A Study on Expediting Analysis of Protein Substructures
Date Issued
2004
Date
2004
Author(s)
Chang, Darby Tien-Hau
DOI
zh-TW
Abstract
One of the fundamental issues in drug design is analysis of protein-ligand interactions. The detailed and accurate analysis of protein-ligand interactions involves calculation of binding free energy based on thermodynamics and even quantum mechanics. However, this approach is highly expensive in terms of computing time. As a result, conformational and structural analysis of proteins and ligands has been widely employed as a screening process in computer-aided drug design. One interesting observation in this regard is that for many applications only the substructures on the contour of a protein are of significance. Therefore, in order to expedite the analysis process, it is desirable to incorporate a mechanism that can effectively extract the residues in the proximities of the caves of protein tertiary structures. In this thesis, an efficient filtering process with $O(nlogn)$ time complexity is proposed, where $n$ is the number of residues in the protein. In comparison with the $alpha$-hull algorithm, which is a widely used algorithm in computer graphics for identifying those instances on the contour of a 3-dimensional object, the filtering process employed in this paper features a lower time complexity, $O(nlogn)$ versus $O(n^2)$. The low time complexity of the proposed filtering process is due to a novel kernel density estimation algorithm. Experimental results revealed that the proposed filtering mechanism is able to speed up the analysis process by a factor ranging from 24.91 to 83.53 times without trading off the accuracy of analysis. The software package developed with the mechanism proposed in this thesis has been applied to search for proteins containing a similar binding site to a well-studied crystal structure in PDB(Protein Data Bank). The experimental results provide the biochemists with some valuable clues for in-depth studies.
Subjects
蛋白質結構
核心密度預測
protein structure
kernel density estimation
Type
thesis
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