東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Optimization algorithms for site-dir...

Zheng, Wei.

Linked to FindBook

Google Book

Amazon

博客來

Optimization algorithms for site-directed protein recombination experiment planning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimization algorithms for site-directed protein recombination experiment planning./
Author:	Zheng, Wei.
Description:	150 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3780.
Contained By:	Dissertation Abstracts International71-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404500
ISBN:	9781124015583

Optimization algorithms for site-directed protein recombination experiment planning.
Zheng, Wei.

Optimization algorithms for site-directed protein recombination experiment planning. - 150 p.

Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3780.

Thesis (Ph.D.)--Dartmouth College, 2010.

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.

ISBN: 9781124015583Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Optimization algorithms for site-directed protein recombination experiment planning.
LDR:03335nam 2200313 4500 001 1399838
005 20110930095844.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124015583
035 $a (UMI)AAI3404500
035 $a AAI3404500
040 $a UMI $c UMI
100 1 $a Zheng, Wei. $3 1260131
245 1 0 $a Optimization algorithms for site-directed protein recombination experiment planning.
300 $a 150 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3780.
500 $a Adviser: Chris Bailey-Kellogg.
502 $a Thesis (Ph.D.)--Dartmouth College, 2010.
520 $a 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.
590 $a School code: 0059.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a Dartmouth College. $b Computer Science. $3 1019183
773 0 $t Dissertation Abstracts International $g 71-06B.
790 1 0 $a Bailey-Kellogg, Chris, $e advisor
790 1 0 $a Balkcom, Devin $e committee member
790 1 0 $a Fleischer, Lisa K. $e committee member
790 1 0 $a Griswold, Karl E. $e committee member
790 $a 0059
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404500