Unknown

Dataset Information

0

Computational design of orthogonal ribosomes.


ABSTRACT: Orthogonal ribosomes (o-ribosomes), also known as specialized ribosomes, are able to selectively translate mRNA not recognized by host ribosomes. As a result, they are powerful tools for investigating translational regulation and probing ribosome structure. To date, efforts directed towards engineering o-ribosomes have involved random mutagenesis-based approaches. As an alternative, we present here a computational method for rationally designing o-ribosomes in bacteria. Working under the assumption that base-pair interactions between the 16S rRNA and mRNA serve as the primary mode for ribosome binding and translational initiation, the algorithm enumerates all possible extended recognition sequences for 16S rRNA and then chooses those candidates that: (i) have a similar binding strength to their target mRNA as the canonical, wild-type ribosome/mRNA pair; (ii) do not bind mRNA with the wild-type, canonical Shine-Dalgarno (SD) sequence and (iii) minimally interact with host mRNA irrespective of whether a recognizable SD sequence is present. In order to test the algorithm, we experimentally characterized a number of computationally designed o-ribosomes in Escherichia coli.

SUBMITTER: Chubiz LM 

PROVIDER: S-EPMC2475622 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

Computational design of orthogonal ribosomes.

Chubiz Lon M LM   Rao Christopher V CV  

Nucleic acids research 20080603 12


Orthogonal ribosomes (o-ribosomes), also known as specialized ribosomes, are able to selectively translate mRNA not recognized by host ribosomes. As a result, they are powerful tools for investigating translational regulation and probing ribosome structure. To date, efforts directed towards engineering o-ribosomes have involved random mutagenesis-based approaches. As an alternative, we present here a computational method for rationally designing o-ribosomes in bacteria. Working under the assumpt  ...[more]

Similar Datasets

| S-EPMC3733048 | biostudies-literature
| S-EPMC6142221 | biostudies-literature
| S-EPMC5814443 | biostudies-literature
2024-07-05 | GSE237017 | GEO
| S-EPMC6537907 | biostudies-literature
| S-EPMC2965240 | biostudies-literature
| S-EPMC4763938 | biostudies-literature
| 2665204 | ecrin-mdr-crc
| S-EPMC4108999 | biostudies-literature
| S-EPMC7483984 | biostudies-literature