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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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 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.

Original languageEnglish
Pages (from-to)1897-1907
Number of pages11
JournalProtein Science
Volume11
Issue number8
DOIs
StatePublished - 8 May 2002

Keywords

  • Evolutionary algorithm
  • Gaussian mutation
  • Protein-structure prediction
  • Rotamer library
  • Side-chain conformation

Fingerprint Dive into the research topics of 'GEM: A Gaussian evolutionary method for predicting protein side-chain conformations'. Together they form a unique fingerprint.

Cite this