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

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Call No.	Ph.D. 2010 c.2
Author	Xiong, Fei.
Title	Graph algorithms for NMR resonance assignment and cross-link experiment planning.
Date	2010
Abstract	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.
Description	xvi, 102 leaves : col. ill. ; 28 cm.
Format	application/pdf
Advisor	Bailey-Kellogg, Chris, Balkcom, Devin, Fleischer, Lisa, Pandurangan, Gopal
Subject	Graph algorithms. Nuclear magnetic resonance spectroscopy -- Data processing. Proteins -- Structure -- Mathematical models. Proteins -- Crosslinking -- Mathematical models. Protein folding -- Mathematical models.
College	Dartmouth College. Dept. of Computer Science.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Copyright	All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means without permission in writing from the author.
DOI	http://dx.doi.org/10.1349/ddlp.366

Title	Page 1
Format	application/pdf
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Full Text	Graph algorithms for NMR resonance assignment and cross-link experiment planning A Thesis Submitted to the Faculty in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science by Fei Xiong DARTMOUTH COLLEGE Hanover, New Hampshire June 2010 Examining Committee: (chair) Chris Bailey-Kellogg Devin Balkcom Lisa Fleischer Gopal Pandurangan Brian W. Pogue, Ph.D. Dean of Graduate Studies

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