Codon optimization for enhanced Escherichia coli expression of human S100A11 and S100A1 proteins

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens

Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.

Strangers in a foreign land: 'Yeastizing' plant enzymes

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

Profiling expression strategies for a type III polyketide synthase in a lysate-based, cell-free system

Some of the most metabolically diverse species of bacteria (e.g., Actinobacteria) have higher GC content in their DNA, differ substantially in codon usage, and have distinct protein folding environments compared to tractable expression hosts like Escherichia coli. Consequentially, expressing biosynthetic gene clusters (BGCs) from these bacteria in E. coli often results in a myriad of unpredictable issues with regard to protein expression and folding, delaying the biochemical characterization of new natural products. Current strategies to achieve soluble, active expression of these enzymes in tractable hosts can be a lengthy trial-and-error process. Cell-free expression (CFE) has emerged as a valuable expression platform as a testbed for rapid prototyping expression parameters. Here, we use a type III polyketide synthase from Streptomyces griseus, RppA, which catalyzes the formation of the red pigment flaviolin, as a reporter to investigate BGC refactoring techniques. We applied a library of constructs with different combinations of promoters and rppA coding sequences to investigate the synergies between promoter and codon usage. Subsequently, we assess the utility of cell-free systems for prototyping these refactoring tactics prior to their implementation in cells. Overall, codon harmonization improves natural product synthesis more than traditional codon optimization across cell-free and cellular environments. More importantly, the choice of coding sequences and promoters impact protein expression synergistically, which should be considered for future efforts to use CFE for high-yield protein expression. The promoter strategy when applied to RppA was not completely correlated with that observed with GFP, indicating that different promoter strategies should be applied for different proteins. In vivo experiments suggest that there is correlation, but not complete alignment between expressing in cell free and in vivo. Refactoring promoters and/or coding sequences via CFE can be a valuable strategy to rapidly screen for catalytically functional production of enzymes from BCGs, which advances CFE as a tool for natural product research.

High-level expression of functional Pfu DNA polymerase recombinant protein by mimicking the enhanced green fluorescence protein gene codon usage

Pfu DNA polymerase is a vital enzyme in PCR-related experiments. However, it is not easy to achieve high-level expression and high purity through one-step purification. This paper illustrates the method to acquire the full-length open reading frame of Pfu DNA polymerase. Without altering its amino acids, we have modified the codon usage, based on that of the enhanced green fluorescence protein (eGFP), and named it rPfu. The synthesized rPfu gene has been subcloned into the pET28a plasmid and expressed in four Escherichia coli strains without the pLysS plasmid. Three strains have expressed a high level of soluble Pfu DNA polymerase. With the aid of Ni-NTA His•Bind^® resin, we could obtain high purity (>95%) soluble recombinant protein. Compared with the commercial, proofreading DNA polymerase, rPfu's bioactivity was 12,987 U/mg; that is, 88,311 U of rPfu could be obtained from 50 mL cultured E. coli. The purified rPfu was able to amplify the length of DNA fragments at least 5.5 kb. The method of increasing soluble protein's yield using the eGFP codon usage may introduce a new possibility to the expression of other soluble recombinant proteins.

Mechanism of Zn2+ and Ca2+ Binding to Human S100A1

S100A1 is a member of the S100 family of small ubiquitous Ca²⁺-binding proteins, which participates in the regulation of cell differentiation, motility, and survival. It exists as homo- or heterodimers. S100A1 has also been shown to bind Zn²⁺, but the molecular mechanisms of this binding are not yet known. In this work, using ESI-MS and ITC, we demonstrate that S100A1 can coordinate 4 zinc ions per monomer, with two high affinity (K_D~4 and 770 nm) and two low affinity sites. Using competitive binding experiments between Ca²⁺ and Zn²⁺ and QM/MM molecular modeling we conclude that Zn²⁺ high affinity sites are located in the EF-hand motifs of S100A1. In addition, two lower affinity sites can bind Zn²⁺ even when the EF-hands are saturated by Ca²⁺, resulting in a 2Ca²⁺:S100A1:2Zn²⁺ conformer. Finally, we show that, in contrast to calcium, an excess of Zn²⁺ produces a destabilizing effect on S100A1 structure and leads to its aggregation. We also determined a higher affinity to Ca²⁺ (K_D~0.16 and 24 μm) than was previously reported for S100A1, which would allow this protein to function as a Ca²⁺/Zn²⁺-sensor both inside and outside cells, participating in diverse signaling pathways under normal and pathological conditions.

Codon optimization with deep learning to enhance protein expression

Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning. First, we introduce the concept of codon boxes, via which DNA sequences can be recoded into codon box sequences while ignoring the order of bases. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. The codon optimization models for Escherichia Coli were trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. In addition to the comparison of the codon adaptation index, protein expression experiments for plasmodium falciparum candidate vaccine and polymerase acidic protein were implemented for comparison with the original sequences and the optimized sequences from Genewiz and ThermoFisher. The results show that our method for enhancing protein expression is efficient and competitive.

"CodonWizard" - An intuitive software tool with graphical user interface for customizable codon optimization in protein expression efforts

Optimization of coding sequences to maximize protein expression yield is often outsourced to external service providers during commercial gene synthesis and thus unfortunately remains a black box for many researchers. The presented software program "CodonWizard" offers scientists a powerful but easy-to-use tool for customizable codon optimization: The intuitive graphical user interface empowers even scientists inexperienced in the art to straightforward design, modify, test and save complex codon optimization strategies and to publicly share successful otimization strategies among the scientific community. "Codon Wizard" provides highly flexible features for sequence analysis and completely customizable modification/optimization of codon usage of any given input sequence data (DNA/RNA/peptide) using freely combinable algorithms, allowing for implementation of contemporary, well-established optimization strategies as well as novel, proprietary ones alike. Contrary to comparable tools, "Codon Wizard" thus finally opens up ways for an empirical approach to codon optimization and may also >be used completely offline to protect resulting intellectual property. As a benchmark, the reliability, intuitiveness and utility of the application could be demonstrated by increasing the yield of recombinant TEV-protease expressed in E. coli by several orders of magnitude after codon optimization using "CodonWizard" - Permanently available for download on the web at http://schwalbe.org.chemie.uni-frankfurt.de/node/3324.

A subset of calcium-binding S100 proteins show preferential heterodimerization

The assembly of proteins into dimers and oligomers is a necessary step for the proper function of transcription factors, muscle proteins, and proteases. In uncontrolled states, oligomerization can also contribute to illnesses such as Alzheimer's disease. The S100 protein family is a group of dimeric proteins that have important roles in enzyme regulation, cell membrane repair, and cell growth. Most S100 proteins have been examined in their homodimeric state, yet some of these important proteins are found in similar tissues implying that heterodimeric molecules can also be formed from the combination of two different S100 members. In this work, we have established co-expression methods in order to identify and quantify the distribution of homo- and heterodimers for four specific pairs of S100 proteins in their calcium-free states. The split GFP trap methodology was used in combination with other GFP variants to simultaneously quantify homo- and heterodimeric S100 proteins in vitro and in living cells. For the specific S100 proteins examined, NMR, mass spectrometry, and GFP trap experiments consistently show that S100A1:S100B, S100A1:S100P, and S100A11:S100B heterodimers are the predominant species formed compared to their corresponding homodimers. We expect the tools developed here will help establish the roles of S100 heterodimeric proteins and identify how heterodimerization might alter the specificity for S100 protein action in cells.

Molecular basis for the interaction between stress-inducible phosphoprotein 1 (STIP1) and S100A1

Stress-inducible phosphoprotein 1 (STIP1) is a cellular co-chaperone, which regulates heat-shock protein 70 (Hsp70) and Hsp90 activity during client protein folding. Members of the S100 family of dimeric calcium-binding proteins have been found to inhibit Hsp association with STIP1 through binding of STIP1 tetratricopeptide repeat (TPR) domains, possibly regulating the chaperone cycle. Here, we investigated the molecular basis of S100A1 binding to STIP1. We show that three S100A1 dimers associate with one molecule of STIP1 in a calcium-dependent manner. Isothermal titration calorimetry revealed that individual STIP1 TPR domains, TPR1, TPR2A and TPR2B, bind a single S100A1 dimer with significantly different affinities and that the TPR2B domain possesses the highest affinity for S100A1. S100A1 bound each TPR domain through a common binding interface composed of α-helices III and IV of each S100A1 subunit, which is only accessible following a large conformational change in S100A1 upon calcium binding. The TPR2B-binding site for S100A1 was predominately mapped to the C-terminal α-helix of TPR2B, where it is inserted into the hydrophobic cleft of an S100A1 dimer, suggesting a novel binding mechanism. Our data present the structural basis behind STIP1 and S100A1 complex formation, and provide novel insights into TPR module-containing proteins and S100 family member complexes.

Functionally active rat S100A4 from a polymerase chain reaction-synthesized gene expressed in soluble form in Escherichia coli

S100A4 protein is associated with Ca²⁺-dependent regulation of intracellular activities and is significant in the invasion, growth and metastasis of cancer. In order to express rat S100A4 functionally and identify its biological activity following purification, an S100A4 gene fragment was optimized and fully synthesized via overlapping polymerase chain reaction. The gene was inserted into the prokaryotic expression vector, pBV220, with phage λ P_RP_L promoters following confirmation by DNA sequencing. The pBV220-S100A4 plasmid was constructed and transformed into Escherichia coli DH5α. Following temperature induction, rat S100A4 was overexpressed and the protein was observed to be located in the supernatant of the lysates, which was ~30–40% of the total protein within the host. The protein was isolated and purified by metal-chelate affinity chromatography. High purity protein (>98% purity) was obtained and in vitro western blot analysis identified that the recombinant S100A4 was able to bind to the antibody against wild-type S100A4. The bioactivity of the recombinant protein was detected via Transwell migration and invasion assays. The polyclonal antibody of rat S100A4 protein was prepared for rabbit immunization and exhibited similar efficacies when compared with commercial S100A4. Therefore, rat S100A4 was functionally expressed in E. coli; thus, the production of active recombinant S100A4 protein in E. coli may further aid with the investigation and application of S100A4.

Expression of codon optimized genes in microbial systems: current industrial applications and perspectives

The efficient production of functional proteins in heterologous hosts is one of the major bases of modern biotechnology. Unfortunately, many genes are difficult to express outside their original context. Due to their apparent “silent” nature, synonymous codon substitutions have long been thought to be trivial. In recent years, this dogma has been refuted by evidence that codon replacement can have a significant impact on gene expression levels and protein folding. In the past decade, considerable advances in the speed and cost of gene synthesis have facilitated the complete redesign of entire gene sequences, dramatically improving the likelihood of high protein expression. This technology significantly impacts the economic feasibility of microbial-based biotechnological processes by, for example, increasing the volumetric productivities of recombinant proteins or facilitating the redesign of novel biosynthetic routes for the production of metabolites. This review discusses the current applications of this technology, particularly those regarding the production of small molecules and industrially relevant recombinant enzymes. Suggestions for future research and potential uses are provided as well.

Explanatory chapter: troubleshooting recombinant protein expression: general

One of the most daunting problems for biochemists is the expression of recombinant proteins. Often, the host organism differs from the organism from which the gene coding for the protein of interest was derived. This article provides guidelines to determine whether or not protein expression is a problem, describes possible reasons for low protein expression, and covers several possible solutions. A protocol for measuring protein expression during E. coli cell growth and after induction is given. The reader should note that low protein expression is a complex problem that often stems from a variety of factors. Combinations of the solutions presented in this article may be required to solve a problem of protein expression. A brief overview of host cell expression systems is given, but the article primarily focuses on expression in E. coli as this is the most commonly used host organism. Some of the methods discussed here, however, may be applied to other expression systems.

Codon preference optimization increases prokaryotic cystatin C expression

Gene expression is closely related to optimal vector-host system pairing in many prokaryotes. Redesign of the human cystatin C (cysC) gene using the preferred codons of the prokaryotic system may significantly increase cysC expression in Escherichia coli (E. coli). Specifically, cysC expression may be increased by removing unstable sequences and optimizing GC content. According to E. coli expression system codon preferences, the gene sequence was optimized while the amino acid sequence was maintained. The codon-optimized cysC (co-cysC) and wild-type cysC (wt-cysC) were expressed by cloning the genes into a pET-30a plasmid, thus transforming the recombinant plasmid into E. coli BL21. Before and after the optimization process, the prokaryotic expression vector and host bacteria were examined for protein expression and biological activation of CysC. The recombinant proteins in the lysate of the transformed bacteria were purified using Ni(2+)-NTA resin. Recombinant protein expression increased from 10% to 46% based on total protein expression after codon optimization. Recombinant CysC purity was above 95%. The significant increase in cysC expression in E. coli expression produced by codon optimization techniques may be applicable to commercial production systems.

An efficient expression and purification strategy for the production of S100 proteins in Escherichia coli

S100 proteins belong to a family of small, acidic, EF-hand Ca ( 2+) -binding proteins and have been found to exert both intracellular and extracellular functions in regulation of Ca ( 2+) homeostasis, cytoskeletal dynamics, cell cycle, motility and differentiation. As a result, they have been widely investigated for their association with diseases, such as, neurological diseases, cardiomyopathy, neoplasias and inflammatory diseases. To facilitate further studies of S100 proteins, we reported a simple and efficient method for the expression and purification of human S100A4 and S100A11 proteins in Escherichia coli. Since S100 proteins share many common physical and chemical characteristics, we expect that this approach can be extended to the production of most S100 proteins.

Comparison of two codon optimization strategies to enhance recombinant protein production in Escherichia coli

BACKGROUND

Variations in codon usage between species are one of the major causes affecting recombinant protein expression levels, with a significant impact on the economy of industrial enzyme production processes. The use of codon-optimized genes may overcome this problem. However, designing a gene for optimal expression requires choosing from a vast number of possible DNA sequences and different codon optimization methods have been used in the past decade. Here, a comparative study of the two most common methods is presented using calf prochymosin as a model.

RESULTS

Seven sequences encoding calf prochymosin have been designed, two using the "one amino acid-one codon" method and five using a "codon randomization" strategy. When expressed in Escherichia coli, the variants optimized by the codon randomization approach produced significantly more proteins than the native sequence including one gene that produced an increase of 70% in the amount of prochymosin accumulated. On the other hand, no significant improvement in protein expression was observed for the variants designed with the one amino acid-one codon method. The use of codon-optimized sequences did not affect the quality of the recovered inclusion bodies.

CONCLUSIONS

The results obtained in this study indicate that the codon randomization method is a superior strategy for codon optimization. A significant improvement in protein expression was obtained for the largely established process of chymosin production, showing the power of this strategy to reduce production costs of industrial enzymes in microbial hosts.