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

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

An exploratory in silico comparison of open-source codon harmonization tools

BACKGROUND

Not changing the native constitution of genes prior to their expression by a heterologous host can affect the amount of proteins synthesized as well as their folding, hampering their activity and even cell viability. Over the past decades, several strategies have been developed to optimize the translation of heterologous genes by accommodating the difference in codon usage between species. While there have been a handful of studies assessing various codon optimization strategies, to the best of our knowledge, no research has been performed towards the evaluation and comparison of codon harmonization algorithms. To highlight their importance and encourage meaningful discussion, we compared different open-source codon harmonization tools pertaining to their in silico performance, and we investigated the influence of different gene-specific factors.

RESULTS

In total, 27 genes were harmonized with four tools toward two different heterologous hosts. The difference in %MinMax values between the harmonized and the original sequences was calculated (ΔMinMax), and statistical analysis of the obtained results was carried out. It became clear that not all tools perform similarly, and the choice of tool should depend on the intended application. Almost all biological factors under investigation (GC content, RNA secondary structures and choice of heterologous host) had a significant influence on the harmonization results and thus must be taken into account. These findings were substantiated using a validation dataset consisting of 8 strategically chosen genes.

CONCLUSIONS

Due to the size of the dataset, no complex models could be developed. However, this initial study showcases significant differences between the results of various codon harmonization tools. Although more elaborate investigation is needed, it is clear that biological factors such as GC content, RNA secondary structures and heterologous hosts must be taken into account when selecting the codon harmonization tool.

Folding of heterologous proteins in bacterial cell factories: Cellular mechanisms and engineering strategies

The expression of correctly folded and functional heterologous proteins is important in many biotechnological production processes, whether it is enzymes, biopharmaceuticals or biosynthetic pathways for production of sustainable chemicals. For industrial applications, bacterial platform organisms, such as E. coli, are still broadly used due to the availability of tools and proven suitability at industrial scale. However, expression of heterologous proteins in these organisms can result in protein aggregation and low amounts of functional protein. This review provides an overview of the cellular mechanisms that can influence protein folding and expression, such as co-translational folding and assembly, chaperone binding, as well as protein quality control, across different model organisms. The knowledge of these mechanisms is then linked to different experimental methods that have been applied in order to improve functional heterologous protein folding, such as codon optimization, fusion tagging, chaperone co-production, as well as strain and protein engineering strategies.

Biosynthetic pathways of triterpenoids and strategies to improve their Biosynthetic Efficiency

"Triterpenoids" can be considered natural products derived from the cyclization of squalene, yielding 3-deoxytriterpenes (hydrocarbons) or 3-hydroxytriterpenes. Triterpenoids are metabolites of these two classes of triterpenes, produced by the functionalization of their carbon skeleton. They can be categorized into different groups based on their structural formula/design. Triterpenoids are an important group of compounds that are widely used in the fields of pharmacology, food, and industrial biotechnology. However, inadequate synthetic methods and insufficient knowledge of the biosynthesis of triterpenoids, such as their structure, enzymatic activity, and the methods used to produce pure and active triterpenoids, are key problems that limit the production of these active metabolites. Here, we summarize the derivatives, pharmaceutical properties, and biosynthetic pathways of triterpenoids and review the enzymes involved in their biosynthetic pathway. Furthermore, we concluded the screening methods, identified the genes involved in the pathways, and highlighted the appropriate strategies used to enhance their biosynthetic production to facilitate the commercial process of triterpenoids through the synthetic biology method.

CHARMING: Harmonizing synonymous codon usage to replicate a desired codon usage pattern

There is a growing appreciation that synonymous codon usage, although historically regarded as phenotypically silent, can instead alter a wide range of mechanisms related to functional protein production, a term we use here to describe the net effect of transcription (mRNA synthesis), mRNA half-life, translation (protein synthesis) and the probability of a protein folding correctly to its active, functional structure. In particular, recent discoveries have highlighted the important role that sub-optimal codons can play in modifying co-translational protein folding. These results have drawn increased attention to the patterns of synonymous codon usage within coding sequences, particularly in light of the discovery that these patterns can be conserved across evolution for homologous proteins. Because synonymous codon usage differs between organisms, for heterologous gene expression it can be desirable to make synonymous codon substitutions to match the codon usage pattern from the original organism in the heterologous expression host. Here we present CHARMING (for Codon HARMonizING), a robust and versatile algorithm to design mRNA sequences for heterologous gene expression and other related codon harmonization tasks. CHARMING can be run as a downloadable Python script or via a web portal at http://www.codons.org.

mRNA codon optimization with quantum computers

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

Genetic and metabolic engineering challenges of C1-gas fermenting acetogenic chassis organisms

Unabated mining and utilisation of petroleum and petroleum resources and their conversion to essential fuels and chemicals have drastic environmental consequences, contributing to global warming and climate change. In addition, fossil fuels are finite resources, with a fast-approaching shortage. Accordingly, research efforts are increasingly focusing on developing sustainable alternatives for chemicals and fuels production. In this context, bioprocesses, relying on microorganisms, have gained particular interest. For example, acetogens use the Wood-Ljungdahl pathway to grow on single carbon C1-gases (CO2 and CO) as their sole carbon source and produce valuable products such as acetate or ethanol. These autotrophs can, therefore, be exploited for large-scale fermentation processes to produce industrially relevant chemicals from abundant greenhouse gases. In addition, genetic tools have recently been developed to improve these chassis organisms through synthetic biology approaches. This review will focus on the challenges of genetically and metabolically modifying acetogens. It will first discuss the physical and biochemical obstacles complicating successful DNA transfer in these organisms. Current genetic tools developed for several acetogens, crucial for strain engineering to consolidate and expand their catalogue of products, will then be described. Recent tool applications for metabolic engineering purposes to allow redirection of metabolic fluxes or production of non-native compounds will lastly be covered.

Codon Harmonization of a Kir3.1-KirBac1.3 Chimera for Structural Study Optimization

The expression of functional, folded, and isotopically enriched membrane proteins is an enduring bottleneck for nuclear magnetic resonance (NMR) studies. Indeed, historically, protein yield optimization has been insufficient to allow NMR analysis of many complex Eukaryotic membrane proteins. However, recent work has found that manipulation of plasmid codons improves the odds of successful NMR-friendly protein production. In the last decade, numerous studies showed that matching codon usage patterns in recombinant gene sequences to those in the native sequence is positively correlated with increased protein yield. This phenomenon, dubbed codon harmonization, may be a powerful tool in optimizing recombinant expression of difficult-to-produce membrane proteins for structural studies. Here, we apply this technique to an inward rectifier K⁺ Channel (Kir) 3.1-KirBac1.3 chimera. Kir3.1 falls within the G protein-coupled inward rectifier K⁺ (GIRK) channel family, thus NMR studies may inform on the nuances of GIRK gating action in the presence and absence of its G Protein, lipid, and small molecule ligands. In our hands, harmonized plasmids increase protein yield nearly two-fold compared to the traditional ‘fully codon optimized’ construct. We then employ a fluorescence-based functional assay and solid-state NMR correlation spectroscopy to show the final protein product is folded and functional.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.

Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways

Natural products (NPs), also known as secondary metabolites, are produced in bacteria, fungi, and plants. NPs represent a rich source of antibacterial, antifungal, and anticancer agents. Recent advances in DNA sequencing technologies and bioinformatics unveiled nature's great potential for synthesizing numerous NPs that may confer unprecedented structural and biological features. However, discovering novel bioactive NPs by genome mining remains a challenge. Moreover, even with interesting bioactivity, the low productivity of many NPs significantly limits their practical applications. Here we discuss the progress in developing bioinformatics tools for efficient discovery of bioactive NPs. In addition, we highlight computational methods for optimizing the productivity of NPs of pharmaceutical importance.

Identification and comparison of allergenicity of native and recombinant fish major allergen parvalbumins from Japanese flounder (Paralichthys olivaceus)

Compared with native parvalbumin, recombinant β-parvalbumin based on the optimized DNA sequence can be used in fish allergen confirmation.