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Fluctuations and Correlations in Crystalline Protein Dynamics: A Simulation Analysis of Staphylococcal Nuclease

Computational Molecular Biophysics, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany

Correspondence: Address reprint requests to Jeremy C. Smith, Computational Molecular Biophysics, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Im Neuenheimer Feld 368, D-69120 Heidelberg, Germany. Tel.: 49-6221-548857; Fax: 49-6221-548868; E-mail: biocomputing{at}iwr.uni-heidelberg.de.

Understanding collective motions in protein crystals is likely to furnish insight into functional protein dynamics and will improve models for refinement against diffraction data. Here, four 10 ns molecular dynamics simulations of crystalline Staphylococcal nuclease are reported and analyzed in terms of fluctuations and correlations in atomic motion. The simulation-derived fluctuations strongly correlate with, but are slightly higher than, the values derived from the experimental B-factors. Approximately 70% of the atomic fluctuations are due to internal protein motion. For 65% of the protein atoms the internal fluctuations converge on the nanosecond timescale. Convergence is much slower for the elements of the interatomic displacement correlation matrix — of these, >80% converge within 1 ns for interatomic distances 6 Å, but only 10% for separations 12 Å. Those collective motions that converged on the nanosecond timescale involve mostly correlations within the ß-barrel or between -helices of the protein. The R-factor with the experimental x-ray diffuse scattering for the crystal, which is determined by the displacement variance-covariance matrix, decreases to 8% after 10 ns simulation. Both the number of converged correlation matrix elements and the R-factor depend logarithmically on time, consistent with a model in which the number of energy minima sampled depends exponentially on the maximum energy barrier crossed. The logarithmic dependence is also extrapolated to predict a convergence time for the whole variance-covariance matrix of 1 µs.

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