Artificial transcriptional terminators

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The goal is to create a series of transcriptional terminators with varying efficiencies. The majority of transcriptional terminators have a G+C rich stem of 7(+/-1)bp and a loop of 4(+/-1) nucleodtides followed by a poly(U) tail. Two common loops are UUCG and GAAA, both of which are known to increase RNA hairpin stability. The sequence GCGGG(G) is a common sequence found on the 3' arm of the stem. [1]


Effects of stem loop sequence on terminator efficiency

Bulges and mismatches in the stem, as well low G+C content of the stem will lower TE more than reducing the length of or elimination of the poly(U) tail [2]. The sequences downstream of the poly(U) tail and between the stop codon and the start of the stem loop structure also affect the TE of a terminator, particularly T7Te or T3Te.

  • T7Te

Several sources [3] [Chamberlin 79] measured the termination efficiency(TE) of T7Te at around 90%. However, efficiency for the biobricks part BBa_B0012 [1], also T7Te, is around 30%. T7Te has a very short poly(U) tail and requires the further downstream sequence for efficiecent termination [3], and this further downstream sequence is lacking in BBa_B0012. If the sequence for BBa_B0012 is lengthened to include this downstream segment, then the TE of part should be improved.

Predicting terminator efficiency

It may be possible to predict terminator efficiency using methods from d'Aubenton, in particular, the score d assigned to a possible terminator sequence

d = nt*18.16+Y*96.59-116.87

where nt measures the statistical distribution of the T residues in the non transcribed DNA strand and Y is the free energy per nucleodtide of the stem loop structure.

The score d will give a rough estimate of how efficient a terminator is.

d<0: TE<20%

0<d<30: 20%<TE<70%

d>30: TE>70%

Ideal terminator

  • has 6 base stem with 3' sequence of GCGGGG
  • 4 base loop, either UUCG or GAAA
  • tail containing >8 uridines
  • for a biobrick part, flanking regions will be biobrick site


  1. d'Aubenton Carafa Y, Brody E, and Thermes C. . pmid:1702475. PubMed HubMed [Aubenton90]
  2. Abe H and Aiba H. . pmid:9150882. PubMed HubMed [Abe96]
  3. Reynolds R and Chamberlin MJ. . pmid:1372366. PubMed HubMed [Reynolds92II]
  4. Brendel V, Hamm GH, and Trifonov EN. . pmid:3078109. PubMed HubMed [Bredel86]
  5. Cheng SW, Lynch EC, Leason KR, Court DL, Shapiro BA, and Friedman DI. . pmid:1835546. PubMed HubMed [Cheng91]
  6. Christie GE, Farnham PJ, and Platt T. . pmid:7027254. PubMed HubMed [Christie81]
  7. Ermolaeva MD, Khalak HG, White O, Smith HO, and Salzberg SL. . pmid:10926490. PubMed HubMed [Ermolaeva00]
  8. Lesnik EA, Sampath R, Levene HB, Henderson TJ, McNeil JA, and Ecker DJ. . pmid:11522828. PubMed HubMed [Lesnik01]
  9. Lynn SP, Kasper LM, and Gardner JF. . pmid:2961747. PubMed HubMed [Lynn88]
  10. Petrillo M, Silvestro G, Di Nocera PP, Boccia A, and Paolella G. . pmid:16820051. PubMed HubMed [Petrillo06]
  11. Reynolds R, Bermúdez-Cruz RM, and Chamberlin MJ. . pmid:1372365. PubMed HubMed [Reynolds92I]
  12. Unniraman S, Prakash R, and Nagaraja V. . pmid:11809879. PubMed HubMed [Unniraman02]
  13. Uptain SM and Chamberlin MJ. . pmid:9391063. PubMed HubMed [Uptain97]
  14. von Hippel PH and Yager TD. . pmid:1536005. PubMed HubMed [VonHippel92]
  15. Wilson KS and von Hippel PH. . pmid:7966320. PubMed HubMed [Wilson94]
  16. Wilson KS and von Hippel PH. . pmid:7568019. PubMed HubMed [Wilson95]
  17. Yager TD and von Hippel PH. . pmid:1703438. PubMed HubMed [Yager91]
All Medline abstracts: PubMed HubMed
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