Free energy periodicity in E. coli coding

Rosnick, David; Bitzer, Donald L.; Vouk, Mladen A.; May, Elebeoba

doi:10.1109/iembs.2000.901311

Cited by 8 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Messenger RNA leader sequences from E. coli K-12 strain MG1655 (down-loaded from the NIH ftp site: ncbi.nlm.nih.gov and parsed by Rosnick [79]) are used as training sequences for constructing the best candidate code model. There were three types of models: horizontal, horizontal motif, and vertical.…”

Section: The ð3; 1; 4þ Convolutional Code Modelmentioning

confidence: 99%

An error-correcting code framework for genetic sequence analysis

May

Vouk

Bitzer

et al. 2004

Journal of the Franklin Institute

View full text Add to dashboard Cite

Section: The ð3; 1; 4þ Convolutional Code Modelmentioning

confidence: 99%

An error-correcting code framework for genetic sequence analysis

May

Vouk

Bitzer

et al. 2004

Journal of the Franklin Institute

View full text Add to dashboard Cite

“…We calculate running averages of every third position along the signal, and interpolate a sine wave through the three resulting points (see Appendix). This method of calculating the magnitude works well in the presence of immense noise, which is characteristic of the E. coli genome [2].…”

Section: A Ensemble Average Signal and Characteristicsmentioning

confidence: 99%

“…About 90 bases downstream, we observe that the signal becomes strongly 3-base periodic. We will refer to this downstream signal as the "synchronization" signal, since it appears to reflect how the ribosomal subunit moves along the mRNA sequence till the formation of the polypeptide chain is complete [2].…”

Section: A Ensemble Average Signalmentioning

confidence: 99%

“…A particular alignment of the 3' exposed tail end against the messenger RNA is referred to as a conformation. The binding energy released in this conformation, also referred to as free energy, is estimated using the method of base-doublets [2]. This calculation penalizes mismatches and rewards consecutive base pairing in the conformation.…”

Section: A Ensemble Average Signalmentioning

confidence: 99%

“…denote the free energy score estimate for gene i at position x along the mRNA as computed in[2]. Let N denote the number of genes in the sample.…”

mentioning

confidence: 99%

See 2 more Smart Citations

The search for the optimal ribosome 3' tail end in E. coli

Ponnala

Barnes²,

Bitzer³

et al.

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Self Cite

View full text Add to dashboard Cite

The 16s ribosomal tail end has been conjectured to play an important role in the regulation of protein production and of translation efficiency. Using E. coli K-12 as our model organism, we generate sequences of 13 base pairs as hypothetical ribosome tail ends. We analyzed the distributions of these random hypothetical ribosome tail ends and found the actual E. coli ribosome tail end to be significantly different from a randomly generated ribosome tail in the magnitude of the lock and synchronization signals, and the signal to noise ratio. We then designed and ran a genetic algorithm to optimize hypothetical ribosome tail ends simultaneously for these three signal criteria. We found that the actual E. coli ribosome tail end was among the best by these measures.

show abstract

Modeling ribosome dynamics to optimize heterologous protein production in escherichia coli

Belloti

Gabriel

et al. 2014

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

View full text Add to dashboard Cite

Ineffective heterologous protein synthesis has often been ascribed to codon bias and rare codons. New experimental evidence suggests that codon bias alone may not be the sole cause of poor translation. In this paper we present a free-energy based model of translation elongation to predict and optimize genes for expression in E. coli. The model takes into account second order free energy effects from the binding between the anti-Shine-Dalgarno sequence of the 3' terminal 16S rRNA tail and the mRNA, tRNA abundance, and ribosome displacement. The model and software allow optimization of genes for increased (or decreased) protein yield. The model's predictive and optimization accuracy was assessed by optimizing and expressing three model genes and multiple mRNA variants coding for GST (26 kDa Glutathion S-Transferase from Schistosoma japonicum). Protein yield of optimized genes showed increase from their wildtype levels. Optimization of Glutathion S-Transferase from Schistosoma japonicum and Alcohol Dehydrogenase from Clostridium ljungdahlii DSM 13528 are discussed as examples.

show abstract

Free energy periodicity in E. coli coding

Cited by 8 publications

References 7 publications

An error-correcting code framework for genetic sequence analysis

An error-correcting code framework for genetic sequence analysis

The search for the optimal ribosome 3' tail end in E. coli

Modeling ribosome dynamics to optimize heterologous protein production in escherichia coli

Contact Info

Product

Resources

About