scholarly journals A combinatorial method to isolate short ribozymes from complex ribozyme libraries

2020 ◽  
Vol 48 (20) ◽  
pp. e116-e116
Author(s):  
Joshua T Arriola ◽  
Ulrich F Müller

Abstract In vitro selections are the only known methods to generate catalytic RNAs (ribozymes) that do not exist in nature. Such new ribozymes are used as biochemical tools, or to address questions on early stages of life. In both cases, it is helpful to identify the shortest possible ribozymes since they are easier to deploy as a tool, and because they are more likely to have emerged in a prebiotic environment. One of our previous selection experiments led to a library containing hundreds of different ribozyme clusters that catalyze the triphosphorylation of their 5′-terminus. This selection showed that RNA systems can use the prebiotically plausible molecule cyclic trimetaphosphate as an energy source. From this selected ribozyme library, the shortest ribozyme that was previously identified had a length of 67 nucleotides. Here we describe a combinatorial method to identify short ribozymes from libraries containing many ribozymes. Using this protocol on the library of triphosphorylation ribozymes, we identified a 17-nucleotide sequence motif embedded in a 44-nucleotide pseudoknot structure. The described combinatorial approach can be used to analyze libraries obtained by different in vitro selection experiments.

1994 ◽  
Vol 4 (4) ◽  
pp. 618-622 ◽  
Author(s):  
Karen B. Chapman ◽  
Jack W. Szostak

RNA ◽  
2020 ◽  
Vol 26 (8) ◽  
pp. 1060-1068
Author(s):  
Devin P. Bendixsen ◽  
Jessica M. Roberts ◽  
Brent Townshend ◽  
Eric J. Hayden

ACS Omega ◽  
2021 ◽  
Author(s):  
Douglas Magde ◽  
Arvin Akoopie ◽  
Michael D. Magde ◽  
Ulrich F. Müller

2003 ◽  
Vol 86 (3) ◽  
pp. 844-854 ◽  
Author(s):  
Marcus Hey ◽  
Christian Hartel ◽  
Michael W. Göbel

10.29007/vkrq ◽  
2020 ◽  
Author(s):  
Puzhou Wang

In vitro selection enables the identification of functional DNA or RNA sequences (i.e., active sequences) out of entirely or partially random pools. Various computational tools have been developed for the analysis of sequencing data from selection experiments. However, most of these tools rely on structure-function relationship that is usually unknown for de novo selection experiments. This largely restricts the applications of these algorithms. In this paper, an active sequence predictor based on Latent Dirichlet allocation (LDA), ASPECT (Active Sequence PrEdiCTor), is proposed. ASPECT is independent of a priori knowledge on the structures of active sequences. Experimental results showed that ASPECT is effective.


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