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Large-scale computational protein de...

Larson, Stefan Mathias.

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Large-scale computational protein design.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Large-scale computational protein design./
Author:	Larson, Stefan Mathias.
Description:	120 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1732.
Contained By:	Dissertation Abstracts International65-04B.
Subject:	Biophysics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3128420
ISBN:	0496756826

Large-scale computational protein design.
Larson, Stefan Mathias.

Large-scale computational protein design. - 120 p.

Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1732.

Thesis (Ph.D.)--Stanford University, 2004.

As in many areas of computational biology, the applicability and utility of computational protein design is often limited by the availability of sufficient computing resources. Protein design has traditionally been restricted in terms of the complexity of molecular modeling and potential energy calculations used, the breadth of sequence and conformational search procedures applied, the number of unique sequences that can be generated for each target, and the total number of target proteins that can be studied. This thesis shows that increasing the amount of computational power applied to protein design, by at least an order of magnitude above that of previous work, can ameliorate many of the restrictions under which protein design research is carried out. This leads to a broader range of applications for computational protein design, and provides an opportunity to develop better protein design algorithms.

ISBN: 0496756826Subjects--Topical Terms:

1019105
Biophysics, General.

Large-scale computational protein design.
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100 1 $a Larson, Stefan Mathias. $3 1932578
245 1 0 $a Large-scale computational protein design.
300 $a 120 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1732.
500 $a Adviser: Vijay Pande.
502 $a Thesis (Ph.D.)--Stanford University, 2004.
520 $a As in many areas of computational biology, the applicability and utility of computational protein design is often limited by the availability of sufficient computing resources. Protein design has traditionally been restricted in terms of the complexity of molecular modeling and potential energy calculations used, the breadth of sequence and conformational search procedures applied, the number of unique sequences that can be generated for each target, and the total number of target proteins that can be studied. This thesis shows that increasing the amount of computational power applied to protein design, by at least an order of magnitude above that of previous work, can ameliorate many of the restrictions under which protein design research is carried out. This leads to a broader range of applications for computational protein design, and provides an opportunity to develop better protein design algorithms.
520 $a A public distributed computing project for large-scale protein design (Genome@home) was developed in order to study several specific problems in protein science. This thesis describes three major studies that were carried out by using large-scale protein design to thoroughly explore the sequence space of several hundred different proteins. First, a study of protein designability suggests that the diversity of designed sequences is primarily determined by a structure's overall fold and that the designability principle postulated from simple models holds in real proteins. A new technique in protein structure prediction is developed, showing that large diverse alignments of computationally designed sequences confer many of the same benefits as natural sequences in identifying structural templates for comparative modeling targets. Finally, a detailed comparison of designed and natural SH3 sequences shows that optimization for the native state structure has dominated the evolution of natural SH3 domains and that sequence optimization for native state stability and conservation of transition state structure are significantly correlated.
590 $a School code: 0212.
650 4 $a Biophysics, General. $3 1019105
650 4 $a Chemistry, Biochemistry. $3 1017722
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710 2 0 $a Stanford University. $3 754827
773 0 $t Dissertation Abstracts International $g 65-04B.
790 1 0 $a Pande, Vijay, $e advisor
790 $a 0212
791 $a Ph.D.
792 $a 2004
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