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Modeling ribosome dynamics to optimize heterologous protein production

A protein and ribosome technology, applied in the field of 3' rRNA tail, a kind of protein production, can solve the problems of translocation, poor translation, spacing, etc.

Active Publication Date: 2017-08-29
NORTH CAROLINA STATE UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Unfortunately, attempts to translate unmodified foreign genes in biological organisms such as E. coli often result in low or no synthesis of the desired protein [1, 2]
Some of the problems identified are poor translation, non-optimal ribosome binding site (RBS), RBS and start codon spacing, translocations, premature termination, and protein aggregation due to protein misfolding [1, 2]
Although protein yield maximization has been studied in detail for some time, the underlying processes and effects of translation elongation on protein yield have not been resolved

Method used

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  • Modeling ribosome dynamics to optimize heterologous protein production
  • Modeling ribosome dynamics to optimize heterologous protein production
  • Modeling ribosome dynamics to optimize heterologous protein production

Examples

Experimental program
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Embodiment 1

[0090] A new biophysical model of translation elongation

[0091] Discovery of periodic free energy signals

[0092] A periodic free energy signal is observed from the average Watson-Crick binding between the 16S rRNA 3' end "exposed tail" (3'-AUUACCUCCACUAG-5') and mRNA during translation, as figure 1 shown in [45,46]. The most prominent binding energy is at priming, which corresponds to the "exposed tail" of the anti-Shine-Dalgarno (aSD) binding Shine-Dalgarno (SD) sequence. Thereafter, during elongation there is a periodic (sinusoid-like) binding signal corresponding to "in-frame" ribosome translocation (where negative free energy indicates binding) [45, 46, 47]. The Fourier transform of this periodic signal shows prominent peaks with a frequency of 1 / 3 period per nucleotide [46, 48]. This illustrates that the exposed tail binds the mRNA at each codon during elongation. However, after reaching the stop codon, the signal decays rapidly, suggesting that the ribosome h...

Embodiment 2

[0124] Algorithms and Mathematics

[0125] The following examples have been included to provide those of ordinary skill in the art with guidance for practicing representative embodiments of the presently disclosed subject matter. The following specific examples and equations are for illustrative purposes only and should not be construed as limiting the methods of the present invention in any way.

[0126] The ribosome spring model and ribosome translocation were modeled using step-size integrators and a 'probabilistic period' algorithm as described above. This probabilistic periodic algorithm calculates ribosomal translocation and ribosomal "latency" at each codon using the force convolved with the probability of picking up an aminoacyl-tRNA (aa-tRNA). The ribosome displaces every "cycle" with a step function calculated as force multiplied by the step constant δT(dT); a cycle is defined as the state in which the ribosome has not picked up aa-tRNA. Probability cycles are de...

Embodiment 3

[0296] Example Study: Forecasting and Optimization

[0297] The predictive and optimization capabilities of the model were evaluated by expressing five model genes in Escherichia coli: gst (glutathione-S-transferase), pf0132 (α-glucosidase), clju_c11880 (alcohol dehydrogenase) and rt8_0542 ( endoglucanase and exoglucanase). The optimization power of the model was also compared to a codon bias optimization algorithm as described elsewhere herein. Multiple mRNA variants of GST were also designed and expressed to further validate the model's predictions compared to those of codon bias. The codon adaptation index (CAI) of Sharpe et al. [86] was used to measure the codon bias of genes. Measured protein yield (GST activity) was correlated with predicted protein yield by codon adaptation index (CAI) and model index (BNI). The rationale for comparing correlations was to demonstrate that the determinants of protein yield are not limited to codon bias, but encompass convolutions be...

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Abstract

The invention relates to modeling ribosome dynamics to optimize heterologous protein production. The presently disclosed subject matter provides a free-energy based model of translation elongation to predict and optimize heterologous gene expression. The model and software allow for the prediction and optimization of genes for increased or decreased protein yield and for increased or decreased protein aggregation.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit of US Provisional Application No. 62 / 100,417, filed January 6, 2015, the entire contents of which are hereby incorporated by reference in their entirety. Background technique [0003] Translation is a biological process by which organisms produce specific polypeptides that fold into functional proteins [1, 13]. Heterologous protein synthesis uses translation to produce proteins not normally produced in the host organism [2, 3]. These proteins are used in a wide range of industrial applications, from biofuel production to agriculture and biopharmaceuticals. Unfortunately, attempts to translate unmodified foreign genes in biological organisms such as E. coli often result in low or no synthesis of the desired protein [1, 2]. Some of the problems identified are poor translation, non-optimal ribosome binding site (RBS), RBS and start codon spacing, translocations, premature termination, a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/68G16B5/20
CPCC12N15/67G01N33/68G16B20/00G16B5/20G16B5/00G16B25/10G16B40/00
Inventor 斯科特·科尔·乌唐纳德·L·比策
Owner NORTH CAROLINA STATE UNIV
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