Application of the Statistical Energy Landscape Theory to Protein Structure Prediction
Koretke, Kristin Kay
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https://hdl.handle.net/2142/84426
Description
Title
Application of the Statistical Energy Landscape Theory to Protein Structure Prediction
Author(s)
Koretke, Kristin Kay
Issue Date
1998
Doctoral Committee Chair(s)
Wolynes, Peter G.
Department of Study
Chemistry
Discipline
Chemistry
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Biostatistics
Language
eng
Abstract
There has been an exponential increase in sequence information over the past few years due mostly to the large scale genome projects. The corresponding structural information, however, is being determined at a much slower rate. Consequently, the sequence to structure gap is still growing. Many scientists are looking to structure prediction through computational methods to narrow this gap. Using ideas from the energy landscape theory first introduced by Bryngelson and Wolynes, and database analysis, we have designed statistical mechanical energy functions for protein structure prediction by threading and simulated annealing molecular dynamics methods. These optimized energy functions improve upon the previous approximations of Goldstein, Luthey-Schulten and Wolynes by taking into account correlations in the energy landscape. For the self-consistent energy function used in threading, alignments are comparable to those obtain by evolutionary distance-based alignments and consistently appear to be more accurate for sequences that have lower than 20% sequence identity. The self-consistent threading algorithm we developed was used in our participation in the second annual critical assessment of protein structure prediction (CASP2) competition, and our results place us among the top three groups in comparative modeling. Using this energy function, we predicted the structures of four archaeal adenylate kinases that had less than 17% sequence similarity to any known structure. These predicted structures were then used to study the sources of protein thermostablitiy. The energy function used in the molecular dynamics approach or treatment of protein folding was optimized based on a more complete characterization of a protein's energy landscape. This characterization involved a better statistical sampling of the misfolded states and a partial treatment of dominant short-range correlations and interactions determining collapse. This lead to more accurate structure predictions than was possible by the previous treatment.
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