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Peptide bond formation by in vitro selected ribozymes

Author

Listed:
  • Biliang Zhang

    (Howard Hughes Medical Institute, University of Colorado)

  • Thomas R. Cech

    (Howard Hughes Medical Institute, University of Colorado)

Abstract

An attractive solution to the problem of the origin of protein synthesis in an evolving ‘RNA world’ involves catalysis by nucleic acid without assistance from proteins1,2. Indeed, even the modern ribosome has been considered to be fundamentally an RNA machine3, and the large ribosomal subunit can carry out peptidyl transfer in the absence of most of its protein subunits4. Successive cycles of in vitro selection and amplification5,6,7 have been used to find RNAs that perform many biochemical reactions8,9,10,11,12,13,14,15,16, including transfer of an RNA-linked amino acid to their own 5′-amino-modified terminus15. Here we demonstrate the in vitro selection of ribozymes (196 nucleotides) that perform the same peptidyl transferase reaction as the ribosome: that is, they can join amino acids by a peptide bond. Like ribosome substrates, one amino acid (N-blocked methionine) is esterified to the 3′(2′)-O of adenosine, whereas the acceptor amino acid (phenylalanine) has a free amino group. Our best characterized ribozyme recognizes the amino-acid ester substrate by binding its adenosine moiety, and is therefore capable of utilizing Leu- and Phe- as well as Met-derived substrates. Such lack of specificity with respect to the amino acid is a feature necessary for a generalized protein-synthesizing enzyme.

Suggested Citation

  • Biliang Zhang & Thomas R. Cech, 1997. "Peptide bond formation by in vitro selected ribozymes," Nature, Nature, vol. 390(6655), pages 96-100, November.
  • Handle: RePEc:nat:nature:v:390:y:1997:i:6655:d:10.1038_36375
    DOI: 10.1038/36375
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    Cited by:

    1. Jonathan C. Chen & Jonathan P. Chen & Max W. Shen & Michael Wornow & Minwoo Bae & Wei-Hsi Yeh & Alvin Hsu & David R. Liu, 2022. "Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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