Literature DB >> 20377457

Protein fragment swapping: a method for asymmetric, selective site-directed recombination.

Wei Zheng1, Karl E Griswold, Chris Bailey-Kellogg.   

Abstract

This article presents a new approach to site-directed recombination, swapping combinations of selected discontiguous fragments from a source protein in place of corresponding fragments of a target protein. By being both asymmetric (differentiating source and target) and selective (swapping discontiguous fragments), our method focuses experimental effort on a more restricted portion of sequence space, constructing hybrids that are more likely to have the properties that are the objective of the experiment. Furthermore, since the source and target need to be structurally homologous only locally (rather than overall), our method supports swapping fragments from functionally important regions of a source into a target "scaffold" (for example, to humanize an exogenous therapeutic protein). A protein fragment swapping plan is defined by the residue position boundaries of the fragments to be swapped; it is assessed by an average potential score over the resulting hybrid library, with singleton and pairwise terms evaluating the importance and fit of the swapped residues. While we prove that it is NP-hard to choose an optimal set of fragments under such a potential score, we develop an integer programming approach, which we call Swagmer, that works very well in practice. We demonstrate the effectiveness of our method in three swapping problems: selective recombination between beta-lactamases, activity swapping between glutathione transferases, and activity swapping between carboxylases and mutases in the purE family. We show that the selective recombination approach generates better plan (in terms of resulting potential score) than traditional site-directed recombination approaches. We also show that in all cases the optimized experiments are significantly better than ones that would result from stochastic methods.

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Year:  2010        PMID: 20377457     DOI: 10.1089/cmb.2009.0189

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  2 in total

1.  Optimization of combinatorial mutagenesis.

Authors:  Andrew S Parker; Karl E Griswold; Chris Bailey-Kellogg
Journal:  J Comput Biol       Date:  2011-09-16       Impact factor: 1.479

2.  Pareto Optimization of Combinatorial Mutagenesis Libraries.

Authors:  Deeptak Verma; Gevorg Grigoryan; Chris Bailey-Kellogg
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2018-07-23       Impact factor: 3.710

  2 in total

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