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Protein Structure Refinement Algorithms.

Chitsaz, Mohsen.

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Protein Structure Refinement Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Protein Structure Refinement Algorithms./
Author:	Chitsaz, Mohsen.
Description:	143 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
Subject:	Biophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3624773
ISBN:	9781303988028

Protein Structure Refinement Algorithms.
Chitsaz, Mohsen.

Protein Structure Refinement Algorithms. - 143 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--California Institute of Technology, 2014.

This item must not be sold to any third party vendors.

Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics. Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMC GRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.

ISBN: 9781303988028Subjects--Topical Terms:

518360
Biophysics.

Protein Structure Refinement Algorithms.
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245 1 0 $a Protein Structure Refinement Algorithms.
300 $a 143 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Adviser: Stephen L. Mayo.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics. Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMC GRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.
590 $a School code: 0037.
650 4 $a Biophysics. $3 518360
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710 2 $a California Institute of Technology. $b Biochemistry and Molecular Biophysics. $3 2101555
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