4.5 Article

A Method to Explore Protein Side Chain Conformational Variability Using Experimental Data

期刊

CHEMPHYSCHEM
卷 10, 期 18, 页码 3213-3228

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/cphc.200900400

关键词

averaging; biomolecular simulations; local elevation; molecular dynamics; NMR spectroscopy

资金

  1. National Center of Competence in Research (NCCR) in Structural Biology
  2. Swiss National Science Foundation [200027-109227]

向作者/读者索取更多资源

Experimentally measured values of molecular properties or observables of biomolecules such as proteins are generally averages over time and space, which do not contain sufficient information to determine the underlying conformational distribution of the molecules in solution. The relationship between experimentally measured NMR (3)J-coupling values and the corresponding dihedral angle values is a particularly complicated case due to its nonlinear, multiple-valued nature. Molecular dynamics (MD) simulations at constant temperature can generate Boltzmann ensembles of molecular structures that are free from a priori assumptions about the nature of the underlying conformational distribution. They suffer, however, from limited sampling with respect to time and conformational space. Moreover, the quality of the obtained structures is dependent on the choice of force field and solvation model. A recently proposed method that uses time-averaging with local-elevation (LE) biasing of the conformational search provides an elegant means of overcoming these three problems. Using a set of side chain (3)J-coupling values for the FK506 binding protein (FKBP), we first investigate the uncertainty in the angle values predicted theoretically. We then propose a simple MD-based technique to detect inconsistencies within an experimental data set and identify degrees of freedom for which conformational averaging takes place or for which force field parameters may be deficient. Finally, we show that LE MD is the best method for producing ensembles of structures that, on average, fit the experimental data.

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