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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51295

Title: GEM: a Gaussian Evolutionary Method for predicting protein side-chain conformations
Authors: Jinn-Moon Yang
Chi-Hung Tsai
Ming-Jing Hwang
Huai-Kuang Tsai
Jenn-Kang Hwang
Cheng-Yan Kao
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Evolutionary algorithm
Gaussian mutation
protein-structure prediction
side-chain conformation
rotamer library
Date: 2002
Issue Date: 2018-11-21T08:31:35Z
Publisher: Protein Sci
Abstract: Abstract: We have developed an evolutionary approach to predicting protein side-chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing-based Gaussian mutations and self-adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for χ1, 66% for χ1 + 2, and 1.36 Å for the root mean square deviation of side-chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side-chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.
Relation: 11(8) pp.1897-1907
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51295
Appears in Collections:[資訊工程學系] 期刊論文

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