Codon optimization enhances protein expression of human peptide deformylase in E. coli

Strategies to overcome the challenges of low or no expression of heterologous proteins in Escherichia coli

Protein expression is a critical process in diverse biological systems. For Escherichia coli, a widely employed microbial host in industrial catalysis and healthcare, researchers often face significant challenges in constructing recombinant expression systems. To maximize the potential of E. coli expression systems, it is essential to address problems regarding the low or absent production of certain target proteins. This article presents viable solutions to the main factors posing challenges to heterologous protein expression in E. coli, which includes protein toxicity, the intrinsic influence of gene sequences, and mRNA structure. These strategies include specialized approaches for managing toxic protein expression, addressing issues related to mRNA structure and codon bias, advanced codon optimization methodologies that consider multiple factors, and emerging optimization techniques facilitated by big data and machine learning.

gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (S _ij values) for codon-tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated S _ij weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the S _ij weights, which is not ideal for obtaining the best set of S _ij weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of S _ij weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.

A new inhibitor of human peptide deformylase suppresses cell proliferation and induces cell apoptosis and autophagy in cancers

Human peptide deformylase (hsPDF) has been found overexpressed in many cancer cells and its inhibitors exhibit antitumor activity. Studies were performed to validate that hsPDF is a good antitumor target. The inhibitory effect of PDF64 on hsPDF enzymatic activity was measured and confirmed by computation analysis. Antiproliferation activity was determined and in-vivo antitumor activity were analyzed in HCT116 and HL60 nude mice xenografts. Mitochondrial membrane potential (MMP), cell apoptosis, and autophagic cell death were analyzed by flow cytometry. ATP level was quantified using an ATP assay kit. Protein expression and kinase phosphorylation were determined by western blotting. A new hsPDF inhibitor PDF64 was identified. It showed evident antiproliferation activity in 10 cancer cells and significantly suppressed tumor growth in HCT116 and HL60 xenografts. It induced an obvious decrease in MMP and caused apparent cell apoptosis and autophagy in HCT116 and Jurkat cells. PDF64 treatment also led to an evident decrease in cellular ATP levels in these cells. Moreover, PDF64 downregulated c-Myc expression and had some effects on extracellular regulated protein kinases (ERK) and protein kinase B (Akt)/ mammalian target of rapamycin (mTOR) pathways. PDF64 exhibited good antitumor effects both in vivo and in vitro . It caused cell apoptosis and autophagic death in HCT116 and Jurkat cells. The effects may be mediated by inhibiting c-Myc expression and ERK or PI3K-Akt-mTOR pathway. Therefore, PDF64 may be a promising reagent for antitumor drug development, which further supports that hsPDF is a good antitumor drug target.

Development of a nanobody-based ELISA for the detection of the insecticides cyantraniliprole and chlorantraniliprole in soil and the vegetable bok choy

Cyantraniliprole and chlorantraniliprole are anthranilic diamide insecticides acting on ryanodine receptors. In this study, two camel-derived nanobodies (Nbs, named C1 and C2) recognizing cyantraniliprole as well as chlorantraniliprole were generated. C1-based enzyme-linked immunosorbent assays (ELISAs) for the detection of the two insecticides were developed. The half-maximum signal inhibition concentrations (IC₅₀) of cyantraniliprole and chlorantraniliprole by ELISA were 1.2 and 1.5 ng mL^-1, respectively. This assay was employed to detect these two insecticides in soil and vegetables. The average recoveries of cyantraniliprole from both bok choy (Brassica chinensis L.) and soil samples were 90-129%, while those of chlorantraniliprole were in a range of 89-120%. The insecticide residues in soil and bok choy, which were collected from plots sprayed with cyantraniliprole and chlorantraniliprole, were simultaneously detected by the resulting ELISA and a high-performance liquid chromatography (HPLC) method, showing a satisfactory correlation. Higher concentrations of chlorantraniliprole than cyantraniliprole were detected in soil and vegetables, which indicates the longer persistence of chlorantraniliprole in the environment.

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.

Efficient mAb production in CHO cells with optimized signal peptide, codon, and UTR

Antibody drugs have been used to treat a number of diseases successfully. Producing antibodies with high yield and quality is necessary for clinical applications of antibodies. For a candidate molecule, optimization of a vector to produce sufficient yield and an accurate primary structure is indispensable in the early stage of the production process development. It is especially important to maintain the fidelity of N-terminal sequence. In order to produce antibodies with a high yield and accurate N-terminal, the expression vector was systematically optimized in this study. First, the heavy chain and light chain were co-expressed in Chinese hamster ovary (CHO) cells with different signal peptides. Mass spectrometry (MS) revealed that signal peptides Esp-K, Bsp-H, and 8Hsp-H were accurately deleted from mature antibodies. Further, the yield was doubled by codon optimization and increased by 50% with the presence of untranslated regions (UTR). The combination of UTR with optimal codon and signal peptide to form an expression vector resulted in yield improvement of 150% and correct N-terminal sequences. Moreover, the main product peak was above 98% as assessed by size-exclusion chromatography (SEC). Additionally, the bioactivity of products made from optimized transient gene expression (TGE) was almost identical to the standard sample. The production efficiency and product quality from the identified TGE optimization strategy was further demonstrated through application to two other antibodies. The expression level of SGE (stable gene expression) can also be improved effectively with this optimization strategy. In conclusion, vector optimization via combination of optimized signal peptide, codon, and UTR is an alternative approach for efficient antibody production with high fidelity N-terminal sequence in CHO cells.

Evolution of Synonymous Codon Usage Bias in West African and Central African Strains of Monkeypox Virus

The evolution of bias in synonymous codon usage in chosen monkeypox viral genomes and the factors influencing its diversification have not been reported so far. In this study, various trends associated with synonymous codon usage in chosen monkeypox viral genomes were investigated, and the results are reported. Identification of factors that influence codon usage in chosen monkeypox viral genomes was done using various codon usage indices, such as the relative synonymous codon usage, the effective number of codons, and the codon adaptation index. The Spearman rank correlation analysis and a correspondence analysis were used for correlating various factors with codon usage. The results revealed that mutational pressure due to compositional constraints, gene expression level, and selection at the codon level for utilization of putative optimal codons are major factors influencing synonymous codon usage bias in monkeypox viral genomes. A cluster analysis of relative synonymous codon usage values revealed a grouping of more virulent strains as one major cluster (Central African strains) and a grouping of less virulent strains (West African strains) as another major cluster, indicating a relationship between virulence and synonymous codon usage bias. This study concluded that a balance between the mutational pressure acting at the base composition level and the selection pressure acting at the amino acid level frames synonymous codon usage bias in the chosen monkeypox viruses. The natural selection from the host does not seem to have influenced the synonymous codon usage bias in the analyzed monkeypox viral genomes.

Ligand and Structure-Based Approaches for the Identification of Peptide Deformylase Inhibitors as Antibacterial Drugs

Peptide deformylase (PDF) is a metalloprotease catalyzing the removal of a formyl group from newly synthesized proteins, which makes it an important antibacterial drug target. Given the importance of PDF inhibitors like actinonin in antibacterial drug discovery, several reported potent PDF inhibitors were used to develop pharmacophore models using the Galahad module of Sybyl 7.1 software. Generated pharmacophore models were composed of two donor atom centers, four acceptor atom centers and two hydrophobic groups. Model-1 was screened against the Zinc database and several compounds were retrieved as hits. Compounds with Qfit values of more than 60 were employed to perform a molecular docking study with the receptor Escherichia coli PDF, then compounds with docking score values of more than 6 were used to predict the in silico pharmacokinetic and toxicity risk via OSIRIS property explorer. Two known PDF inhibitors were also used to perform a molecular docking study with E. coli PDF as reference molecules. The results of the molecular docking study were validated by reproducing the crystal structure of actinonin. Molecular docking and in silico pharmacokinetic and toxicity prediction studies suggested that ZINC08740166 has a relatively high docking score of 7.44 and a drug score of 0.78.

Extracellular expression and antiviral activity of a bovine interferon-alpha through codon optimization in Pichia pastoris

Interferons (IFNs) are the primary line of defense against infectious agents. In particular, IFN-α is an important antiviral cytokine and has a wide range of immune-modulating functions. Porcine and human IFN-α have been successfully prepared and play important roles in the prevention and therapy of viral diseases. To date, there has been limited applied research on bovine IFN-α. To achieve high-level expression of recombinant bovine IFN-α (bIFN-α) in Pichia pastoris for large-scale application, the bIFN-α gene was optimized and synthesized on the basis of codon bias of P. pastoris. Optimized bIFN-α (opti-bIFN-α) was successfully expressed in P. pastoris and directly secreted into the culture supernatant. The amount of extracellular soluble opti-bIFN-α was observed to be 200μg/mL in a shake flask. Expression efficiency of opti-bIFN-α was found to be about three times that of wild-type bIFN-α when the expression yield was compared at the same copies of the targeted gene. In addition, both the original cultural supernatant and purified opti-bIFN-α showed strong antiviral activity in MDBK cells (2×10(6)AU/mL and 1×10(7)AU/mg, respectively) and IBRS-2 cells (3×10(5)AU/mL and 1.5×10(6)AU/mg, respectively) against a recombinant vesicular stomatitis virus expressing the green fluorescence protein. In this study, we demonstrated high-level extracellular expression of opti-bIFN-α by P. pastoris. To the best of our knowledge, the opti-bIFN-α yield observed in this study is the highest to be reported to date. Our results demonstrated that the extracellular opti-bIFN-α with strong antiviral activity could be easily prepared and purified at a low cost and that it may be a potential biological therapeutic drug against bovine viral infections.

Structure-Based Drug Design of Small Molecule Peptide Deformylase Inhibitors to Treat Cancer

Human peptide deformylase (HsPDF) is an important target for anticancer drug discovery. In view of the limited HsPDF, inhibitors were reported, and high-throughput virtual screening (HTVS) studies based on HsPDF for developing new PDF inhibitors remain to be reported. We reported here on diverse small molecule inhibitors with excellent anticancer activities designed based on HTVS and molecular docking studies using the crystal structure of HsPDF. The compound M7594_0037 exhibited potent anticancer activities against HeLa, A549 and MCF-7 cell lines with IC_50s of 35.26, 29.63 and 24.63 μM, respectively. Molecular docking studies suggested that M7594_0037 and its three derivatives could interact with HsPDF by several conserved hydrogen bonds. Moreover, the pharmacokinetic and toxicity properties of M7594_0037 and its derivatives were predicted using the OSIRIS property explorer. Thus, M7594_0037 and its derivatives might represent a promising scaffold for the further development of novel anticancer drugs.

Expression and Characterization of a Recombinant Laccase with Alkalistable and Thermostable Properties from Streptomyces griseorubens JSD-1

Streptomyces griseorubens JSD-1 is a novel actinomycete that could grow efficiently upon lignin, and the ligninolytic genes active in this biotransformation were expected to be crucial. To investigate the molecular mechanism of utilizing lignin, genome sequencing was carried out to obtain its draft genome, which was deposited at GenBank under the accession No. JJMG00000000. Multiple copper oxidase (MCO) was obtained, which proved to be an extracellular enzyme and have relative high expression with the stimulation of ligninolytic materials. Judging from its putative 3D structure, the N-terminal of MCO was bared, which was fit for the linkage of poly-HIS10 tag. As a result, heterogeneous expression conditions of recombinant laccase was achieved with TransB(DE3) grown in a modified terrific broth (TB) medium with an extra addition of 0.5% glucose at 30 °C until optical density at 600 nm (OD600) reached 0.8 when expression was induced by 25 μM isopropyl β-D-1-thiogalactopyranoside (IPTG) and also 100 μM copper sulphate as supplement. Finally, it exhibited special characters of thermal robustness, alkaline activity profiles, high resistance to metallic ions and chemical inhibitors as well as dye decolourization. In summary, our findings illustrated the genetic basic of utilizing lignin in this isolate. Additionally, a novel laccase expected to be potential in agricultural and industrial application was expressed and characterized as well.

Influence of hydrophilic amino acids and GC-content on expression of recombinant proteins used in vaccines against foot-and-mouth disease virus in Escherichia coli

Epitope-based protein expression in Escherichia coli can be improved by adjusting its amino acid composition and encoding genes. To that end, we analyzed 24 recombinant epitope proteins (rEPs) that carry multiple epitopes derived from VP1 protein of foot-and-mouth disease virus. High level expression of the rEPs was attributed to a high content of Arg, Asn, Asp and Thr, a low content of Gln, Pro and Lys, a high content of hydrophilic amino acids and a higher isoelectric point value resulting from abundant Arg. It is also attributed to the appropriate guanine and cytosine content in the encoding genes. The data provide a reference for adjusting the amino acid composition in designing epitope-based proteins used in vaccines and for adjusting the synonymous codons to improve their expressions in E. coli.

Expression, purification and identification of CtCVNH, a novel anti-HIV (Human Immunodeficiency Virus) protein from Ceratopteris thalictroides

CVN (cyanovirin-N) is an anti-HIV protein. CVNH (cyanovirin-N homology) represents its homology. In a previous study, we first reported the full-length sequences of the CVNH gene cloned from Ceratopteris thalictroides. Based on the finding, the coding sequence of CtCVNH was optimized in the study, and then a pET prokaryotic expression vector was constructed. The purification and identification of CtCVNH protein were investigated, as well. SDS-PAGE analysis indicated that a 31 kDa protein was overexpressed and mainly accumulated in the soluble fraction. Only a single protein was obtained after the Ni- nitrilotriacetic acid (NTA) affinity chromatography. The purified protein was identified to be the recombinant CtCVNH by both Western blot and peptide mass fingerprinting analysis.

Metabolic responses to recombinant bioprocesses in Escherichia coli

Escherichia coli has been widely used for the production of recombinant proteins. However, the unbalances between host metabolism and recombinant biosynthesis continue to hamper the efficiency of these recombinant bioprocesses. The additional drainage of biosynthetic precursors toward recombinant processes burdens severely the metabolism of cells that, ultimately, elicits a series of stress responses, reducing biomass growth and recombinant protein production. Several strategies to overcome these metabolic limitations have been implemented; however, in most cases, improvements in recombinant protein expression were achieved at the expense of biomass growth arrest, which significantly hampers the efficiency of recombinant bioprocesses. With the advent of high throughput techniques and modelling approaches that provide a system-level understanding of the cellular systems, it is now expected that new advances in recombinant bioprocesses are achieved. By providing means to deal with these systems, our understanding on the metabolic behaviour of recombinant cells will advance and can be further explored to the design of suitable hosts and more efficient and cost-effective bioprocesses. Here, we review the major metabolic responses associated with recombinant processes and the engineering strategies relevant to overcome these stresses. Moreover, the advantages of applying systems levels engineering strategies to enhance recombinant protein production in E. coli cells are discussed and future perspectives on the advances of mathematical modelling approaches to study these systems are exposed.