Literature DB >> 15939434

Why high-error-rate random mutagenesis libraries are enriched in functional and improved proteins.

D Allan Drummond1, Brent L Iverson, George Georgiou, Frances H Arnold.   

Abstract

The fraction of proteins that retain wild-type function after mutation has long been observed to decline exponentially as the average number of mutations per gene increases. Recently, several groups have used error-prone polymerase chain reactions (PCR) to generate libraries with 15 to 30 mutations per gene, on average, and have reported that orders of magnitude more proteins retain function than would be expected from the low-mutation-rate trend. Proteins with improved or novel function were isolated disproportionately from these high-error-rate libraries, leading to claims that high mutation rates unlock regions of sequence space that are enriched in positively coupled mutations. Here, we show experimentally that error-prone PCR produces a broader non-Poisson distribution of mutations consistent with a detailed model of PCR. As error rates increase, this distribution leads directly to the observed excesses in functional clones. We then show that while very low mutation rates result in many functional sequences, only a small number are unique. By contrast, very high mutation rates produce mostly unique sequences, but few retain function. Thus an optimal mutation rate exists that balances uniqueness and retention of function. Overall, high-error-rate mutagenesis libraries are enriched in improved sequences because they contain more unique, functional clones. Our findings demonstrate how optimal error-prone PCR mutation rates may be calculated, and indicate that "optimal" rates depend on both the protein and the mutagenesis protocol.

Mesh:

Year:  2005        PMID: 15939434     DOI: 10.1016/j.jmb.2005.05.023

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  37 in total

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2.  Predicting the tolerance of proteins to random amino acid substitution.

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Journal:  Biophys J       Date:  2005-09-08       Impact factor: 4.033

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4.  The optimal burst of mutation to create a phenotype.

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5.  Lethal mutagenesis of bacteria.

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6.  Structure-based design of combinatorial mutagenesis libraries.

Authors:  Deeptak Verma; Gevorg Grigoryan; Chris Bailey-Kellogg
Journal:  Protein Sci       Date:  2015-03-02       Impact factor: 6.725

7.  Machine learning-assisted directed protein evolution with combinatorial libraries.

Authors:  Zachary Wu; S B Jennifer Kan; Russell D Lewis; Bruce J Wittmann; Frances H Arnold
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-12       Impact factor: 11.205

8.  Engineering antibody fragments to fold in the absence of disulfide bonds.

Authors:  Min Jeong Seo; Ki Jun Jeong; Clinton E Leysath; Andrew D Ellington; Brent L Iverson; George Georgiou
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Review 9.  Exploring protein fitness landscapes by directed evolution.

Authors:  Philip A Romero; Frances H Arnold
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10.  A study in molecular contingency: glutamine phosphoribosylpyrophosphate amidotransferase is a promiscuous and evolvable phosphoribosylanthranilate isomerase.

Authors:  Wayne M Patrick; Ichiro Matsumura
Journal:  J Mol Biol       Date:  2008-01-26       Impact factor: 5.469

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