Protein engineering 20 years on

Generating Site Saturation Mutagenesis Libraries and Transferring Them to Broad Host-Range Plasmids Using Golden Gate Cloning

Protein engineering is an established method for tailoring enzymatic reactivity. A commonly used method is directed evolution, where the mutagenesis and natural selection process is mimicked and accelerated in the laboratory. Here, we describe a reliable method for generating saturation mutagenesis libraries by Golden Gate cloning in a broad host range plasmid containing the pBBR1 replicon. The applicability is demonstrated by generating a mutant library of the iron nitrogenase gene cluster (anfHDGK) of Rhodobacter capsulatus, which is subsequently screened for the improved formation of molecular hydrogen.

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort

Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein's fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.

Heliorhodopsin-mediated light-modulation of ABC transporter

Heliorhodopsins (HeRs) have been hypothesized to have widespread functions. Recently, the functions for few HeRs have been revealed; however, the hypothetical functions remain largely unknown. Herein, we investigate light-modulation of heterodimeric multidrug resistance ATP-binding cassette transporters (OmrDE) mediated by Omithinimicrobium cerasi HeR. In this study, we classifiy genes flanking the HeR-encoding genes and identify highly conservative residues for protein-protein interactions. Our results reveal that the interaction between OcHeR and OmrDE shows positive cooperatively sequential binding through thermodynamic parameters. Moreover, light-induced OcHeR upregulates OmrDE drug transportation. Hence, the binding may be crucial to drug resistance in O. cerasi as it survives in a drug-containing habitat. Overall, we unveil a function of HeR as regulatory rhodopsin for multidrug resistance. Our findings suggest potential applications in optogenetic technology.

A systematic analysis of regression models for protein engineering

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

Guiding discovery of protein sequence-structure-function modeling

MOTIVATION

Protein engineering techniques are key in designing novel catalysts for a wide range of reactions. Although approaches vary in their exploration of the sequence-structure-function paradigm, they are often hampered by the labor-intensive steps of protein expression and screening. In this work, we describe the development and testing of a high-throughput in silico sequence-structure-function pipeline using AlphaFold2 and fast Fourier transform docking that is benchmarked with enantioselectivity and reactivity predictions for an ancestral sequence library of fungal flavin-dependent monooxygenases.

RESULTS

The predicted enantioselectivities and reactivities correlate well with previously described screens of an experimentally available subset of these proteins and capture known changes in enantioselectivity across the phylogenetic tree representing ancestorial proteins from this family. With this pipeline established as our functional screen, we apply ensemble decision tree models and explainable AI techniques to build sequence-function models and extract critical residues within the binding site and the second-sphere residues around this site. We demonstrate that the top-identified key residues in the control of enantioselectivity and reactivity correspond to experimentally verified residues. The in silico sequence-to-function pipeline serves as an accelerated framework to inform protein engineering efforts from vast informative sequence landscapes contained in protein families, ancestral resurrects, and directed evolution campaigns.

AVAILABILITY

Jupyter notebooks detailing the sequence-structure-function pipeline are available at https://github.com/BrooksResearchGroup-UM/seq_struct_func.

Glycine N-Thiocarboxyanhydride: A Key to Glycine-Rich Protein Mimics

Glycine-rich proteins (GRPs) containing a high content of glycine residues (>30%) possess unique structural stability. However, the controllable synthesis of glycine-rich poly(amino acid)s (PAAs) to mimic GRPs has not been realized yet due to the poor solubility of polyglycine segments. We developed a novel method to synthesize glycine-rich PAAs via the controlled ring-opening copolymerization of glycine-N-thiocarboxyanhydrides (Gly-NTA) with sarcosine-N-carboxyanhydride and ε-Cbz-l-lysine-N-carboxyanhydride. The random copolymerization is evidenced by a kinetic study that shows that the propagation rate constant of Gly-NTA is close to those of comonomers. The copolymers exhibit predictable molecular weights between 4.5 and 24.6 kg/mol and tunable glycine incorporation, varying from 10.3 to 59.2%. Poly(Gly-r-Sar) samples with various glycine contents form nanoparticles or a hydrogel in water. Remarkably, the β-sheet folding of poly(Gly-r-Lys) remains intact in a neutral environment where the amine groups are protonated. Overall, the strategy paves the way to engineer glycine-rich PAAs and thereby expands their applications.

Protein engineering of pores for separation, sensing, and sequencing

Proteins are critical to cellular function and survival. They are complex molecules with precise structures and chemistries, which allow them to serve diverse functions for maintaining overall cell homeostasis. Since the discovery of the first enzyme in 1833, a gamut of advanced experimental and computational tools has been developed and deployed for understanding protein structure and function. Recent studies have demonstrated the ability to redesign/alter natural proteins for applications in industrial processes of interest and to make customized, novel synthetic proteins in the laboratory through protein engineering. We comprehensively review the successes in engineering pore-forming proteins and correlate the amino acid-level biochemistry of different pore modification strategies to the intended applications limited to nucleotide/peptide sequencing, single-molecule sensing, and precise molecular separations.

Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration

Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD⁺/NADH and NADP⁺/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD⁺/NADH and NADP⁺/NADPH pairs, and of non-canonical redox cofactor pairs NMN⁺/NMNH and NCD⁺/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.

Engineering Candida boidinii formate dehydrogenase for activity with the non-canonical cofactor 3'-NADP(H)

Oxidoreductases catalyze essential redox reactions, and many require a diffusible cofactor for electron transport, such as NAD(H). Non-canonical cofactor analogs have been explored as a means to create enzymatic reactions that operate orthogonally to existing metabolism. Here, we aimed to engineer the formate dehydrogenase from Candid boidinii (CbFDH) for activity with the non-canonical cofactor nicotinamide adenine dinucleotide 3'-phosphate (3'-NADP(H)). We used PyRosetta, the Cofactor Specificity Reversal Structural Analysis and Library Design (CSR-SALAD), and structure-guided saturation mutagenesis to identify mutations that enable CbFDH to use 3'-NADP+. Two single mutants, D195A and D195G, had the highest activities with 3'-NADP+, while the double mutant D195G/Y196S exhibited the highest cofactor selectivity reversal behavior. Steady state kinetic analyses were performed; the D195A mutant exhibited the highest KTS value with 3'-NADP+. This work compares the utility of computational approaches for cofactor specificity engineering while demonstrating the engineering of an important enzyme for novel non-canonical cofactor selectivity.

Using Intrinsic Fluorescence to Measure Protein Stability Upon Thermal and Chemical Denaturation

Understanding how point mutations affect the performance of protein stability has been the focus of several studies all over the years. Intrinsic fluorescence is commonly used to follow protein unfolding since during denaturation, progressive redshifts on tryptophan fluorescence emission are observed. Since the unfolding process (achieved by chemical or physical denaturants) can be considered as two-state N➔D, it is possible to utilize the midpoint unfolding curves (fU = 50%) as a parameter to evaluate if the mutation destabilizes wild-type protein. The idea is to determine the [D]_1/2 or T_m values from both wild type and mutant and calculate the difference between them. Positive values indicate the mutant is less stable than wild type.

Protposer: the web server that readily proposes protein stabilizing mutations with high PPV

Protein stability is a requisite for most biotechnological and medical applications of proteins. As natural proteins tend to suffer from a low conformational stability ex vivo, great efforts have been devoted toward increasing their stability through rational design and engineering of appropriate mutations. Unfortunately, even the best currently used predictors fail to compute the stability of protein variants with sufficient accuracy and their usefulness as tools to guide the rational stabilisation of proteins is limited. We present here Protposer, a protein stabilising tool based on a different approach. Instead of quantifying changes in stability, Protposer uses structure- and sequence-based screening modules to nominate candidate mutations for subsequent evaluation by a logistic regression model, carefully trained to avoid overfitting. Thus, Protposer analyses PDB files in search for stabilization opportunities and provides a ranked list of promising mutations with their estimated success rates (eSR), their probabilities of being stabilising by at least 0.5 kcal/mol. The agreement between eSRs and actual positive predictive values (PPV) on external datasets of mutations is excellent. When Protposer is used with its Optimal kappa selection threshold, its PPV is above 0.7. Even with less stringent thresholds, Protposer largely outperforms FoldX, Rosetta and PoPMusiC. Indicating the PDB file of the protein suffices to obtain a ranked list of mutations, their eSRs and hints on the likely source of the stabilization expected. Protposer is a distinct, straightforward and highly successful tool to design protein stabilising mutations, and it is freely available for academic use at http://webapps.bifi.es/the-protposer.

Enhancement of Methane Catalysis Rates in Methylosinus trichosporium OB3b

Particulate methane monooxygenase (pMMO), a membrane-bound enzyme having three subunits (α, β, and γ) and copper-containing centers, is found in most of the methanotrophs that selectively catalyze the oxidation of methane into methanol. Active sites in the pMMO of Methylosinus trichosporium OB3b were determined by docking the modeled structure with ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene. The docking energy between the modeled pMMO structure and ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene was -5.2, -5.7, -4.2, and -3.8 kcal/mol, respectively, suggesting the existence of more than one active site within the monomeric subunits due to the presence of multiple binding sites within the pMMO monomer. The evaluation of tunnels and cavities of the active sites and the docking results showed that each active site is specific to the radius of the substrate. To increase the catalysis rates of methane in the pMMO of M. trichosporium OB3b, selected amino acid residues interacting at the binding site of ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene were mutated. Based on screening the strain energy, docking energy, and physiochemical properties, five mutants were downselected, B:Leu31Ser, B:Phe96Gly, B:Phe92Thr, B:Trp106Ala, and B:Tyr110Phe, which showed the docking energy of -6.3, -6.7, -6.3, -6.5, and -6.5 kcal/mol, respectively, as compared to the wild type (-5.2 kcal/mol) with ethylbenzene. These results suggest that these five mutants would likely increase methane oxidation rates compared to wild-type pMMO.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Looking Back: A Short History of the Discovery of Enzymes and How They Became Powerful Chemical Tools

Enzymatic approaches to challenges in chemical synthesis are increasingly popular and very attractive to industry given their green nature and high efficiency compared to traditional methods. In this historical review we highlight the developments across several fields that were necessary to create the modern field of biocatalysis, with enzyme engineering and directed evolution at its core. We exemplify the modular, incremental, and highly unpredictable nature of scientific discovery, driven by curiosity, and showcase the resulting examples of cutting-edge enzymatic applications in industry.

Synthesis, characterization and immobilization of bilirubin oxidase nanoparticles (BOxNPs) with enhanced activity: Application for serum bilirubin determination in jaundice patients

A high- power ultrasonic method was used to prepare bilirubin oxidase nanoparticles (BOxNPs) which were immobilized on polyethylene (PE) film. The characterization of PE film bound to BOxNPs and BOxNPs was carried out using "Dynamic Light Scattering (DLS)," "Transmission Electron Microscopy (TEM)," and "Scanning Electron Microscopy (SEM)." The PE film was treated with nitric acid (HNO₃) for its activation. BOxNPs bound to PE film exhibited optimal activity (pH-8), incubation time (11 s) with temperature 35 °C. A linear relationship was observed between the bilirubin concentrations (0.02-250 μM), with an apparent Km value and Vmax for PE- bound BOxNPs, at 0.015 μM and 2.56 μmol/mL/min. The mean recoveries of added serum bilirubin were 94.5 % at a level of 5 mM whereas 98.5 % were observed at 10 mM which showed the satisfactory reliability of BOxNPs immobilized on PE film. The coefficient of variation for serum bilirubin ranged between 4.52%-5.25%, measured on the first day (within batch) and after seven days of storage (between batch).This current method has showed a good correlation for bilirubin values when compared to the standard enzymatic colorimetric method using free enzyme. BOxNPs bound to PE film were reutilized 150 times with storage at 4 °C for 120 days.

Nucleophilic Transformations of Lewis Acid-Activated Disubstituted Epoxides with Catalyst-Controlled Regioselectivity

Due to their inherent ring strain and electrophilicity, epoxides are highly attractive building blocks for fundamental organic reactions. However, controlling the regioselectivity of disubstituted epoxide transformations is often particularly challenging. Most Lewis acid-mediated processes take advantage of intrinsic steric or electronic substrate bias to influence the site of nucleophilic attack. Therefore, the scope of many of these systems is frequently quite limited. Recent efforts to generate catalysts that can overcome substrate bias have expanded the synthetic utility of these well-known reactions. In this Perspective, we highlight various regioselective transformations of disubstituted epoxides, emphasizing those that have inspired the production of challenging, catalyst-controlled processes.

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

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

Decreasing the immunogenicity of arginine deiminase enzyme via structure-based computational analysis

The clinical applications of therapeutic enzymes are often limited due to their immunogenicity. B-cell epitope removal is an effective approach to solve this obstacle. The identification of hot spot epitopic residues is a critical step in the removal of protein B-cell epitope. Hereof, computational approaches are a suitable alternative to costly and labor-intensive experimental approaches. Arginine deiminase, a Mycoplasma arginine-catabolizing enzyme, is in the clinical trial for treating arginine auxotrophic cancers, especially hepatocellular carcinomas and melanomas through depleting plasma arginine and causing cell starvation. In this study, arginine deiminase from Mycoplasma hominis (MhADI) was computationally analyzed for recognizing and locating its immune-reactive regions. The 3D structure of the bioactive form of MhADI was modeled. The B-cell epitope mapping of protein was performed using various servers with different algorithms. Six segments: 31-40, 48-55, 131-140, 196-206, 294-314, and 331-344 were predicted to be the consensus immunogenic regions. The modification of epitopic hot spot residue was performed to reduce immune-reactiveness. The hot spot residue was selected considering a high B-cell epitope score, convexity index, surface accessibility, flexibility, and hydrophilicity. The structure stability of native and mutant proteins was evaluated through molecular dynamics simulation. The E304L mutein was suggested as a lower antigenic and stable enzyme derivative.

An Activator-Blocker Pair Provides a Controllable On-Off Switch for a Ketosteroid Isomerase Active Site Mutant

Control of enzyme activity is fundamental to biology and represents a long-term goal in bioengineering and precision therapeutics. While several powerful molecular strategies have been developed, limitations remain in their generalizability and dynamic range. We demonstrate a control mechanism via separate small molecules that turn on the enzyme (activator) and turn off the activation (blocker). We show that a pocket created near the active site base of the enzyme ketosteriod isomerase (KSI) allows efficient and saturable base rescue when the enzyme's natural general base is removed. Binding a small molecule with similar properties but lacking general-base capability in this pocket shuts off rescue. The ability of small molecules to directly participate in and directly block catalysis may afford a broad controllable dynamic range. This approach may be amenable to numerous enzymes and to engineering and screening approaches to identify activators and blockers with strong, specific binding for engineering and therapeutic applications.

Biomolecular engineering for nanobio/bionanotechnology

Biomolecular engineering can be used to purposefully manipulate biomolecules, such as peptides, proteins, nucleic acids and lipids, within the framework of the relations among their structures, functions and properties, as well as their applicability to such areas as developing novel biomaterials, biosensing, bioimaging, and clinical diagnostics and therapeutics. Nanotechnology can also be used to design and tune the sizes, shapes, properties and functionality of nanomaterials. As such, there are considerable overlaps between nanotechnology and biomolecular engineering, in that both are concerned with the structure and behavior of materials on the nanometer scale or smaller. Therefore, in combination with nanotechnology, biomolecular engineering is expected to open up new fields of nanobio/bionanotechnology and to contribute to the development of novel nanobiomaterials, nanobiodevices and nanobiosystems. This review highlights recent studies using engineered biological molecules (e.g., oligonucleotides, peptides, proteins, enzymes, polysaccharides, lipids, biological cofactors and ligands) combined with functional nanomaterials in nanobio/bionanotechnology applications, including therapeutics, diagnostics, biosensing, bioanalysis and biocatalysts. Furthermore, this review focuses on five areas of recent advances in biomolecular engineering: (a) nucleic acid engineering, (b) gene engineering, (c) protein engineering, (d) chemical and enzymatic conjugation technologies, and (e) linker engineering. Precisely engineered nanobiomaterials, nanobiodevices and nanobiosystems are anticipated to emerge as next-generation platforms for bioelectronics, biosensors, biocatalysts, molecular imaging modalities, biological actuators, and biomedical applications.

Posttranslational mutagenesis: A chemical strategy for exploring protein side-chain diversity

Posttranslational modification of proteins expands their structural and functional capabilities beyond those directly specified by the genetic code. However, the vast diversity of chemically plausible (including unnatural but functionally relevant) side chains is not readily accessible. We describe C (sp³)-C (sp³) bond-forming reactions on proteins under biocompatible conditions, which exploit unusual carbon free-radical chemistry, and use them to form Cβ-Cγ bonds with altered side chains. We demonstrate how these transformations enable a wide diversity of natural, unnatural, posttranslationally modified (methylated, glycosylated, phosphorylated, hydroxylated), and labeled (fluorinated, isotopically labeled) side chains to be added to a common, readily accessible dehydroalanine precursor in a range of representative protein types and scaffolds. This approach, outside of the rigid constraints of the ribosome and enzymatic processing, may be modified more generally for access to diverse proteins.

Modulating Mobility: a Paradigm for Protein Engineering?

Proteins are highly mobile structures. In addition to gross conformational changes occurring on, for example, ligand binding, they are also subject to constant thermal motion. The mobility of a protein varies through its structure and can be modulated by ligand binding and other events. It is becoming increasingly clear that this mobility plays an important role in key functions of proteins including catalysis, allostery, cooperativity, and regulation. Thus, in addition to an optimum structure, proteins most likely also require an optimal dynamic state. Alteration of this dynamic state through protein engineering will affect protein function. A dramatic example of this is seen in some inherited metabolic diseases where alternation of residues distant from the active site affects the mobility of the protein and impairs function. We postulate that using molecular dynamics simulations, experimental data or a combination of the two, it should be possible to engineer the mobility of active sites. This may be useful in, for example, increasing the promiscuity of enzymes. Thus, a paradigm for protein engineering is suggested in which the mobility of the active site is rationally modified. This might be combined with more "traditional" approaches such as altering functional groups in the active site.

Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies

In many cases the stability of a protein has to be increased to permit its biotechnological use. Rational methods of protein stabilization based on optimizing electrostatic interactions have provided some fine successful predictions. However, the precise calculation of stabilization energies remains challenging, one reason being that the electrostatic effects on the unfolded state are often neglected. We have explored here the feasibility of incorporating Poisson-Boltzmann model electrostatic calculations performed on representations of the unfolded state as large ensembles of geometrically optimized conformations calculated using the ProtSA server. Using a data set of 80 electrostatic mutations experimentally tested in two-state proteins, the predictive performance of several such models has been compared to that of a simple one that considers an unfolded structure of non-interacting residues. The unfolded ensemble models, while showing correlation between the predicted stabilization values and the experimental ones, are worse than the simple model, suggesting that the ensembles do not capture well the energetics of the unfolded state. A more attainable goal is classifying potential mutations as either stabilizing or non-stabilizing, rather than accurately calculating their stabilization energies. To implement a fast classification method that can assist in selecting stabilizing mutations, we have used a much simpler electrostatic model based only on the native structure and have determined its precision using different stabilizing energy thresholds. The binary classifier developed finds 7 true stabilizing mutants out of every 10 proposed candidates and can be used as a robust tool to propose stabilizing mutations.

Gγ recruitment systems specifically select PPI and affinity-enhanced candidate proteins that interact with membrane protein targets

Protein-protein interactions (PPIs) are crucial for the vast majority of biological processes. We previously constructed a Gγ recruitment system to screen PPI candidate proteins and desirable affinity-altered (affinity-enhanced and affinity-attenuated) protein variants. The methods utilized a target protein fused to a mutated G-protein γ subunit (Gγ_cyto) lacking the ability to localize to the inner leaflet of the plasma membrane. However, the previous systems were adapted to use only soluble cytosolic proteins as targets. Recently, membrane proteins have been found to form the principal nodes of signaling involved in diseases and have attracted a great deal of interest as primary drug targets. Here, we describe new protocols for the Gγ recruitment systems that are specifically designed to use membrane proteins as targets to overcome previous limitations. These systems represent an attractive approach to exploring novel interacting candidates and affinity-altered protein variants and their interactions with proteins on the inner side of the plasma membrane, with high specificity and selectivity.

Designing Conservation Relations in Layered Synthetic Biomolecular Networks

In Synthetic Biology, biomolecular networks are designed and constructed to perform specified tasks. Design strategies for these networks tend to center on tuning the parameters of mathematical models to achieve a specified behavior, and implementing these parameters experimentally. This design strategy often assumes a fixed network structure that defines the possible behaviors, which may be too restrictive for our purposes. This paper investigates the extent to which the state space of a synthetic network can also be designed and shaped by parametric tuning. We exploit timescale separation to implement new, nonlinear, tunable conservation relations that hold for all times beyond a fast transient. We demonstrate an application of this design strategy by flexibly constraining the possible behaviors of a gene regulatory network through the design of fast protein interactions.

The Iterative Protein Redesign and Optimization (IPRO) suite of programs

Proteins are an important class of biomolecules with applications spanning across biotechnology and medicine. In many cases, native proteins must be redesigned to improve various performance metrics by changing their amino acid sequences. Algorithms can help sharpen protein library design by focusing the library on sequences that optimize computationally accessible proxies. The Iterative Protein Redesign and Optimization (IPRO) suite of programs offers an integrated environment for (1) altering protein binding affinity and specificity, (2) grafting a binding pocket into an existing protein scaffold, (3) predicting an antibody's tertiary structure based on its sequence, (4) enhancing enzymatic activity, and (5) assessing the structure and binding energetics for a specific mutant. This manuscript provides an overview of the methods involved in IPRO, input language terminology, algorithmic details, software implementation specifics and application highlights. IPRO can be downloaded at http://maranas.che.psu.edu.

Side effects of cytokines approved for therapy

Cytokines, currently known to be more than 130 in number, are small MW (<30 kDa) key signaling proteins that modulate cellular activities in immunity, infection, inflammation and malignancy. Key to understanding their function is recognition of their pleiotropism and often overlapping and functional redundancies. Classified here into 9 main families, most of the 20 approved cytokine preparations (18 different cytokines; 3 pegylated), all in recombinant human (rh) form, are grouped in the hematopoietic growth factor, interferon, platelet-derived growth factor (PDGF) and transforming growth factor β (TGFβ) families. In the hematopoietin family, approved cytokines are aldesleukin (rhIL-2), oprelvekin (rhIL-11), filgrastim and tbo-filgrastim (rhG-CSF), sargramostim (rhGM-CSF), metreleptin (rh-leptin) and the rh-erythropoietins, epoetin and darbepoietin alfa. Anakinra, a recombinant receptor antagonist for IL-1, is in the IL-1 family; recombinant interferons alfa-1, alfa-2, beta-1 and gamma-1 make up the interferon family; palifermin (rhKGF) and becaplermin (rhPDGF) are in the PDGF family; and rhBMP-2 and rhBMP-7 represent the TGFβ family. The main physicochemical features, FDA-approved indications, modes of action and side effects of these approved cytokines are presented. Underlying each adverse events profile is their pleiotropism, potency and capacity to release other cytokines producing cytokine 'cocktails'. Side effects, some serious, occur despite cytokines being endogenous proteins, and this therefore demands caution in attempts to introduce individual members into the clinic. This caution is reflected in the relatively small number of cytokines currently approved by regulatory agencies and by the fact that 14 of the FDA-approved preparations carry warnings, with 10 being black box warnings.

Desired alteration of protein affinities: competitive selection of protein variants using yeast signal transduction machinery

Molecules that can control protein-protein interactions (PPIs) have recently drawn attention as new drug pipeline compounds. Here, we report a technique to screen desirable affinity-altered (affinity-enhanced and affinity-attenuated) protein variants. We previously constructed a screening system based on a target protein fused to a mutated G-protein γ subunit (Gγcyto) lacking membrane localization ability. This ability, required for signal transmission, is restored by recruiting Gγcyto into the membrane only when the target protein interacts with an artificially membrane-anchored candidate protein, thereby allowing interacting partners (Gγ recruitment system) to be searched and identified. In the present study, the Gγ recruitment system was altered by integrating the cytosolic expression of a third protein as a competitor to set a desirable affinity threshold. This enabled the reliable selection of both affinity-enhanced and affinity-attenuated protein variants. The presented approach may facilitate the development of therapeutic proteins that allow the control of PPIs.

Structural and dynamic changes associated with beneficial engineered single-amino-acid deletion mutations in enhanced green fluorescent protein

Single-amino-acid deletions are a common part of the natural evolutionary landscape but are rarely sampled during protein engineering owing to limited and prejudiced molecular understanding of mutations that shorten the protein backbone. Single-amino-acid deletion variants of enhanced green fluorescent protein (EGFP) have been identified by directed evolution with the beneficial effect of imparting increased cellular fluorescence. Biophysical characterization revealed that increased functional protein production and not changes to the fluorescence parameters was the mechanism that was likely to be responsible. The structure EGFP(D190Δ) containing a deletion within a loop revealed propagated changes only after the deleted residue. The structure of EGFP(A227Δ) revealed that a `flipping' mechanism was used to adjust for residue deletion at the end of a β-strand, with amino acids C-terminal to the deletion site repositioning to take the place of the deleted amino acid. In both variants new networks of short-range and long-range interactions are generated while maintaining the integrity of the hydrophobic core. Both deletion variants also displayed significant local and long-range changes in dynamics, as evident by changes in B factors compared with EGFP. Rather than being detrimental, deletion mutations can introduce beneficial structural effects through altering core protein properties, folding and dynamics, as well as function.

Glycotherapy: new advances inspire a reemergence of glycans in medicine

The beginning of the 20(th) century marked the dawn of modern medicine with glycan-based therapies at the forefront. However, glycans quickly became overshadowed as DNA- and protein-focused treatments became readily accessible. The recent development of new tools and techniques to study and produce structurally defined carbohydrates has spurred renewed interest in the therapeutic applications of glycans. This review focuses on advances within the past decade that are bringing glycan-based treatments back to the forefront of medicine and the technologies that are driving these efforts. These include the use of glycans themselves as therapeutic molecules as well as engineering protein and cell surface glycans to suit clinical applications. Glycan therapeutics offer a rich and promising frontier for developments in the academic, biopharmaceutical, and medical fields.

Improving the stability and activity of oral therapeutic enzymes-recent advances and perspectives

Exogenous, orally-administered enzymes are currently in clinical use or under development for the treatment of pathologies, such as celiac disease and phenylketonuria. However, the administration of therapeutic enzymes via the oral route remains challenging due to potential inactivation of these fragile macromolecular entities in the harsh environment of the gastrointestinal tract. Enzymes are particularly sensitive because both proteolysis and unfolding can lead to their inactivation. Current efforts to overcome these shortcomings involve the application of gastro-resistant delivery systems and the modification of enzyme structures by polymer conjugation or protein engineering. This perspective manuscript reviews and critically discusses recent progress in the oral delivery of therapeutic enzymes, whose substrate is localized in the gastrointestinal tract.

Tuning the dials of Synthetic Biology

Synthetic Biology is the ‘Engineering of Biology’ – it aims to use a forward-engineering design cycle based on specifications, modelling, analysis, experimental implementation, testing and validation to modify natural or design new, synthetic biology systems so that they behave in a predictable fashion. Motivated by the need for truly plug-and-play synthetic biological components, we present a comprehensive review of ways in which the various parts of a biological system can be modified systematically. In particular, we review the list of ‘dials’ that are available to the designer and discuss how they can be modelled, tuned and implemented. The dials are categorized according to whether they operate at the global, transcriptional, translational or post-translational level and the resolution that they operate at. We end this review with a discussion on the relative advantages and disadvantages of some dials over others.

Directed Evolution: Past, Present and Future

Directed evolution, the laboratory process by which biological entities with desired traits are created through iterative rounds of genetic diversification and library screening or selection, has become one of the most useful and widespread tools in basic and applied biology. From its roots in classical strain engineering and adaptive evolution, modern directed evolution came of age twenty years ago with the demonstration of repeated rounds of PCR-driven random mutagenesis and activity screening to improve protein properties. Since then, numerous techniques have been developed that have enabled the evolution of virtually any protein, pathway, network or entire organism of interest. Here we recount some of the major milestones in the history of directed evolution, highlight the most promising recent developments in the field, and discuss the future challenges and opportunities that lie ahead.

Preparation of enzyme nanoparticles and studying the catalytic activity of the immobilized nanoparticles on polyethylene films

Using high-intensity ultrasound, in situ generated α-amylase nanoparticles (NPs) were immobilized on polyethylene (PE) films. The α-amylase NP-coated PE films have been characterized by E-SEM, FTIR, DLS, XPS and RBS. The PE was reacted with HNO(3) and NPs of the α-amylase were also deposited on the activated PE. The PE impregnated with α-amylase (4 μg per 1mg PE) was used for hydrolyzing soluble potato starch to maltose. The immobilization improved the catalytic activity of α-amylase at all the reaction conditions studied. The kinetic parameters, K(m) (5 and 4 g L(-1) for the regular and activated PE, respectively) and V(max) (5 × 10(-7) mol ml(-1) min(-1), almost the same numbers were obtained for the regular and activated PEs) for the immobilized amylase were found to slightly favor the respective values obtained for the free enzyme (K(m) = 6.6 g L(-1), V(max) = 3.7 × 10(-7) mol ml(-1) min(-1)). The enzyme remained bound to PE even after soaking the PE in a starch solution for 72 h and was still found to be weakly active.

MuteinDB: the mutein database linking substrates, products and enzymatic reactions directly with genetic variants of enzymes

Mutational events as well as the selection of the optimal variant are essential steps in the evolution of living organisms. The same principle is used in laboratory to extend the natural biodiversity to obtain better catalysts for applications in biomanufacturing or for improved biopharmaceuticals. Furthermore, single mutation in genes of drug-metabolizing enzymes can also result in dramatic changes in pharmacokinetics. These changes are a major cause of patient-specific drug responses and are, therefore, the molecular basis for personalized medicine. MuteinDB systematically links laboratory-generated enzyme variants (muteins) and natural isoforms with their biochemical properties including kinetic data of catalyzed reactions. Detailed information about kinetic characteristics of muteins is available in a systematic way and searchable for known mutations and catalyzed reactions as well as their substrates and known products. MuteinDB is broadly applicable to any known protein and their variants and makes mutagenesis and biochemical data searchable and comparable in a simple and easy-to-use manner. For the import of new mutein data, a simple, standardized, spreadsheet-based data format has been defined. To demonstrate the broad applicability of the MuteinDB, first data sets have been incorporated for selected cytochrome P450 enzymes as well as for nitrilases and peroxidases.

Database URL:http://www.MuteinDB.org