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Xiong, Fei.

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Graph algorithms for NMR resonance assignment and cross-link experiment planning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Graph algorithms for NMR resonance assignment and cross-link experiment planning./
Author:	Xiong, Fei.
Description:	119 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3778.
Contained By:	Dissertation Abstracts International71-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404499
ISBN:	9781124015576

Graph algorithms for NMR resonance assignment and cross-link experiment planning.
Xiong, Fei.

Graph algorithms for NMR resonance assignment and cross-link experiment planning. - 119 p.

Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3778.

Thesis (Ph.D.)--Dartmouth College, 2010.

The study of three-dimensional protein structures produces insights into protein function at the molecular level. Graphs provide a natural representation of protein structures and associated experimental data, and enable the development of graph algorithms to analyze the structures and data. This thesis develops such graph representations and algorithms for two novel applications: structure-based NMR resonance assignment and disulfide cross-link experiment planning for protein fold determination. The first application seeks to identify correspondences between spectral peaks in NMR data and backbone atoms in a structure (from x-ray crystallography or homology modeling), by computing correspondences between a contact graph representing the structure and an analogous but very noisy and ambiguous graph representing the data. The assignment then supports further NMR studies of protein dynamics and protein-ligand interactions. A hierarchical grow-and-match algorithm was developed for smaller assignment problems, ensuring completeness of assignment, while a random graph approach was developed for larger problems, provably determining unique matches in polynomial time with high probability. Test results show that our algorithms are robust to typical levels of structural variation, noise, and missings, and achieve very good overall assignment accuracy. The second application aims to rapidly determine the overall organization of secondary structure elements of a target protein by probing it with a set of planned disulfide cross-links. A set of informative pairs of secondary structure elements is selected from graphs representing topologies of predicted structure models. For each pair in this "fingerprint", a set of informative disulfide probes is selected from graphs representing residue proximity in the models. Information-theoretic planning algorithms were developed to maximize information gain while minimizing experimental complexity, and Bayes error plan assessment frameworks were developed to characterize the probability of making correct decisions given experimental data. Evaluation of the approach on a number of structure prediction case studies shows that the optimized plans have low risk of error while testing only a very small portion of the quadratic number of possible cross-link candidates.

ISBN: 9781124015576Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Graph algorithms for NMR resonance assignment and cross-link experiment planning.
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020 $a 9781124015576
035 $a (UMI)AAI3404499
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100 1 $a Xiong, Fei. $3 1678850
245 1 0 $a Graph algorithms for NMR resonance assignment and cross-link experiment planning.
300 $a 119 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3778.
500 $a Adviser: Chris Bailey-Kellogg.
502 $a Thesis (Ph.D.)--Dartmouth College, 2010.
520 $a The study of three-dimensional protein structures produces insights into protein function at the molecular level. Graphs provide a natural representation of protein structures and associated experimental data, and enable the development of graph algorithms to analyze the structures and data. This thesis develops such graph representations and algorithms for two novel applications: structure-based NMR resonance assignment and disulfide cross-link experiment planning for protein fold determination. The first application seeks to identify correspondences between spectral peaks in NMR data and backbone atoms in a structure (from x-ray crystallography or homology modeling), by computing correspondences between a contact graph representing the structure and an analogous but very noisy and ambiguous graph representing the data. The assignment then supports further NMR studies of protein dynamics and protein-ligand interactions. A hierarchical grow-and-match algorithm was developed for smaller assignment problems, ensuring completeness of assignment, while a random graph approach was developed for larger problems, provably determining unique matches in polynomial time with high probability. Test results show that our algorithms are robust to typical levels of structural variation, noise, and missings, and achieve very good overall assignment accuracy. The second application aims to rapidly determine the overall organization of secondary structure elements of a target protein by probing it with a set of planned disulfide cross-links. A set of informative pairs of secondary structure elements is selected from graphs representing topologies of predicted structure models. For each pair in this "fingerprint", a set of informative disulfide probes is selected from graphs representing residue proximity in the models. Information-theoretic planning algorithms were developed to maximize information gain while minimizing experimental complexity, and Bayes error plan assessment frameworks were developed to characterize the probability of making correct decisions given experimental data. Evaluation of the approach on a number of structure prediction case studies shows that the optimized plans have low risk of error while testing only a very small portion of the quadratic number of possible cross-link candidates.
590 $a School code: 0059.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a Dartmouth College. $b Computer Science. $3 1019183
773 0 $t Dissertation Abstracts International $g 71-06B.
790 1 0 $a Bailey-Kellogg, Chris, $e advisor
790 1 0 $a Balkcom, Devin $e committee member
790 1 0 $a Fleischer, Lisa $e committee member
790 1 0 $a Pandurangan, Gopal $e committee member
790 $a 0059
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404499