NDLI: Algorithms for optimizing cross-overs in DNA shuffling

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Algorithms for optimizing cross-overs in DNA shuffling

Content Provider	ACM Digital Library
Author	He, Lu Friedman, Alan M. Bailey-Kellogg, Chris
Abstract	DNA shuffling generates combinatorial libraries of chimeric genes by stochastically recombining parent genes. The resulting libraries are subjected to large-scale genetic selection or screening to identify those chimeras with favorable properties (e.g., enhanced stability or enzymatic activity). While DNA shuffling has been applied quite successfully, it is limited by its homology-dependent, stochastic nature. Consequently, it is used only with parents of sufficient overall sequence identity, and provides no control over the resulting chimeric library. This paper presents efficient methods to extend the scope of DNA shuffling to handle significantly more diverse parents and to generate more predictable, optimized libraries. Our Codns (cross-over optimization for DNA shuffling) approach employs polynomial-time dynamic programming algorithms to select codons for the parental amino acids, allowing for zero or a fixed number of conservative substitutions. We first present efficient algorithms to optimize the local sequence identity or the nearest-neighbor approximation of the change in free energy upon annealing, objectives that were previously optimized by computationally-expensive integer programming methods. We then present efficient algorithms for more powerful objectives that seek to localize and enhance the frequency of recombination by producing "runs" of common nucleotides either overall or according to the sequence diversity of the resulting chimeras. We demonstrate the effectiveness of Codns in choosing codons and allocating substitutions to promote recombination between parents targeted in earlier studies: two GAR transformylases (41% amino acid sequence identity), two very distantly related DNA polymerases, Pol X and β (15%), and beta-lactamases of varying identity (26--47%).
Starting Page	143
Ending Page	152
Page Count	10
File Format	PDF
ISBN	9781450307963
DOI	10.1145/2147805.2147821
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Protein engineering in vitro evolution Combinatorial optimization Experiment planning
Content Type	Text
Resource Type	Article

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