HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Yang, J.-M. Articles by Kao, C.-Y. Search for Related Content PubMed PubMed Citation Articles by Yang, J.-M. Articles by Kao, C.-Y. Social Bookmarking                   What's this?

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

¹ Department of Biological Science and Technology and Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 30050, Taiwan
² Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan
³ Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan

Reprint requests to: Jinn-Moon Yang, Department of Biological Science and Technology and Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 30050, Taiwan; e-mail: moon{at}cc.nctu.edu.tw; fax: 886-3-5729288 or Ming-Jing Hwang, Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan; e-mail: mjhwang{at}ibms.sinica.edu.tw; fax: 886-2-27887641.

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 ₁, 66% for _{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.

Keywords: Evolutionary algorithm; Gaussian mutation; protein-structure prediction; side-chain conformation; rotamer library

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				V. Z. Spassov, L. Yan, and P. K. Flook The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: A side-chain prediction algorithm based on side-chain backbone interactions Protein Sci., March 1, 2007; 16(3): 494 - 506. [Abstract] [Full Text] [PDF]

				P. Larranaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armananzas, G. Santafe, A. Perez, et al. Machine learning in bioinformatics Brief Bioinform, March 1, 2006; 7(1): 86 - 112. [Abstract] [Full Text] [PDF]

				C. Wang, O. Schueler-Furman, and D. Baker Improved side-chain modeling for protein-protein docking Protein Sci., May 1, 2005; 14(5): 1328 - 1339. [Abstract] [Full Text] [PDF]