Monte Carlo sampling of near-native structures of proteins with applications

Jinfeng Zhang, Ming Lin, Rong Chen, Jie Liang, Jun S. Liu

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Since a protein's dynamic fluctuation inside cells affects the protein's biological properties, we present a novel method to study the ensemble of near-native structures (NNS) of proteins, namely, the conformations that are very similar to the experimentally determined native structure. We show that this method enables us to (i) quantify the difficulty of predicting a protein's structure, (ii) choose appropriate simplified representations of protein structures, and (iii) assess the effectiveness of knowledge-based potential functions. We found that well-designed simple representations of protein structures are likely as accurate as those more complex ones for certain potential functions. We also found that the widely used contact potential functions stabilize NNS poorly, whereas potential functions incorporating local structure information significantly increase the stability of NNS.

Original languageEnglish (US)
Pages (from-to)61-68
Number of pages8
JournalProteins: Structure, Function and Genetics
Volume66
Issue number1
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology

Keywords

  • Near-native structures
  • Potential function
  • Protein structure prediction
  • Protein structure simulation
  • Sequential Monte Carlo
  • Structural representation

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