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Access Type
Open Access
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
2010
Month Degree Awarded
September
Keywords
Data Fusion, Side Chain Assignment, X-ray Crystallography, Error Distribution, Electron density Map, Real-space Refinement
Abstract
In this thesis, we study the problem of protein side chain assignment (SCA) given
multiple sources of experimental and modeling data. In particular, the mechanism
of X-ray crystallography (X-ray) is re-examined using Fourier analysis, and a novel
probabilistic model of X-ray is proposed for SCA's decision making. The relationship
between the measurements in X-ray and the desired structure is reformulated in terms
of Discrete Fourier Transform (DFT). The decision making is performed by developing
a new resolution-dependent electron density map (EDM) model and applying
Maximum Likelihood (ML) estimation, which simply reduces to the Least Squares
(LS) solution. Calculation of the condence probability associated with this decision
making is also given. One possible extension of this novel model is the real-space
refinement when the continuous conformational space is used.
Furthermore, we present a data fusion scheme combining multi-sources of data
to solve SCA problem. The merit of our framework is the capability of exploiting
multi-sources of information to make decisions in a probabilistic perspective based on
Bayesian inference. Although our approach aims at SCA problem, it can be easily
transplanted to solving for the entire protein structure.
DOI
https://doi.org/10.7275/1443519
First Advisor
Ramgopal R. Mettu