GEM: A Gaussian evolutionary method for predicting protein side-chain conformations

Jinn-Moon Yang*, Chi Hung Tsai, Ming Jing Hwang, Huai Kuang Tsai, Jenn Kang Hwang, Cheng Yan Kao

*此作品的通信作者

研究成果: Article同行評審

21 引文 斯高帕斯(Scopus)

摘要

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 X 1 , 66% for X 1 + 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.

原文English
頁(從 - 到)1897-1907
頁數11
期刊Protein Science
11
發行號8
DOIs
出版狀態Published - 8 五月 2002

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