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Document Type

Open Access

Degree Program

Electrical & Computer Engineering

Degree Type

Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)

Year Degree Awarded


Month Degree Awarded



Data Fusion, Side Chain Assignment, X-ray Crystallography, Error Distribution, Electron density Map, Real-space Refinement


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.

First Advisor

Ramgopal R. Mettu