Type of Document Dissertation Author DENG, HAI Author's Email Address hdeng1@student.gsu.edu URN etd-08032007-170558 Title IDENTIFYING CALCIUM-BINDING SITES AND PREDICTING DISULFIDE CONNECTIVITY Degree Ph.D. Department Computer Science Advisory Committee
Advisor Name Title Guantao Chen Committee Chair Yi Pan Committee Co-Chair Jenny J. Yang Committee Member Rajshekhar Sunderraman Committee Member Keywords
- Disulfide connectivity
- Calcium-binding sites
- Nearest neighboring methods
- Geometry structure
- Graph algorithm
Date of Defense 2007-02-27 Availability unrestricted Abstract Most questions in proteomics require complex answers. Yet graphtheory, supervised learning, and statistical model have decomposed
complex questions into simple questions with simple answers. The
expertise in the field of protein study often address tasks that
demand answers as complex as the questions. Such complex answers may
consist of multiple factors that must be weighed against each other
to arrive at a globally satisfactory and consistent solution to the
question.
In the prediction of calcium binding in proteins, we construct a
global oxygen contact graph of a protein, then apply a graph
algorithm to find oxygen clusters with the fixed size of four,
finally employ a geometry algorithm to judge if the oxygen clusters
are calcium-binding sites or not. Additionally, we can predict the
locations of those sites.
Furthermore, we construct a global oxygen contact graph including
oxygen-bonded carbon atoms of a protein, then apply a graph
algorithm to find local biggest oxygen clusters, finally design
another geometric filter to exclude the non-calcium binding oxygen
clusters. In addition, we apply observed chemical properties as a
chemical filter to recognize some non-calcium binding oxygen
clusters.
In order to explore the characteristics of calcium-binding sites in
proteins, we conduct a statistic survey on four datasets derived
from 1994 to 2005 about the geometric parameters and chemical
properties of calcium-binding sites.
In the prediction of disulfide bond connectivity, we analyze protein
sequences to predict the folding of proteins relative to the
cystines using nearest neighboring methods. we extend a new
pattern-wise method to all available template proteins, and find
global pattern of pairing cysteines with a new descriptor of
cysteine separation profile on protein secondary structure.
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