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

Applying computational tools to impr...

University of Virginia.

Linked to FindBook

Google Book

Amazon

博客來

Applying computational tools to improve stability and reduce aggregate formation of proteins.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applying computational tools to improve stability and reduce aggregate formation of proteins./
Author:	Jordan, Jacob L.
Description:	168 p.
Notes:	Adviser: Erik J. Fernandez.
Contained By:	Dissertation Abstracts International70-04B.
Subject:	Biophysics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3353839
ISBN:	9781109104981

Applying computational tools to improve stability and reduce aggregate formation of proteins.
Jordan, Jacob L.

Applying computational tools to improve stability and reduce aggregate formation of proteins. - 168 p.

Adviser: Erik J. Fernandez.

Thesis (Ph.D.)--University of Virginia, 2009.

The goal of this work was to apply two computational tools to address the aggregation problem in multi-domain proteins. The first tool is based on a set of algorithms developed to predict aggregation- or self association-prone sequences of unfolded peptides. This tool was applied to the analysis of aggregates formed from alpha-chymotrypsinogen to frame a hypothesis for the potential pathway to aggregate formation as well as the aggregation-prone region of the molecule.

ISBN: 9781109104981Subjects--Topical Terms:

1019105
Biophysics, General.

Applying computational tools to improve stability and reduce aggregate formation of proteins.
LDR:03179nam 2200325 a 45 001 857123
005 20100709
008 100709s2009 ||||||||||||||||| ||eng d
020 $a 9781109104981
035 $a (UMI)AAI3353839
035 $a AAI3353839
040 $a UMI $c UMI
100 1 $a Jordan, Jacob L. $3 1024052
245 1 0 $a Applying computational tools to improve stability and reduce aggregate formation of proteins.
300 $a 168 p.
500 $a Adviser: Erik J. Fernandez.
500 $a Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2432.
502 $a Thesis (Ph.D.)--University of Virginia, 2009.
520 $a The goal of this work was to apply two computational tools to address the aggregation problem in multi-domain proteins. The first tool is based on a set of algorithms developed to predict aggregation- or self association-prone sequences of unfolded peptides. This tool was applied to the analysis of aggregates formed from alpha-chymotrypsinogen to frame a hypothesis for the potential pathway to aggregate formation as well as the aggregation-prone region of the molecule.
520 $a The second tool, Rosetta Design, is a growing software suite capable of an assortment of protein-related applications. Rosetta Design was first used to analyze a set of known stabilizing and destabilizing mutations for a biopharmaceutical system and then identify an additional stabilizing mutation previously unidentified by other design tools.
520 $a Finally, these two tools are applied in tandem to the problem of human gammaD-crystallin aggregate formation. Two mutations were chosen for experimental design, M69Q and S130P. The N-td mutation, M69Q, was chosen due to the large increase in domain stability predicted by Rosetta as well as a notable increase in domain-domain interaction. The C-td mutation, S130P, was chosen primarily because of its location in a consensus aggregation "hot spot" and the tendency for proline to reduce beta-sheet formation. Additionally, the mutation was predicted to increase domain-domain interactions at the expense of a portion of the C-td stability. Preliminary stability experiments for this indicate a slight increase in the stability of the M69Q mutation and a noticeable the loss of stability for S130P. The overriding result though is a remarkable reduction in aggregation rate for both mutants at elevated temperatures approaching the Tm, essentially forming little or no aggregate in a two hour time period with the S130P variant.
520 $a These results highlight the strength of computational tools in protein design. By narrowing in silico the sequence space to be tested experimentally from a vast array of candidate mutations, computational design equips researchers a qualitative evaluation of potential mutants and it enables detailed studies that exhaust potential routes to protein stability.
590 $a School code: 0246.
650 4 $a Biophysics, General. $3 1019105
650 4 $a Chemistry, Biochemistry. $3 1017722
650 4 $a Engineering, Chemical. $3 1018531
690 $a 0487
690 $a 0542
690 $a 0786
710 2 $a University of Virginia. $3 645578
773 0 $t Dissertation Abstracts International $g 70-04B.
790 $a 0246
790 1 0 $a Fernandez, Erik J., $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3353839