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Site-directed protein recombination produces improved and novel protein variants by recombining
sequence fragments from parent proteins. The resulting hybrids accumulate
multiple mutations that have been evolutionarily accepted together. Subsequent screening
or selection identifies hybrids with desirable characteristics. In order to increase the "hit
rate" of good variants, this thesis develops experiment planning algorithms to optimize
protein recombination experiments. First, to improve the frequency of generating novel
hybrids, a metric is developed to assess the diversity among hybrids and parent proteins.
Dynamic programming algorithms are then created to optimize the selection of breakpoint
locations according to this metric. Second, the trade-off between diversity and stability in
recombination experiment planning is studied, recognizing that diversity requires changes
from parent proteins, which may also disrupt important residue interactions necessary for
protein stability. Accordingly, methods based on dynamic programming are developed
to provide combined optimization of diversity and stability, finding optimal breakpoints
such that no other experiment plan has better performance in both aspects simultaneously.
Third, in order to support protein recombination with heterogeneous structures and focus
on functionally important regions, a general framework for protein fragment swapping is
developed. Differentiating source and target parents, and swappable regions within them,
fragment swapping enables asymmetric, selective site-directed recombination. Two applications
of protein fragment swapping are studied. In order to generate hybrids inheriting
functionalities from both source and target proteins by fragment swapping, a method based
on integer programming selects optimal swapping fragments to maximize the predicted stability
and activity of hybrids in the resulting library. In another application, human source
protein fragments are swapped into therapeutic exogenous target protein to minimize the
occurrence of peptides that trigger immune response. A dynamic programming method is
developed to optimize fragment selection for both humanity and functionality, resulting in
therapeutically active variants with decreased immunogenicity.
Ph.D Dissertation of Wei Zheng in Computer Science at Dartmouth College.
Advisor: Chris Bailey-Kellogg
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Wei Zheng, "Optimization Algorithms for Site-directed Protein Recombination Experiment Planning." Dartmouth Computer Science Technical Report TR2010-672, June 2010.
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