Protein Fragment Swapping: A Method for Asymmetric, Selective Site-Directed Recombination
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- Zheng W., Griswold K.E., Bailey-Kellogg C. (2009) Protein Fragment Swapping: A Method for Asymmetric, Selective Site-Directed Recombination. In: Batzoglou S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science, vol 5541. Springer, Berlin, Heidelberg
This paper 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”; e.g., 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 two types of swapping problem: selective recombination between beta-lactamases and activity swapping between glutathione transferases. We show that the selective recombination approach generates a better plan (in terms of resulting potential score) than a traditional site-directed recombination approach. We also show that in both cases the optimized experiment is significantly better than one that would result from stochastic methods.
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