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Algorithms exploiting the chain stru...

Lotan, Itay.

Algorithms exploiting the chain structure of proteins.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algorithms exploiting the chain structure of proteins.
Author:	Lotan, Itay.
Description:	136 p.
Notes:	Adviser: Jean-Claude Latombe.
Notes:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4672.
Contained By:	Dissertation Abstracts International65-09B.
Subject:	Computer Science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145611
ISBN:	0496045806

Algorithms exploiting the chain structure of proteins.
Lotan, Itay.

Algorithms exploiting the chain structure of proteins. - 136 p.

Adviser: Jean-Claude Latombe.

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

Proteins are large molecules that affect almost all properties of living organisms. Since their functions are largely dependent on their spatial structure, studying their 3-D shapes is very important. Various computational methods have been developed to study proteins, which are widely used in research today, creating a need for efficient algorithms to handle the complexity of proteins. This thesis presents three algorithms addressing fundamental problems in computational structural biology. The common thread of these algorithms is their use of concepts and techniques from robotics and computational geometry to exploit the long chain kinematics of proteins. The first algorithm is an automatic method for completing partial models of protein structures resolved using X-ray crystallography. A protein fragment of known sequence is optimally fitted into an electron density map between two anchor points. The generation of the missing fragment is approached as an inverse kinematics problem. First, a large set of candidates is sampled that meet the closure constraint and then the best candidates are refined. The second algorithm speeds up the computation of structural similarity at the expense of introducing a small error in the similarity measure. It exploits the fact that the representation of a protein conformation contains redundant information, due to the chain topology and limited compactness of proteins. This redundancy is reduced by approximating sub-chains by their centers of mass. This method can be used in applications where small errors can be tolerated or as a fast filter when exact measures are required. It has been successfully tested for comparing large collections of conformations of the same protein, as well as for comparing native structures of different proteins. The third algorithm is a method for speeding up Monte Carlo simulation of proteins. It exploits the chain kinematics of the protein backbone and the fact that only a few degrees of freedom are changed at each simulation step to quickly detect pairs of interacting atoms. It uses a novel data structure that captures the kinematics and shape of a protein at successive resolutions. It also makes it possible to identify partial energy sums left unchanged at each simulation step.

ISBN: 0496045806Subjects--Topical Terms:

212513
Computer Science.

Algorithms exploiting the chain structure of proteins.
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502 $a Thesis (Ph.D.)--Stanford University, 2004.
520 # $a Proteins are large molecules that affect almost all properties of living organisms. Since their functions are largely dependent on their spatial structure, studying their 3-D shapes is very important. Various computational methods have been developed to study proteins, which are widely used in research today, creating a need for efficient algorithms to handle the complexity of proteins. This thesis presents three algorithms addressing fundamental problems in computational structural biology. The common thread of these algorithms is their use of concepts and techniques from robotics and computational geometry to exploit the long chain kinematics of proteins. The first algorithm is an automatic method for completing partial models of protein structures resolved using X-ray crystallography. A protein fragment of known sequence is optimally fitted into an electron density map between two anchor points. The generation of the missing fragment is approached as an inverse kinematics problem. First, a large set of candidates is sampled that meet the closure constraint and then the best candidates are refined. The second algorithm speeds up the computation of structural similarity at the expense of introducing a small error in the similarity measure. It exploits the fact that the representation of a protein conformation contains redundant information, due to the chain topology and limited compactness of proteins. This redundancy is reduced by approximating sub-chains by their centers of mass. This method can be used in applications where small errors can be tolerated or as a fast filter when exact measures are required. It has been successfully tested for comparing large collections of conformations of the same protein, as well as for comparing native structures of different proteins. The third algorithm is a method for speeding up Monte Carlo simulation of proteins. It exploits the chain kinematics of the protein backbone and the fact that only a few degrees of freedom are changed at each simulation step to quickly detect pairs of interacting atoms. It uses a novel data structure that captures the kinematics and shape of a protein at successive resolutions. It also makes it possible to identify partial energy sums left unchanged at each simulation step.
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