Protein engineering: a new frontier for biological therapeutics

Model acetylcholinesterase-Fc fusion glycoprotein biotechnology system for the manufacture of an organophosphorus toxicant bioscavenging countermeasure

Organophosphate (OP) toxicants remain an active threat to public health and to warfighters in the military. Current countermeasures require near immediate administration following OP exposure and are reported to have controversial efficacies. Acetylcholinesterase (AChE) fused to the human immunoglobulin 1 (IgG1) Fc domain (AChE-Fc) is a potential bioscavenger for OP toxicants, but a reproducible AChE-Fc biomanufacturing strategy remains elusive. This report is the first to establish a comprehensive laboratory-scale bioprocessing strategy that can reproducibly produce AChE-Fc and AChE(W86A)-Fc which is a mutated AChE protein with reduced enzymatic activity. Characterization studies revealed that AChE-Fc and AChE(W86A)-Fc are N-glycosylated dimeric fusion glycoproteins but only AChE-Fc had the capability to bind to paraoxon (a model OP). This AChE-Fc fusion glycoprotein bioprocessing strategy can be leveraged during industrial biomanufacturing development, while the research-grade AChE-Fc proteins can be used to determine the potential clinical relevance of the countermeasure against OP toxicants.

Integrating Computational Design and Experimental Approaches for Next-Generation Biologics

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

A Novel Non-Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Amino-acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

New advances in protein engineering for industrial applications: Key takeaways

Recent advancements in protein/enzyme engineering have enabled the production of a diverse array of high-value compounds in microbial systems with the potential for industrial applications. The goal of this review is to articulate some of the most recent protein engineering advances in bacteria, yeast, and other microbial systems to produce valuable substances. These high-value substances include α-farnesene, vitamin B12, fumaric acid, linalool, glucaric acid, carminic acid, mycosporine-like amino acids, patchoulol, orcinol glucoside, d-lactic acid, keratinase, α-glucanotransferases, β-glucosidase, seleno-methylselenocysteine, fatty acids, high-efficiency β-glucosidase enzymes, cellulase, β-carotene, physcion, and glucoamylase. Additionally, recent advances in enzyme engineering for enhancing thermostability will be discussed. These findings have the potential to revolutionize various industries, including biotechnology, food, pharmaceuticals, and biofuels.

Metagenomic exploration of cold-active enzymes for detergent applications: Characterization of a novel, cold-active and alkali-stable GH8 endoglucanase from ikaite columns in SW Greenland

Microbial communities from extreme environments are largely understudied, but are essential as producers of metabolites, including enzymes, for industrial processes. As cultivation of most microorganisms remains a challenge, culture-independent approaches for enzyme discovery in the form of metagenomics to analyse the genetic potential of a community are rapidly becoming the way forward. This study focused on analysing a metagenome from the cold and alkaline ikaite columns in Greenland, identifying 282 open reading frames (ORFs) that encoded putative carbohydrate-modifying enzymes with potential applications in, for example detergents and other processes where activity at low temperature and high pH is desired. Seventeen selected ORFs, representing eight enzyme families were synthesized and expressed in two host organisms, Escherichia coli and Aliivibrio wodanis. Aliivibrio wodanis demonstrated expression of a more diverse range of enzyme classes compared to E. coli, emphasizing the importance of alternative expression systems for enzymes from extremophilic microorganisms. To demonstrate the validity of the screening strategy, we chose a recombinantly expressed cellulolytic enzyme from the metagenome for further characterization. The enzyme, Cel240, exhibited close to 40% of its relative activity at low temperatures (4°C) and demonstrated endoglucanase characteristics, with a preference for cellulose substrates. Despite low sequence similarity with known enzymes, computational analysis and structural modelling confirmed its cellulase-family affiliation. Cel240 displayed activity at low temperatures and good stability at 25°C, activity at alkaline pH and increased activity in the presence of CaCl2, making it a promising candidate for detergent and washing industry applications.

Protein structure generation via folding diffusion

The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a diffusion-based generative model that generates protein backbone structures via a procedure inspired by the natural folding process. We describe a protein backbone structure as a sequence of angles capturing the relative orientation of the constituent backbone atoms, and generate structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins natively twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for more complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release an open-source codebase and trained models for protein structure diffusion.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Targeted Mutation of a Non-catalytic Gating Residue Increases the Rate of Pseudomonas aeruginosa d-Arginine Dehydrogenase Catalytic Turnover

Commercial food and l-amino acid industries rely on bioengineered d-amino acid oxidizing enzymes to detect and remove d-amino acid contaminants. However, the bioengineering of enzymes to generate faster biological catalysts has proven difficult as a result of the failure to target specific kinetic steps that limit enzyme turnover, kcat, and the poor understanding of loop dynamics critical for catalysis. Pseudomonas aeruginosa d-arginine dehydrogenase (PaDADH) oxidizes most d-amino acids and is a good candidate for application in the l-amino acid and food industries. The side chain of the loop L2 E246 residue located at the entrance of the PaDADH active site pocket potentially favors the closed active site conformation and secures the substrate upon binding. This study used site-directed mutagenesis, steady-state, and rapid reaction kinetics to generate the glutamine, glycine, and leucine variants and investigate whether increasing the rate of product release could translate to an increased enzyme turnover rate. Upon E246 mutation to glycine, there was an increased rate of d-arginine turnover kcat from 122 to 500 s-1. Likewise, the kcat values increased 2-fold for the glutamine or leucine variants. Thus, we have engineered a faster biocatalyst for industrial applications by selectively increasing the rate of the PaDADH product release.

Surfaces: a software to quantify and visualize interactions within and between proteins and ligands

SUMMARY

Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here, we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent or better correlations with experimental data as computationally expensive methods based on molecular dynamics.

AVAILABILITY AND IMPLEMENTATION

All scripts are available at https://github.com/NRGLab/Surfaces. Surface's documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html.

The Cost-Effectiveness of Anti-IL17 Biologic Therapies for Moderate-to-Severe Plaque Psoriasis Treatment in Italy and Germany: A Sequential Treatment Analysis

Objective

The objective of this study was to optimise the cost-effectiveness of different anti-IL17 treatment sequences used in the treatment of moderate-to-severe plaque psoriasis in Italy and Germany over a five-year time horizon.

Methods

We adjusted a previously published treatment sequence model for biologic drugs used in psoriasis treatment to an Italian and German setting, respectively. The model included all anti-IL17 biologics currently available in the treatment of moderate-to-severe plaque psoriasis in the markets of scope (secukinumab, ixekizumab, brodalumab and bimekizumab). Real-world discontinuation rates were used to model switches between the four anti-IL17 biologics included in the study. The treatment costs were based on label dosing recommendations for each drug, including induction and maintenance therapy, and the manufacturer prices of each drug in Italy and Germany, respectively. We used long-term Psoriasis Area and Severity Index 100 (PASI100) measures to inform the model on the efficacy for each treatment. The cost-effectiveness in the analysis was evaluated based on the cost per PASI100-responder.

Results

We found that the most cost-effective treatment sequence was achieved by using brodalumab as first-line treatment, bimekizumab as second-line treatment, ixekizumab as third-line treatment and secukinumab as fourth-line treatment in both Italy and Germany, which resulted in a total cost per responder of €128,200 and €138,212, respectively, over a five-year period. Several scenario analyses were also conducted and ensured that the results were robust to changes in key input parameters.

Conclusion

Our study showed that using brodalumab as a first-line therapy to treat moderate-to-severe psoriasis in both Italy and Germany leads to the most cost-effective treatment sequence, when compared to all possible combinations of anti-IL17s over a five-year time horizon. In addition, we found that treatment discontinuation and switching are important factors when assessing the cost-effectiveness of biologic therapies.

Fundamental concepts of protein therapeutics and spacing in oncology: an updated comprehensive review

Current treatment regimens in cancer cases cause significant side effects and cannot effectively eradicate the advanced disease. Hence, much effort has been expended over the past years to understand how cancer grows and responds to therapies. Meanwhile, proteins as a type of biopolymers have been under commercial development for over three decades and have been proven to improve the healthcare system as effective medicines for treating many types of progressive disease, such as cancer. Following approving the first recombinant protein therapeutics by FDA (Humulin), there have been a revolution for drawing attention toward protein-based therapeutics (PTs). Since then, the ability to tailor proteins with ideal pharmacokinetics has provided the pharmaceutical industry with an important noble path to discuss the clinical potential of proteins in oncology research. Unlike traditional chemotherapy molecules, PTs actively target cancerous cells by binding to their surface receptors and the other biomarkers particularly associated with tumorous or healthy tissue. This review analyzes the potential and limitations of protein therapeutics (PTs) in the treatment of cancer as well as highlighting the evolving strategies by addressing all possible factors, including pharmacology profile and targeted therapy approaches. This review provides a comprehensive overview of the current state of PTs in oncology, including their pharmacology profile, targeted therapy approaches, and prospects. The reviewed data show that several current and future challenges remain to make PTs a promising and effective anticancer drug, such as safety, immunogenicity, protein stability/degradation, and protein-adjuvant interactions.

Calibration-free PAT: Locating selective crystallization or precipitation sweet spot in screenings with multi-way PARAFAC models

When developping selective crystallization or precipitation processes, biopharmaceutical modalities require empirical screenings and analytics tailored to the specific needs of the target molecule. The multi-way chemometric approach called parallel factor analysis (PARAFAC) coupled with ultraviolet visible light (UV/Vis) spectroscopy is able to predict specific concentrations and spectra from highly structured data sets without the need for calibration samples and reference analytics. These calculated models can provide exploratory information on pure species spectra and concentrations in all analyzed samples by representing one model component with one species. In this work, protein mixtures, monoclonal antibodies, and virus-like particles in chemically defined and complex solutions were investigated in three high-throughput crystallization or precipitation screenings with the aim to construct one PARAFAC model per case. Spectroscopic data sets of samples after the selective crystallization or precipitation, washing, and redissolution were recorded and arranged into a four-dimensional data set per case study. Different reference analytics and pure species spectra served as validation. Appropriate spectral preprocessing parameters were found for all case studies allowing even the application of this approach to the third case study in which quantitative concentration analytics are missing. Regardless of the modality or the number of species present in complex solutions, all models were able to estimate the specific concentration and find the optimal process condition regarding yield and product purity. It was shown that in complex solutions, species demonstrating similar phase behavior can be clustered as one component and described in the model. PARAFAC as a calibration-free approach coupled with UV/Vis spectroscopy provides a fast overview of species present in complex solution and of their concentration during selective crystallization or precipitation, washing, and redissolution.

A Biopharmaceutical Perspective on Higher-Order Structure and Thermal Stability of mRNA Vaccines

Preservation of the integrity of macromolecular higher-order structure is a tenet central to achieving biologic drug and vaccine product stability toward manufacturing, distribution, storage, handling, and administration. Given that mRNA lipid nanoparticles (mRNA-LNPs) are held together by an intricate ensemble of weak forces, there are some intriguing parallels to biologic drugs, at least at first glance. However, mRNA vaccines are not without unique formulation and stabilization challenges derived from the instability of unmodified mRNA and its limited history as a drug or vaccine. Since certain learning gained from biologic drug development may be applicable for the improvement of mRNA vaccines, we present a perspective on parallels and contrasts between the emerging role of higher-order structure pertaining to mRNA-LNPs compared to pharmaceutical proteins. In a recent publication, the location of mRNA encapsulated within lipid nanoparticles was identified, revealing new insights into the LNP structure, nanoheterogeneity, and microenvironment of the encapsulated mRNA molecules [Brader et al. Biophys. J. 2021, 120, 2766]. We extend those findings by considering the effect of encapsulation on mRNA thermal unfolding with the observation that encapsulation in LNPs increases mRNA unfolding temperatures.

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations

Granulocyte-colony stimulating factor (GCSF) is a widely used therapeutic protein to treat neutropenia. GCSF has an increased propensity to aggregate if the pH is increased above 5.0. Although GCSF is very well experimentally characterized, the exact pH-dependent aggregation mechanism of GCSF is still under debate. This study aimed to model the complex pH-dependent aggregation behavior of GCSF using state-of-the-art simulation techniques. The conformational stability of GCSF was investigated by performing metadynamics simulations, while the protein-protein interactions were investigated using coarse-grained (CG) simulations of multiple GCSF monomers. The CG simulations were directly compared with small-angle X-ray (SAXS) data. The metadynamics simulations demonstrated that the orientations of Trp residues in GCSF are dependent on pH. The conformational change of Trp residues is due to the loss of Trp-His interactions at the physiological pH, which in turn may increase protein flexibility. The helical structure of GCSF was not affected by the pH conditions of the simulations. Our CG simulations indicate that at pH 4.0, the colloidal stability may be more important than the conformational stability of GCSF. The electrostatic potential surface and CG simulations suggested that the basic residues are mainly responsible for colloidal stability as deprotonation of these residues causes a reduction of the highly positively charged electrostatic barrier close to the aggregation-prone long loop regions.

Therapeutic enzyme engineering using a generative neural network

Enhancing the potency of mRNA therapeutics is an important objective for treating rare diseases, since it may enable lower and less-frequent dosing. Enzyme engineering can increase potency of mRNA therapeutics by improving the expression, half-life, and catalytic efficiency of the mRNA-encoded enzymes. However, sequence space is incomprehensibly vast, and methods to map sequence to function (computationally or experimentally) are inaccurate or time-/labor-intensive. Here, we present a novel, broadly applicable engineering method that combines deep latent variable modelling of sequence co-evolution with automated protein library design and construction to rapidly identify metabolic enzyme variants that are both more thermally stable and more catalytically active. We apply this approach to improve the potency of ornithine transcarbamylase (OTC), a urea cycle enzyme for which loss of catalytic activity causes a rare but serious metabolic disease.

Effect of backbone circularization on colloidal stability: Compaction of unfolded structures improves aggregation resistance of granulocyte colony-stimulating factor

Aggregation of protein therapeutics can lead to immunogenicity and loss of function in vivo. Its effective prevention requires an understanding of the conformational and colloidal stability of protein and the improvement of both. Granulocyte colony-stimulating factor (G-CSF), which is one of the most widely used protein therapeutics, was previously shown to be conformationally stabilized by connecting its N- and C-termini with amide bonds (backbone circularization). In this study, we investigated whether circularization affects the colloidal stability of proteins. Colloidal stability was indirectly assessed by analyzing the aggregation behavior of G-CSF variants using analytical ultracentrifugation (AUC) and small-angle X-ray scattering (SAXS). Consequently, we found that the unfolded structure of circularized G-CSF was more compact than non-circularized G-CSF, and that backbone circularization improved its aggregation resistance against chemical denaturation by guanidine hydrochloride (GdnHCl). The improved aggregation resistance suggests that the expansion tolerance of circularized G-CSF in the unfolded state increased its colloidal stability. Thus, backbone circularization is an excellent method for enhancing the colloidal and the conformational stability of protein with minimal sequence changes. It is therefore expected to be effective in extending the storage stability of protein therapeutics, enhancing their biological stability.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Biomolecular Systems Engineering: Unlocking the Potential of Engineered Allostery via the Lactose Repressor Topology

Allosteric function is a critical component of many of the parts used to construct gene networks throughout synthetic biology. In this review, we discuss an emerging field of research and education, biomolecular systems engineering, that expands on the synthetic biology edifice-integrating workflows and strategies from protein engineering, chemical engineering, electrical engineering, and computer science principles. We focus on the role of engineered allosteric communication as it relates to transcriptional gene regulators-i.e., transcription factors and corresponding unit operations. In this review, we (a) explore allosteric communication in the lactose repressor LacI topology, (b) demonstrate how to leverage this understanding of allostery in the LacI system to engineer non-natural BUFFER and NOT logical operations, (c) illustrate how engineering workflows can be used to confer alternate allosteric functions in disparate systems that share the LacI topology, and (d) demonstrate how fundamental unit operations can be directed to form combinational logical operations.

Development of a novel prostate apoptosis response-4 (Par-4) protein entity with an extended duration of action for therapeutic treatment of cancer

Prostate apoptosis response-4 (Par-4) is a tumor suppressor which protects against neoplastic transformation. Remarkably, Par-4 is capable of inducing apoptosis selectively in cancer cells without affecting the normal cells. In this study, we found that recombinant Par-4 protein had limited serum persistence in mice that may diminish its anti-tumor activity in vivo. To improve the in vivo performance of the short-lived Par-4 protein, we aimed to develop a novel, long-lasting form of Par-4 with extended sequence, denoted as Par-4Ex, without affecting the desirable molecular function of the natural Par-4. We demonstrate that the Par-4Ex protein entity, produced by using the Escherichia coli expression system suitable for large-scale production, fully retains the desirable pro-apoptotic activity of Par-4 protein, but with ~7-fold improved biological half-life. Further in vivo tests confirmed that, due to the prolonged biological half-life, the Par-4Ex protein is indeed more potent in suppressing metastatic tumor growth in mice.

A comprehensive practical laboratory course on protein engineering: Evolution of a glycine oxidase variant active on the herbicide glyphosate

Protein engineering represents a modern approach to generate novel proteins for the different fields of biotechnology. Here, we report about an 8-day laboratory activity in which students generate enzyme variants to degrade the herbicide glyphosate. The students conduct a true research experiment in an important field (bioremediation by novel, engineered enzymes) and are introduced to widely used techniques in molecular biology and protein biochemistry laboratories. Based on a docking analysis of glycine (the original substrate) and of glyphosate into the active site of glycine oxidase, residues putatively involved in substrate selectivity are identified that will become the target of site-saturation mutagenesis. Each group of students focuses on the library generated at one position and selects the most active variant based on colorimetric screening. Following protein overexpression in Escherichia coli, the selected glycine oxidase variants are purified and their kinetic properties on glycine and glyphosate assessed. The best variant identified by the whole class is then used for detecting the herbicide in water. With the help of the professor, students can improve technical skills, ability to evaluate results, team work activity, and critical thinking. © 2019 International Union of Biochemistry and Molecular Biology, 47(6):689-699, 2019.

Stabilization of backbone-circularized protein is attained by synergistic gains in enthalpy of folded structure and entropy of unfolded structure

Backbone circularization is an effective technique for protein stabilization. Here, we investigated the effect of a connector, an engineered segment that connects two protein termini, on the conformational stability of previously designed circularized variants of granulocyte colony-stimulating factor (G-CSF). Heat tolerance and chemical denaturation analyses revealed that aggregation resistance and thermodynamic stability of the circularized variants were superior to those of linear G-CSF. Crystal structure and molecular dynamics (MD) simulation of the most thermodynamically stable variant (C166) revealed a high number of intramolecular hydrogen bonds in both the connector region and Helix D adjacent to the connector region in the folded structure. MD simulations and theoretical calculations involving different force fields indicated a reduction in the main chain entropy of C166 in the unfolded state and increase in the intramolecular hydrogen bond energy of C166 in the folded structure. Although backbone circularization is usually considered to alter chain entropy of the unfolded state, the data indicated that it could also improve the conformational enthalpy of the folded state. Further structural examination of the connector region confirmed that protein design based on a statistical analysis of local structures is an effective approach for predicting an optimum connector length to improve the conformational stability of backbone-circularized proteins. Protein design using backbone circularization with an optimum connector length will be useful for the development of effective and safe protein therapeutics. DATABASE: Structural data are available in Protein Data Bank under the accession number 5ZO6.

Structure-based reconstruction of a Mycobacterium hypothetical protein into an active Δ5-3-ketosteroid isomerase

Protein engineering based on structure homology holds the potential to engineer steroid-transforming enzymes on demand. Based on the genome sequencing analysis of industrial Mycobacterium strain HGMS2 to produce 4-androstene-3,17-dione (4-AD), three hypothetical proteins were predicted as putative Δ⁵-3-ketosteroid isomerases (KSIs) to catalyze an intramolecular proton transfer involving the transformation of 5-androstene-3,17-dione (5-AD) into 4-AD, which were defined as mKSI228, mKSI291 and mKSI753. Activity assays indicated that mKSI228 and mKSI291 exhibited weak activity, as low as 0.7% and 1.5%, respectively, of a well-studied and highly active KSI from Pseudomonas putida KSI (pKSI), while mKSI753 had no activity similar to Mycobacterium tuberculosis KSI (mtKSI). Although the 3D structures of the putative mKSIs were homologous to pKSI, their amino acid sequences were significantly different from those of pKSI and tKSI. Thus, by use of these two KSIs as homology models, we were able to convert the low-active mKSI291 into a high-active active KSI by site-directed mutagenesis. On the other hand, an X-ray crystallographic structure of mKSI291 identified a water molecule in its active site. This unique water molecule might function as a bridge to connect Ser-OH, Tyr57-OH and C3O of the intermediate form a hydrogen-bonding network that was responsible for its weak activity, compared with that of mtKSI. Our results not only demonstrated the use of a protein engineering approach to understanding KSI catalytic mechanism, but also provided an example for engineering the catalytic active sites and gaining a functional enzyme based on homologous structures.

Engineering Botulinum Toxins to Improve and Expand Targeting and SNARE Cleavage Activity

Botulinum neurotoxins (BoNTs) are highly successful protein therapeutics. Over 40 naturally occurring BoNTs have been described thus far and, of those, only 2 are commercially available for clinical use. Different members of the BoNT family present different biological properties but share a similar multi-domain structure at the molecular level. In nature, BoNTs are encoded by DNA in producing clostridial bacteria and, as such, are amenable to recombinant production through insertion of the coding DNA into other bacterial species. This, in turn, creates possibilities for protein engineering. Here, we review the production of BoNTs by the natural host and also recombinant production approaches utilised in the field. Applications of recombinant BoNT-production include the generation of BoNT-derived domain fragments, the creation of novel BoNTs with improved performance and enhanced therapeutic potential, as well as the advancement of BoNT vaccines. In this article, we discuss site directed mutagenesis, used to affect the biological properties of BoNTs, including approaches to alter their binding to neurons and to alter the specificity and kinetics of substrate cleavage. We also discuss the target secretion inhibitor (TSI) platform, in which the neuronal binding domain of BoNTs is substituted with an alternative cellular ligand to re-target the toxins to non-neuronal systems. Understanding and harnessing the potential of the biological diversity of natural BoNTs, together with the ability to engineer novel mutations and further changes to the protein structure, will provide the basis for increasing the scope of future BoNT-based therapeutics.

Transient AID expression for in situ mutagenesis with improved cellular fitness

Activation induced cytidine deaminase (AID) in germinal center B cells introduces somatic DNA mutations in transcribed immunoglobulin genes to increase antibody diversity. Ectopic expression of AID coupled with selection has been successfully employed to develop proteins with desirable properties. However, this process is laborious and time consuming because many rounds of selection are typically required to isolate the target proteins. AID expression can also adversely affect cell viability due to off target mutagenesis. Here we compared stable and transient expression of AID mutants with different catalytic activities to determine conditions for maximum accumulation of mutations with minimal toxicity. We find that transient (3–5 days) expression of an AID upmutant in the presence of selection pressure could induce a high rate of mutagenesis in reporter genes without affecting cells growth and expansion. Our findings may help improve protein evolution by ectopic expression of AID and other enzymes that can induce DNA mutations.

Recent advances in automated protein design and its future challenges

INTRODUCTION

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

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.

Recent advances in (therapeutic protein) drug development

Therapeutic protein drugs are an important class of medicines serving patients most in need of novel therapies. Recently approved recombinant protein therapeutics have been developed to treat a wide variety of clinical indications, including cancers, autoimmunity/inflammation, exposure to infectious agents, and genetic disorders. The latest advances in protein-engineering technologies have allowed drug developers and manufacturers to fine-tune and exploit desirable functional characteristics of proteins of interest while maintaining (and in some cases enhancing) product safety or efficacy or both. In this review, we highlight the emerging trends and approaches in protein drug development by using examples of therapeutic proteins approved by the U.S. Food and Drug Administration over the previous five years (2011–2016, namely January 1, 2011, through August 31, 2016).