of each peptide variant are characterized fully in solution and, in most cases, to high resolution with X-ray crystal structures. Analysis and inspection of these structures helps rationalize the different states formed. This navigation of the structural landscape of α-helical coiled coils above the dimers and trimers that dominate in nature has allowed us to design rationally a well-defined and hyperstable antiparallel coiled-coil tetramer (apCC-Tet). This robust de novo protein provides another scaffold for further structural and functional designs in protein engineering and synthetic biology.

Self-assembly of photochromic diarylethene-peptide conjugates stabilized by β-sheet formation at the liquid/graphite interface

Two-dimensional (2-D) self-assembly of diarylethene (DAE)-peptide conjugates at the octanoic acid/graphite interface was investigated by scanning tunnelling microscopy (STM). The open-ring isomer of a DAE-peptide conjugate formed a stable 2-D molecular assembly with an antiparallel β-sheet structure. Quantitative analysis of surface coverage depending on concentration revealed a stronger stabilization effect of the oligopeptide than that of the alkyl group with a similar side chain length.

De Novo-Designed α-Helical Barrels as Receptors for Small Molecules

We describe de novo-designed α-helical barrels (αHBs) that bind and discriminate between lipophilic biologically active molecules. αHBs have five or more α-helices arranged around central hydrophobic channels the diameters of which scale with oligomer state. We show that pentameric, hexameric, and heptameric αHBs bind the environmentally sensitive dye 1,6-diphenylhexatriene (DPH) in the micromolar range and fluoresce. Displacement of the dye is used to report the binding of nonfluorescent molecules: palmitic acid and retinol bind to all three αHBs with submicromolar inhibitor constants; farnesol binds the hexamer and heptamer; but β-carotene binds only the heptamer. A co-crystal structure of the hexamer with farnesol reveals oriented binding in the center of the hydrophobic channel. Charged side chains engineered into the lumen of the heptamer facilitate binding of polar ligands: a glutamate variant binds a cationic variant of DPH, and introducing lysine allows binding of the biosynthetically important farnesol diphosphate.

ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design

Motivation

The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users.

Results

Here we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the ‘dark matter of protein-fold space’. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology.

Availability and implementation

A current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper.

Supplementary information

Supplementary data are available at Bioinformatics online.

Coiled coil protein origami: from modular design principles towards biotechnological applications

This review illustrates the current state in designing coiled-coil-based proteins with an emphasis on coiled coil protein origami structures and their potential.

Binding and Energetics of Electron Transfer between an Artificial Four-Helix Mn-Protein and Reaction Centers from Rhodobacter sphaeroides

The ability of an artificial four-helix bundle Mn-protein, P1, to bind and transfer an electron to photosynthetic reaction centers from the purple bacterium Rhodobacter sphaeroides was characterized using optical spectroscopy. Upon illumination of reaction centers, an electron is transferred from P, the bacteriochlorophyll dimer, to Q_A, the primary electron acceptor. The P1 Mn-protein can bind to the reaction center and reduce the oxidized bacteriochlorophyll dimer, P⁺, with a dissociation constant of 1.2 μM at pH 9.4, comparable to the binding constant of c-type cytochromes. Amino acid substitutions of surface residues on the Mn-protein resulted in increases in the dissociation constant to 8.3 μM. The extent of reduction of P⁺ by the P1 Mn-protein was dependent on the P/P⁺ midpoint potential and the pH. Analysis of the free energy difference yielded a midpoint potential of approximately 635 mV at pH 9.4 for the Mn cofactor of the P1 Mn-protein, a value similar to those found for other Mn cofactors in proteins. The linear dependence of -56 mV/pH is consistent with one proton being released upon Mn oxidation, allowing the complex to maintain overall charge neutrality. These outcomes demonstrate the feasibility of designing four-helix bundles and other artificial metalloproteins to bind and transfer electrons to bacterial reaction centers and establish the usefulness of this system as a platform for designing sites to bind novel metal cofactors capable of performing complex oxidation-reduction reactions.

Biochemical and spectroscopic characterization of dinuclear Mn-sites in artificial four-helix bundle proteins

To better understand metalloproteins with Mn-clusters, we have designed artificial four-helix bundles to have one, two, or three dinuclear metal centers able to bind Mn(II). Circular dichroism measurements showed that the Mn-proteins have substantial α-helix content, and analysis of electron paramagnetic resonance spectra is consistent with the designed number of bound Mn-clusters. The Mn-proteins were shown to catalyze the conversion of hydrogen peroxide into molecular oxygen. The loss of hydrogen peroxide was dependent upon the concentration of protein with bound Mn, with the proteins containing multiple Mn-clusters showing greater activity. Using an oxygen sensor, the oxygen concentration was found to increase with a rate up to 0.4μM/min, which was dependent upon the concentrations of hydrogen peroxide and the Mn-protein. In addition, the Mn-proteins were shown to serve as electron donors to bacterial reaction centers using optical spectroscopy. Similar binding of the Mn-proteins to reaction centers was observed with an average dissociation constant of 2.3μM. The Mn-proteins with three metal centers were more effective at this electron transfer reaction than the Mn-proteins with one or two metal centers. Thus, multiple Mn-clusters can be incorporated into four-helix bundles with the capability of performing catalysis and electron transfer to a natural protein.

Packing in protein cores

Proteins are biological polymers that underlie all cellular functions. The first high-resolution protein structures were determined by x-ray crystallography in the 1960s. Since then, there has been continued interest in understanding and predicting protein structure and stability. It is well-established that a large contribution to protein stability originates from the sequestration from solvent of hydrophobic residues in the protein core. How are such hydrophobic residues arranged in the core; how can one best model the packing of these residues, and are residues loosely packed with multiple allowed side chain conformations or densely packed with a single allowed side chain conformation? Here we show that to properly model the packing of residues in protein cores it is essential that amino acids are represented by appropriately calibrated atom sizes, and that hydrogen atoms are explicitly included. We show that protein cores possess a packing fraction of [Formula: see text], which is significantly less than the typically quoted value of 0.74 obtained using the extended atom representation. We also compare the results for the packing of amino acids in protein cores to results obtained for jammed packings from discrete element simulations of spheres, elongated particles, and composite particles with bumpy surfaces. We show that amino acids in protein cores pack as densely as disordered jammed packings of particles with similar values for the aspect ratio and bumpiness as found for amino acids. Knowing the structural properties of protein cores is of both fundamental and practical importance. Practically, it enables the assessment of changes in the structure and stability of proteins arising from amino acid mutations (such as those identified as a result of the massive human genome sequencing efforts) and the design of new folded, stable proteins and protein-protein interactions with tunable specificity and affinity.

Coiled-Coil Design: Updated and Upgraded

α-Helical coiled coils are ubiquitous protein-folding and protein-interaction domains in which two or more α-helical chains come together to form bundles. Through a combination of bioinformatics analysis of many thousands of natural coiled-coil sequences and structures, plus empirical protein engineering and design studies, there is now a deep understanding of the sequence-to-structure relationships for this class of protein architecture. This has led to considerable success in rational design and what might be termed in biro de novo design of simple coiled coils, which include homo- and hetero-meric parallel dimers, trimers and tetramers. In turn, these provide a toolkit for directing the assembly of both natural proteins and more complex designs in protein engineering, materials science and synthetic biology. Moving on, the increased and improved use of computational design is allowing access to coiled-coil structures that are rare or even not observed in nature, for example α-helical barrels, which comprise five or more α-helices and have central channels into which different functions may be ported. This chapter reviews all of these advances, outlining improvements in our knowledge of the fundamentals of coiled-coil folding and assembly, and highlighting new coiled coil-based materials and applications that this new understanding is opening up. Despite considerable progress, however, challenges remain in coiled-coil design, and the next decade promises to be as productive and exciting as the last.

A designed repeat protein as an affinity capture reagent

Repeat proteins are an attractive target for protein engineering and design. We have focused our attention on the design and engineering of one particular class: tetratricopeptide repeat (TPR) proteins. In previous work, we have shown that the structure and stability of TPR proteins can be manipulated in a rational fashion [Cortajarena (2011) Prot. Sci. 20: , 1042-1047; Main (2003) Structure 11: , 497-508]. Building on those studies, we have designed and characterized a number of different peptide-binding TPR modules and we have also assembled these modules into supramolecular arrays [Cortajarena (2009) ACS Chem. Biol. 5: , 545-552; Cortajarena (2008) ACS Chem. Biol. 3: , 161-166; Jackrel (2009) Prot. Sci. 18: , 762-774; Kajander (2007) Acta Crystallogr. D Biol. Crystallogr. 63: , 800-811]. Here we focus on the development of one such TPR-peptide interaction for a practical application, affinity purification. We illustrate the general utility of our designed protein interaction. Furthermore, this example highlights how basic research on protein-peptide interactions can lead to the development of novel reagents with important practical applications.

Protein rethreading: A novel approach to protein design

Protein engineering is an important tool for the design of proteins with novel and desirable features. Templates from the protein databank (PDB) are often used as initial models that can be modified to introduce new properties. We examine whether it is possible to reconnect a protein in a manner that generates a new topology yet preserves its structural integrity. Here, we describe the rethreading of dihydrofolate reductase (DHFR) from E. coli (wtDHFR). The rethreading process involved the removal of three native loops, and the introduction of three new loops with alternate connections. The structure of the rethreaded DHFR (rDHFR-1) was determined to 1.6 Å, demonstrating the success of the rethreading process. Both wtDHFR and rDHFR-1 exhibited similar affinities towards methotrexate. However, rDHFR-1 showed no reducing activity towards dihydrofolate, and exhibited about ~6-fold lower affinity towards NADPH than wtDHFR. This work demonstrates that protein rethreading can be a powerful tool for the design of a large array of proteins with novel structures and topologies, and that by careful rearrangement of a protein sequence, the sequence to structure relationship can be expanded substantially.

Protein engineering strategies with potential applications for altering clinically relevant cellular pathways at the protein level

All diseases can be fundamentally viewed as the result of malfunctioning cellular pathways. Protein engineering offers the potential to develop new tools that will allow these dysfunctional pathways to be better understood, in addition to potentially providing new routes to restore proper function. Here we discuss different approaches that can be used to change the intracellular activity of a protein by intervening at the protein level: targeted protein sequestration, protein recruitment, protein degradation, and selective inhibition of binding interfaces. The potential of each of these tools to be developed into effective therapeutic treatments will also be discussed, along with any major barriers that currently block their translation into the clinic.

Using the folding landscapes of proteins to understand protein function

Proteins fold on a biologically-relevant timescale because of a funnel-shaped energy landscape. This landscape is sculpted through evolution by selecting amino-acid sequences that stabilize native interactions while suppressing stable non-native interactions that occur during folding. However, there is strong evolutionary selection for functional residues and these cannot be chosen to optimize folding. Their presence impacts the folding energy landscape in a variety of ways. Here, we survey the effects of functional residues on folding by providing several examples. We then review how such effects can be detected computationally and be used as assays for protein function. Overall, an understanding of how functional residues modulate folding should provide insights into the design of natural proteins and their homeostasis.

Protein sequence design and its applications

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

Protein cages and synthetic polymers: a fruitful symbiosis for drug delivery applications, bionanotechnology and materials science

Protein cages have become essential tools in bionanotechnology due to their well-defined, monodisperse, capsule-like structure. Combining them with synthetic polymers greatly expands their application, giving rise to novel nanomaterials fore.g.drug-delivery, sensing, electronic devices and for uses as nanoreactors.