scholarly journals Assessing optimal: inequalities in codon optimization algorithms

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Matthew J. Ranaghan ◽  
Jeffrey J. Li ◽  
Dylan M. Laprise ◽  
Colin W. Garvie
Keyword(s):  
Dna Sequences ◽  
Codon Optimization ◽  
Recombinant Protein Expression ◽  
Optimization Algorithms ◽  
Synonymous Codons ◽  
Variable Activity ◽  
Common Resource ◽  
Activity Of Enzymes ◽  
Native Host

Abstract Background Custom genes have become a common resource in recombinant biology over the last 20 years due to the plummeting cost of DNA synthesis. These genes are often “optimized” to non-native sequences for overexpression in a non-native host by substituting synonymous codons within the coding DNA sequence (CDS). A handful of studies have compared native and optimized CDSs, reporting different levels of soluble product due to the accumulation of misfolded aggregates, variable activity of enzymes, and (at least one report of) a change in substrate specificity. No study, to the best of our knowledge, has performed a practical comparison of CDSs generated from different codon optimization algorithms or reported the corresponding protein yields. Results In our efforts to understand what factors constitute an optimized CDS, we identified that there is little consensus among codon-optimization algorithms, a roughly equivalent chance that an algorithm-optimized CDS will increase or diminish recombinant yields as compared to the native DNA, a near ubiquitous use of a codon database that was last updated in 2007, and a high variability of output CDSs by some algorithms. We present a case study, using KRas4B, to demonstrate that a median codon frequency may be a better predictor of soluble yields than the more commonly utilized CAI metric. Conclusions We present a method for visualizing, analyzing, and comparing algorithm-optimized DNA sequences for recombinant protein expression. We encourage researchers to consider if DNA optimization is right for their experiments, and work towards improving the reproducibility of published recombinant work by publishing non-native CDSs.

scholarly journals ICOR: Improving codon optimization with recurrent neural networks

2021 ◽  
Author(s):  
Rishab Jain ◽  
Aditya Jain ◽  
Elizabeth Mauro ◽  
Kevin LeShane ◽  
Douglas Densmore
Keyword(s):  
Escherichia Coli ◽  
Amino Acids ◽  
Heterologous Expression ◽  
Codon Usage ◽  
Dna Sequences ◽  
Codon Optimization ◽  
Optimization Techniques ◽  
Codon Adaptation Index ◽  
Synonymous Codons ◽  
Adaptation Index

In protein sequences—as there are 61 sense codons but only 20 standard amino acids—most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. We demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome, therefore improving protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR's performance across five metrics is compared to that of five different codon optimization techniques. The codon adaptation index -- a metric indicative of high real-world expression -- was utilized as the primary benchmark in this study. ICOR is shown to improve the codon adaptation index by 41.69% and 17.25% compared to the original and Genscript's GenSmart-optimized sequences, respectively. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

Endangered Butterflies and their Non-Native Host Plants: Examining Shifting Values of Belonging in Restoration

2019 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
Robert M. Anderson ◽  
Amy M. Lambert
Keyword(s):  
Ecological Restoration ◽  
Native Species ◽  
Puget Sound ◽  
A Value ◽  
The Social ◽  
Remote Island ◽  
Scientific Truth ◽  
Native Host

The island marble butterfly (Euchloe ausonides insulanus), thought to be extinct throughout the 20th century until re-discovered on a single remote island in Puget Sound in 1998, has become the focus of a concerted protection effort to prevent its extinction. However, efforts to “restore” island marble habitat conflict with efforts to “restore” the prairie ecosystem where it lives, because of the butterfly’s use of a non-native “weedy” host plant. Through a case study of the island marble project, we examine the practice of ecological restoration as the enactment of particular norms that define which species are understood to belong in the place being restored. We contextualize this case study within ongoing debates over the value of “native” species, indicative of deep-seated uncertainties and anxieties about the role of human intervention to alter or manage landscapes and ecosystems, in the time commonly described as the “Anthropocene.” We interpret the question of “what plants and animals belong in a particular place?” as not a question of scientific truth, but a value-laden construct of environmental management in practice, and we argue for deeper reflexivity on the part of environmental scientists and managers about the social values that inform ecological restoration.

scholarly journals Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3385
Author(s):  
Erickson Puchta ◽  
Priscilla Bassetto ◽  
Lucas Biuk ◽  
Marco Itaborahy Filho ◽  
Attilio Converti ◽  
...  
Keyword(s):  
Pid Controller ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Buck Converter ◽  
Algebraic Solution ◽  
Nonlinear Load ◽  
Bee Colony ◽  
Arduous Task ◽  
Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Selection intensity for codon bias.

Genetics ◽  
1994 ◽  
Vol 138 (1) ◽  
pp. 227-234 ◽  
Author(s):  
D L Hartl ◽  
E N Moriyama ◽  
S A Sawyer
Keyword(s):  
Amino Acids ◽  
Dna Sequences ◽  
Codon Bias ◽  
Enteric Bacteria ◽  
Average Intensity ◽  
Ancestral State ◽  
Selection Intensity ◽  
Coding Region ◽  
Effective Population ◽  
Synonymous Codons

Abstract The patterns of nonrandom usage of synonymous codons (codon bias) in enteric bacteria were analyzed. Poisson random field (PRF) theory was used to derive the expected distribution of frequencies of nucleotides differing from the ancestral state at aligned sites in a set of DNA sequences. This distribution was applied to synonymous nucleotide polymorphisms and amino acid polymorphisms in the gnd and putP genes of Escherichia coli. For the gnd gene, the average intensity of selection against disfavored synonymous codons was estimated as approximately 7.3 x 10(-9); this value is significantly smaller than the estimated selection intensity against selectively disfavored amino acids in observed polymorphisms (2.0 x 10(-8)), but it is approximately of the same order of magnitude. The selection coefficients for optimal synonymous codons estimated from PRF theory were consistent with independent estimates based on codon usage for threonine and glycine. Across 118 genes in E. coli and Salmonella typhimurium, the distribution of estimated selection coefficients, expressed as multiples of the effective population size, has a mean and standard deviation of 0.5 +/- 0.4. No significant differences were found in the degree of codon bias between conserved positions and replacement positions, suggesting that translational misincorporation is not an important selective constraint among synonymous polymorphic codons in enteric bacteria. However, across the first 100 codons of the genes, conserved amino acids with identical codons have significantly greater codon bias than that of either synonymous or nonidentical codons, suggesting that there are unique selective constraints, perhaps including mRNA secondary structures, in this part of the coding region.

VMLP neural network design using optimization algorithms to predict spider suspend (Case Study: Watershed Dam Kardeh)

2018 ◽  
Vol 18 (51) ◽  
pp. 183-198
Author(s):  
masooume darmani ◽  
Mohammad Nahtani ◽  
haedeh Ara ◽  
Ali Golkarian ◽  
Salman Sharif Azari ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Optimization Algorithms

Fixture Layout Design of Sheet Metal Parts Based on Global Optimization Algorithms

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
YanFeng Xing
Keyword(s):  
Finite Element ◽  
Global Optimization ◽  
Sheet Metal ◽  
Optimization Algorithms ◽  
Layout Optimization ◽  
Sheet Metal Parts ◽  
Fixture Layout ◽  
Fixture Layout Optimization ◽  
Dimensional Variation

Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated with a quantity of finite element (FE) analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3dcs simulation (for dimensional analyses) and global optimization algorithms. In this paper, two global algorithms are proposed to optimize fixture locator points, which are social radiation algorithm (SRA) and GAOT, a genetic algorithm (GA) in optimization toolbox in matlab. The flowchart of fixture design includes the following steps: (1) The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. (2) The two global optimization algorithms are edited to be used to optimize fixture schemes based on matlab. (3) Since matlab macrocommands of 3dcs have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the GAOT algorithm is more suitable than SRA for generating the optimal fixture layout with excellent efficiency for engineering applications.

Are polymorphic species of Uropodina (Acari: Mesostigmata) more successful evolutionarily?—A case study of closely related species from the genus Oodinychus Berlese, 1917 based on DNA sequences

2019 ◽  
Vol 24 (5) ◽  
pp. 866
Author(s):  
Jerzy Błoszyk ◽  
Katarzyna Buczkowska ◽  
Anna Maria Bobowicz ◽  
Alina Bączkiewicz ◽  
Zbigniew Adamski ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Related Species ◽  
Habitat Preferences ◽  
Abundant Species ◽  
Systematic Position ◽  
Genetic Differences ◽  
Similar Species ◽  
Closely Related Species ◽  
Wide Range

The study presented in this research paper is the first taxonomic investigation focusing on Uropodina (Acari: Mesostigmata) mites with a brief discussion of the genetic differences of two very closely related species from the genus Oodinychus Berlese, 1917, i.e. O. ovalis (C.L. Koch, 1839) and O. karawaiewi (Berlese, 1903). These two morphologically similar species are quite common and they have a wide range of occurrence in Europe. They also live in almost the same types of habitat. However, O. ovalis usually exhibits higher abundance and frequency of occurrence. The major aim of the study was to carry out a comparative analysis of the systematic position, morphological and biological differences, as well as habitat preferences and distribution of O. ovalis and O. karawaiewi. The next aim was to ascertain whether the differences in number and frequency of these species may stem from the genetic differences at the molecular level (16S rDNA and COI). The study shows that O. ovalis, which is a more abundant species than O. karawaiewi, turned out to be genetically more polymorphic.

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