語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Adaptation and use of four-body stat...
~
Reck, Gregory M.
FindBook
Google Book
Amazon
博客來
Adaptation and use of four-body statistical potential to examine thermodynamic properties of proteins.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Adaptation and use of four-body statistical potential to examine thermodynamic properties of proteins./
作者:
Reck, Gregory M.
面頁冊數:
190 p.
附註:
Adviser: Iosif I. Vaisman.
Contained By:
Dissertation Abstracts International67-11B.
標題:
Biology, Biostatistics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3240838
ISBN:
9780542955471
Adaptation and use of four-body statistical potential to examine thermodynamic properties of proteins.
Reck, Gregory M.
Adaptation and use of four-body statistical potential to examine thermodynamic properties of proteins.
- 190 p.
Adviser: Iosif I. Vaisman.
Thesis (Ph.D.)--George Mason University, 2007.
While most proteins in biological systems are inherently stable as a prerequisite to performing their functions, a small number of normally well-behaved proteins can engage in a process of aggregation that eventually leads to the formation of an insoluble material identified as an amyloid. Details of the aggregation process are not fully known, but for some model proteins the process can be initiated with known destabilizing conditions. While no sequence or structural similarities have been observed among the proteins, structural instability associated with a characteristic motif in the protein could be a common thread. The proposed strategy to search for such a feature employs a knowledge-based tool that examines the sequence-structure relationship in a specific target protein based on similar relationships drawn from a large representative sample of proteins. The tool incorporates a computational structural analysis known as tessellation to identify small geometric elements each containing four neighboring amino acid residues, and builds a potential score for the protein based on a statistical analysis of the appearance of these quadruplets in the reference set. Components of the protein potential score can be associated with the residues in the primary sequence leading to a potential profile or vector that characterizes the local compatibility of the protein structure with its sequence. The aim of this effort was to demonstrate a relationship between tessellation-derived potentials and thermodynamic measurements of protein stability. A major part of the study was to improve the representation of the protein environment by computationally hydrating the proteins used in the analysis. Several strategies were investigated for including the surrounding hydration water in the statistical analysis of the reference proteins. The resulting model has been used to successfully correlate the stability of several model proteins and to discriminate native proteins from large groups of decoy structures. Machine learning tools were also employed to search for information content in the potential profile vectors and to seek an association between the potential profiles of mutants of transthyretin and their amyloidogenic behavior.
ISBN: 9780542955471Subjects--Topical Terms:
1018416
Biology, Biostatistics.
Adaptation and use of four-body statistical potential to examine thermodynamic properties of proteins.
LDR
:03165nam 2200277 a 45
001
974364
005
20110929
008
110929s2007 eng d
020
$a
9780542955471
035
$a
(UMI)AAI3240838
035
$a
AAI3240838
040
$a
UMI
$c
UMI
100
1
$a
Reck, Gregory M.
$3
1298301
245
1 0
$a
Adaptation and use of four-body statistical potential to examine thermodynamic properties of proteins.
300
$a
190 p.
500
$a
Adviser: Iosif I. Vaisman.
500
$a
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6209.
502
$a
Thesis (Ph.D.)--George Mason University, 2007.
520
$a
While most proteins in biological systems are inherently stable as a prerequisite to performing their functions, a small number of normally well-behaved proteins can engage in a process of aggregation that eventually leads to the formation of an insoluble material identified as an amyloid. Details of the aggregation process are not fully known, but for some model proteins the process can be initiated with known destabilizing conditions. While no sequence or structural similarities have been observed among the proteins, structural instability associated with a characteristic motif in the protein could be a common thread. The proposed strategy to search for such a feature employs a knowledge-based tool that examines the sequence-structure relationship in a specific target protein based on similar relationships drawn from a large representative sample of proteins. The tool incorporates a computational structural analysis known as tessellation to identify small geometric elements each containing four neighboring amino acid residues, and builds a potential score for the protein based on a statistical analysis of the appearance of these quadruplets in the reference set. Components of the protein potential score can be associated with the residues in the primary sequence leading to a potential profile or vector that characterizes the local compatibility of the protein structure with its sequence. The aim of this effort was to demonstrate a relationship between tessellation-derived potentials and thermodynamic measurements of protein stability. A major part of the study was to improve the representation of the protein environment by computationally hydrating the proteins used in the analysis. Several strategies were investigated for including the surrounding hydration water in the statistical analysis of the reference proteins. The resulting model has been used to successfully correlate the stability of several model proteins and to discriminate native proteins from large groups of decoy structures. Machine learning tools were also employed to search for information content in the potential profile vectors and to seek an association between the potential profiles of mutants of transthyretin and their amyloidogenic behavior.
590
$a
School code: 0883.
650
4
$a
Biology, Biostatistics.
$3
1018416
650
4
$a
Biology, Molecular.
$3
1017719
690
$a
0307
690
$a
0308
710
2 0
$a
George Mason University.
$3
1019450
773
0
$t
Dissertation Abstracts International
$g
67-11B.
790
$a
0883
790
1 0
$a
Vaisman, Iosif I.,
$e
advisor
791
$a
Ph.D.
792
$a
2007
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3240838
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9132594
電子資源
11.線上閱覽_V
電子書
EB W9132594
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入