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A prevalent peptide-binding domain guides ribosomal natural product biosynthesis.


ABSTRACT: Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a rapidly growing class of natural products. RiPP precursor peptides can undergo extensive enzymatic tailoring to yield structurally and functionally diverse products, and their biosynthetic logic makes them attractive bioengineering targets. Recent work suggests that unrelated RiPP-modifying enzymes contain structurally similar precursor peptide-binding domains. Using profile hidden Markov model comparisons, we discovered related and previously unrecognized peptide-binding domains in proteins spanning the majority of known prokaryotic RiPP classes, and we named this conserved domain the RiPP precursor peptide recognition element (RRE). Through binding studies we verified RRE's roles for three distinct RiPP classes: linear azole-containing peptides, thiopeptides and lasso peptides. Because numerous RiPP biosynthetic enzymes act on peptide substrates, our findings have powerful predictive value as to which protein(s) drive substrate binding, thereby laying a foundation for further characterization of RiPP biosynthetic pathways and the rational engineering of new peptide-binding activities.

SUBMITTER: Burkhart BJ 

PROVIDER: S-EPMC4509860 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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A prevalent peptide-binding domain guides ribosomal natural product biosynthesis.

Burkhart Brandon J BJ   Hudson Graham A GA   Dunbar Kyle L KL   Mitchell Douglas A DA  

Nature chemical biology 20150713 8


Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a rapidly growing class of natural products. RiPP precursor peptides can undergo extensive enzymatic tailoring to yield structurally and functionally diverse products, and their biosynthetic logic makes them attractive bioengineering targets. Recent work suggests that unrelated RiPP-modifying enzymes contain structurally similar precursor peptide-binding domains. Using profile hidden Markov model comparisons, we disco  ...[more]

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