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Title: Model-based design of synthetic antisense RNA for predictable gene repression
Our enhanced understanding of RNA folding and function has increased the use of small RNA regulators. Among these RNA regulators, synthetic antisense RNA (asRNA) is designed to contain an RNA sequence complementary to the target mRNA sequence, and the formation of double-stranded RNA (dsRNA) facilitates gene repression due to dsRNA degradation or prevention of ribosome access to the mRNA. Despite the simple complementarity rule, however, predictably tunable repression has been challenging when synthetic asRNAs are used. Here, the protocol for model-based asRNA design is described. This model can predict synthetic asRNA-mediated repression efficiency using two parameters: the change in free energy of complex formation (ΔGCF) and percent mismatch of the target binding region (TBR). The model has been experimentally validated in both Gram-positive and Gram-negative bacteria as well as for target genes in both plasmids and chromosomes. These asRNAs can be created by simply replacing the TBR sequence with one that is complementary to the target mRNA sequence of interest. In principle, this protocol can be applied to design and build asRNAs for predictable gene repression in various contexts, including multiple target genes and organisms, making asRNAs predictably tunable regulators for broad applications.  more » « less
Award ID(s):
2001743
PAR ID:
10414745
Author(s) / Creator(s):
Date Published:
Journal Name:
Methods in molecular biology
ISSN:
1064-3745
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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