COStar: A D-star Lite-based dynamic search algorithm for codon optimization

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 Integer Linear Programming Model to Optimize Coding DNA Sequences By Joint Control of Transcript Indicators

A Coding DNA Sequence (CDS) is a fraction of DNA whose nucleotides are grouped into consecutive triplets called codons, each one encoding an amino acid. Because most amino acids can be encoded by more than one codon, the same amino acid chain can be obtained by a very large number of different CDSs. These synonymous CDSs show different features that, also depending on the organism the transcript is expressed in, could affect translational efficiency and yield. The identification of optimal CDSs with respect to given transcript indicators is in general a challenging task, but it has been observed in recent literature that integer linear programming (ILP) can be a very flexible and efficient way to achieve it. In this article, we add evidence to this observation by proposing a new ILP model that simultaneously optimizes different well-grounded indicators. With this model, we efficiently find solutions that dominate those returned by six existing codon optimization heuristics.

Improved ant colony optimization for safe path planning of AUV

In order to address the autonomous underwater vehicle navigation challenge for dam inspections, with the goal of enabling safe inspections and reliable obstacle avoidance, an improved smooth Ant Colony Optimization algorithm is proposed for path planning. This improved algorithm would optimize the smoothness of the path besides the robustness, avoidance of local optima, and fast computation speed. To achieve the goal of reducing turning time and improving the directional effect of path selection, a corner-turning heuristic function is introduced. Experimental simulation results show that the improved algorithm performs best than other algorithms in terms of path smoothness and iteration stability in path planning.

PRM-D* Method for Mobile Robot Path Planning

Various navigation tasks involving dynamic scenarios require mobile robots to meet the requirements of a high planning success rate, fast planning, dynamic obstacle avoidance, and shortest path. PRM (probabilistic roadmap method), as one of the classical path planning methods, is characterized by simple principles, probabilistic completeness, fast planning speed, and the formation of asymptotically optimal paths, but has poor performance in dynamic obstacle avoidance. In this study, we use the idea of hierarchical planning to improve the dynamic obstacle avoidance performance of PRM by introducing D* into the network construction and planning process of PRM. To demonstrate the feasibility of the proposed method, we conducted simulation experiments using the proposed PRM-D* (probabilistic roadmap method and D*) method for maps of different complexity and compared the results with those obtained by classical methods such as SPARS2 (improving sparse roadmap spanners). The experiments demonstrate that our method is non-optimal in terms of path length but second only to graph search methods; it outperforms other methods in static planning, with an average planning time of less than 1 s, and in terms of the dynamic planning speed, our method is two orders of magnitude faster than the SPARS2 method, with a single dynamic planning time of less than 0.02 s. Finally, we deployed the proposed PRM-D* algorithm on a real vehicle for experimental validation. The experimental results show that the proposed method was able to perform the navigation task in a real-world scenario.

The Path Planning and Location Method of Inspection Robot in a Large Storage Tank Bottom

With the development of robot technology, inspection robots have been applied to the defect detection of large tanks. However, the existing path planning algorithm of the tank bottom detection robot is easy to fall into the local minimum, and the path is not smooth. Besides, the positioning of the tank bottom detection robot is not accurate. This article proposes a path planning and location algorithm for the large tank bottom detection robot. Specifically, we design a preset spiral path according to the shape of the tank bottom, and a rotating potential field (RPF) near the obstacle is added to avoid the problem of path planning falling into a local minimum. We obtained accurate and smooth planning results. Compared with the state-of-the-art, the RPF method reduced the average RMSE by 9.49%. In addition, by measuring the acoustic emission distance, the three-point positioning algorithm can be used to achieve the calculation of the robot position detection in the proposed method, and the average positioning error on the spiral path is only 0.0748 ± 0.0032.

Auto-splitting D* lite path planning for large disaster area

This research introduces a new path planning method for rescue robots in a dynamic and partially known area when the robots are performing tasks in a large area. The path planning of the rescue robots that move in the dynamic area is introduced to solve the issue of unnecessary areas, which are the disadvantages of the existing D*-based algorithms. This paper proposes a method to eliminate unnecessary expanded nodes of the dynamic and partially known environment by segmenting a map with an auto-clustering algorithm, which is able to achieve a faster execution time than conventional algorithms. Furthermore, to show the effectiveness of the proposed algorithms, an expected value of re-planned nodes in the dynamic and partially known area is introduced using a probability-based approach.

Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic, and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multisite random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.

Codon optimization: a mathematical programing approach

Motivation

Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions.

Results

We develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations.

Availability and implementation

http://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.

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.

Modulating the 3' end-DNA and the fermentation process for enhanced production and biological activity of porcine interferon-gamma

Porcine gamma interferon is a cytokine produced by activated T cells and NK cells with broad-spectrum antiviral activity and immunomodulatory function. However, pIFN-γ is a secretory protein that has a short half-life in organisms and is easily inactivated, making it difficult to apply widely in clinics. Therefore, we tried to optimize the expression of pIFN-γ in Pichia pastoris to obtain a large amount of highly active, easily purified pIFN-γ protein in vitro. Through C-terminal sequence analysis, we found a signal sequence (EKREAEAE) that was easily enzymolysed by a signal peptide enzyme, resulting in degradation and inactivation of the pIFN-γ protein. In this study, we optimized the pIFN-γ gene recombination sequence and mutated the 3' end of the pIFN-γ gene, resulting in a higher expression level and stronger biological activity, as well as a significant upregulation in the expression of the interferon-stimulated genes Mx1 and OAS1 in IPEC-J2 jejunal epithelial cells. Our data also showed that the fermentation process could significantly improve productivity. A recombinant Pichia pastoris strain with the optimized pIFN-γ gene could obtain a high yield of pIFN-γ protein, up to 9536 mg/L, after staged incubation for 0–24 h at 28°C, pH 6.0, and 50% dissolved oxygen (DO), followed by incubation for 24–72 h at 25°C, pH 6.0 and 30% DO. These data demonstrated, for the first time, that the expression level of pIFN-γ in Pichia pastoris was improved significantly by gene optimization with 3' end mutation and a fermentation process that maintained good biological activity, which is beneficial to the application of pIFN-γ in animal husbandry.

Presyncodon, a Web Server for Gene Design with the Evolutionary Information of the Expression Hosts

In the natural host, most of the synonymous codons of a gene have been evolutionarily selected and related to protein expression and function. However, for the design of a new gene, most of the existing codon optimization tools select the high-frequency-usage codons and neglect the contribution of the low-frequency-usage codons (rare codons) to the expression of the target gene in the host. In this study, we developed the method Presyncodon, available in a web version, to predict the gene code from a protein sequence, using built-in evolutionary information on a specific expression host. The synonymous codon-usage pattern of a peptide was studied from three genomic datasets (Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae). Machine-learning models were constructed to predict a selection of synonymous codons (low- or high-frequency-usage codon) in a gene. This method could be easily and efficiently used to design new genes from protein sequences for optimal expression in three expression hosts (E. coli, B. subtilis, and S. cerevisiae). Presyncodon is free to academic and noncommercial users; accessible at http://www.mobioinfor.cn/presyncodon_www/index.html.

Predicting synonymous codon usage and optimizing the heterologous gene for expression in E. coli

Of the 20 common amino acids, 18 are encoded by multiple synonymous codons. These synonymous codons are not redundant; in fact, all of codons contribute substantially to protein expression, structure and function. In this study, the codon usage pattern of genes in the E. coli was learned from the sequenced genomes of E. coli. A machine learning based method, Presyncodon was proposed to predict synonymous codon selection in E. coli based on the learned codon usage patterns of the residue in the context of the specific fragment. The predicting results indicate that Presycoden could be used to predict synonymous codon selection of the gene in the E. coli with the high accuracy. Two reporter genes (egfp and mApple) were designed with a combination of low- and high-frequency-usage codons by the method. The fluorescence intensity of eGFP and mApple expressed by the (egfp and mApple) designed by this method was about 2.3- or 1.7- folds greater than that from the genes with only high-frequency-usage codons in E. coli. Therefore, both low- and high-frequency-usage codons make positive contributions to the functional expression of the heterologous proteins. This method could be used to design synthetic genes for heterologous gene expression in biotechnology.

Synthetic gene design-The rationale for codon optimization and implications for molecular pharming in plants

Degeneracy in the genetic code allows multiple codon sequences to encode the same protein. Codon usage bias in genes is the term given to the preferred use of particular synonymous codons. Synonymous codon substitutions had been regarded as "silent" as the primary structure of the protein was not affected; however, it is now accepted that synonymous substitutions can have a significant effect on heterologous protein expression. Codon optimization, the process of altering codons within the gene sequence to improve recombinant protein expression, has become widely practised. Multiple inter-linked factors affecting protein expression need to be taken into consideration when optimizing a gene sequence. Over the years, various computer programmes have been developed to aid in the gene sequence optimization process. However, as the rulebook for altering codon usage to affect protein expression is still not completely understood, it is difficult to predict which strategy, if any, will design the "optimal" gene sequence. In this review, codon usage bias and factors affecting codon selection will be discussed and the evidence for codon optimization impact will be reviewed for recombinant protein expression using plants as a case study. These developments will be relevant to all recombinant expression systems; however, molecular pharming in plants is an area which has consistently encountered difficulties with low levels of recombinant protein expression, and should benefit from an evidence based rational approach to synthetic gene design. Biotechnol. Bioeng. 2017;114: 492-502. © 2016 Wiley Periodicals, Inc.

Engineering Translation in Mammalian Cell Factories to Increase Protein Yield: The Unexpected Use of Long Non-Coding SINEUP RNAs

Mammalian cells are an indispensable tool for the production of recombinant proteins in contexts where function depends on post-translational modifications. Among them, Chinese Hamster Ovary (CHO) cells are the primary factories for the production of therapeutic proteins, including monoclonal antibodies (MAbs). To improve expression and stability, several methodologies have been adopted, including methods based on media formulation, selective pressure and cell- or vector engineering. This review presents current approaches aimed at improving mammalian cell factories that are based on the enhancement of translation. Among well-established techniques (codon optimization and improvement of mRNA secondary structure), we describe SINEUPs, a family of antisense long non-coding RNAs that are able to increase translation of partially overlapping protein-coding mRNAs. By exploiting their modular structure, SINEUP molecules can be designed to target virtually any mRNA of interest, and thus to increase the production of secreted proteins. Thus, synthetic SINEUPs represent a new versatile tool to improve the production of secreted proteins in biomanufacturing processes.

Effect of cooperation of chaperones and gene dosage on the expression of porcine PGLYRP-1 in Pichia pastoris

Mammalian peptidoglycan recognition proteins (PGLYRPs) are highly conserved pattern-recognition molecules of the innate immune system with considerable bactericidal activity, which manifest their potential values for the application to food and pharmaceutical industry. However, the effective expression of porcine PGLYRP-1 in Pichia pastoris has not been reported so far. In this study, expression in P. pastoris was explored as an efficient way to produce functional porcine PGLYRP-1. Cooperation of chaperones co-expression and gene dosage (including protein disulfide isomerase (PDI)/binding protein (BiP) and pglyrp-1) were used to enhance functional expression of antimicrobial protein in P. pastoris. Overexpression of PDI was certainly able to increase secretion level of PGLYRP-1 protein because the increase in secreted PGLYRP-1 secretion was correlated with the copy numbers of PDI in high copy pglyrp-1 clones. However, co-expression of BiP was proved to be detrimental to PGLYRP-1 secretion. In addition, we also found that excessive expression of PDI and/or BiP could decrease the mRNA expression of pglyrp-1 gene. This showed that PDI and BiP as the target genes of unfolded protein response (UPR) might regulate the transcription of the target protein. These data demonstrated for the first time that the combination of chaperones and gene dosages could improve the yield of PGLYRP-1, which could facilitate the application to food and pharmaceutical industry.

Multi-omics data driven analysis establishes reference codon biases for synthetic gene design in microbial and mammalian cells

In this study, we analyzed multi-omics data and subsets thereof to establish reference codon usage biases for codon optimization in synthetic gene design. Specifically, publicly available genomic, transcriptomic, proteomic and translatomic data for microbial and mammalian expression hosts, Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Chinese hamster ovary (CHO) cells, were compiled to derive their individual codon and codon pair frequencies. Then, host dependent and -omics specific codon biases were generated and compared by principal component analysis and hierarchical clustering. Interestingly, our results indicated the similar codon bias patterns of the highly expressed transcripts, highly abundant proteins, and efficiently translated mRNA in microbial cells, despite the general lack of correlation between mRNA and protein expression levels. However, for CHO cells, the codon bias patterns among various -omics subsets are not distinguishable, forming one cluster. Thus, we further investigated the effect of different input codon biases on codon optimized sequences using the codon context (CC) and individual codon usage (ICU) design parameters, via in silico case study on the expression of human IFNγ sequence in CHO cells. The results supported that CC is more robust design parameter than ICU for improved heterologous gene design.

Molecular mechanisms for enhanced DNA vaccine immunogenicity

In the two decades since their initial discovery, DNA vaccines technologies have come a long way. Unfortunately, when applied to human subjects inadequate immunogenicity is still the biggest challenge for practical DNA vaccine use. Many different strategies have been tested in preclinical models to address this problem, including novel plasmid vectors and codon optimization to enhance antigen expression, new gene transfection systems or electroporation to increase delivery efficiency, protein or live virus vector boosting regimens to maximise immune stimulation, and formulation of DNA vaccines with traditional or molecular adjuvants. Better understanding of the mechanisms of action of DNA vaccines has also enabled better use of the intrinsic host response to DNA to improve vaccine immunogenicity. This review summarizes recent advances in DNA vaccine technologies and related intracellular events and how these might impact on future directions of DNA vaccine development.